Prompt:
Implement the Python class `TestInfluxdbPlugin` described below.

Class description:
Class for smoke testing the InfluxDB-Grafana plugin.

Method signatures and docstrings:
- def install_influxdb_grafana(self): Install InfluxDB-Grafana plugin and check it exists Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m
- def deploy_influxdb_grafana(self): Deploy a cluster with the InfluxDB-Grafana plugin Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 1 node with controller role 5. Add 1 node with compute and cinder roles 6. Add 1 node with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 60m Snapshot deploy_influxdb_grafana
- def deploy_ha_influxdb_grafana(self): Deploy a cluster with the InfluxDB-Grafana plugin in HA mode Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 3 nodes with controller role 5. Add 3 nodes with compute and cinder roles 6. Add 3 nodes with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 120m Snapshot deploy_ha_influxdb_grafana
- def uninstall_influxdb_grafana(self): Uninstall the InfluxDB-Grafana plugin Scenario: 1. Install the plugin. 2. Remove the plugin. Duration 5m
- def uninstall_deployed_influxdb_grafana(self): Uninstall the InfluxDB-Grafana plugin with a deployed environment Scenario: 1. Try to remove the plugin using the Fuel CLI 2. Check plugin can't be uninstalled on deployed cluster. 3. Remove the environment. 4. Remove the plugin. Duration 20m

Implement the Python class `TestInfluxdbPlugin` described below.

Class description:
Class for smoke testing the InfluxDB-Grafana plugin.

Method signatures and docstrings:
- def install_influxdb_grafana(self): Install InfluxDB-Grafana plugin and check it exists Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m
- def deploy_influxdb_grafana(self): Deploy a cluster with the InfluxDB-Grafana plugin Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 1 node with controller role 5. Add 1 node with compute and cinder roles 6. Add 1 node with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 60m Snapshot deploy_influxdb_grafana
- def deploy_ha_influxdb_grafana(self): Deploy a cluster with the InfluxDB-Grafana plugin in HA mode Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 3 nodes with controller role 5. Add 3 nodes with compute and cinder roles 6. Add 3 nodes with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 120m Snapshot deploy_ha_influxdb_grafana
- def uninstall_influxdb_grafana(self): Uninstall the InfluxDB-Grafana plugin Scenario: 1. Install the plugin. 2. Remove the plugin. Duration 5m
- def uninstall_deployed_influxdb_grafana(self): Uninstall the InfluxDB-Grafana plugin with a deployed environment Scenario: 1. Try to remove the plugin using the Fuel CLI 2. Check plugin can't be uninstalled on deployed cluster. 3. Remove the environment. 4. Remove the plugin. Duration 20m

<|skeleton|>
class TestInfluxdbPlugin:
    """Class for smoke testing the InfluxDB-Grafana plugin."""

    def install_influxdb_grafana(self):
        """Install InfluxDB-Grafana plugin and check it exists Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m"""
        <|body_0|>

    def deploy_influxdb_grafana(self):
        """Deploy a cluster with the InfluxDB-Grafana plugin Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 1 node with controller role 5. Add 1 node with compute and cinder roles 6. Add 1 node with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 60m Snapshot deploy_influxdb_grafana"""
        <|body_1|>

    def deploy_ha_influxdb_grafana(self):
        """Deploy a cluster with the InfluxDB-Grafana plugin in HA mode Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 3 nodes with controller role 5. Add 3 nodes with compute and cinder roles 6. Add 3 nodes with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 120m Snapshot deploy_ha_influxdb_grafana"""
        <|body_2|>

    def uninstall_influxdb_grafana(self):
        """Uninstall the InfluxDB-Grafana plugin Scenario: 1. Install the plugin. 2. Remove the plugin. Duration 5m"""
        <|body_3|>

    def uninstall_deployed_influxdb_grafana(self):
        """Uninstall the InfluxDB-Grafana plugin with a deployed environment Scenario: 1. Try to remove the plugin using the Fuel CLI 2. Check plugin can't be uninstalled on deployed cluster. 3. Remove the environment. 4. Remove the plugin. Duration 20m"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.env.revert_snapshot('ready_with_3_slaves')
        self.prepare_plugin()
        self.helpers.create_cluster(name=self.__class__.__name__)
        self.activate_plugin()
<|end_body_0|>

<|body_start_1|>
        self.check_run('deploy_influxdb_grafana')
        self.env.revert_snapshot('ready_with_3_slaves')
        self.prepare_plugin()
        self.helpers.create_cluster(name=self.__class__.__name__)
        self.activate_plugin()
        self.helpers.deploy_cluster(self.base_nodes)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.env.make_snapshot('deploy_influxdb_grafana', is_make=True)
<|end_body_1|>

<|body_start_2|>
        self.check_run('deploy_ha_influxdb_grafana')
        self.env.revert_snapshot('ready_with_9_slaves')
        self.prepare_plugin()
        self.helpers.create_cluster(name=self.__class__.__name__)
        self.activate_plugin()
        self.helpers.deploy_cluster(self.full_ha_nodes)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.env.make_snapshot('deploy_ha_influxdb_grafana', is_make=True)
<|end_body_2|>

<|body_start_3|>
        self.env.revert_snapshot('ready_with_3_slaves')
        self.prepare_plugin()
        self.uninstall_plugin()
<|end_body_3|>

<|body_start_4|>
        self.env.revert_snapshot('deploy_influxdb_grafana')
        self.check_uninstall_failure()
        self.fuel_web.delete_env_wait(self.helpers.cluster_id)
        self.uninstall_plugin()
<|end_body_4|>

Solution:
class TestInfluxdbPlugin:
    """Class for smoke testing the InfluxDB-Grafana plugin."""

    def install_influxdb_grafana(self):
        """Install InfluxDB-Grafana plugin and check it exists Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m"""
        self.env.revert_snapshot('ready_with_3_slaves')
        self.prepare_plugin()
        self.helpers.create_cluster(name=self.__class__.__name__)
        self.activate_plugin()

    def deploy_influxdb_grafana(self):
        """Deploy a cluster with the InfluxDB-Grafana plugin Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 1 node with controller role 5. Add 1 node with compute and cinder roles 6. Add 1 node with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 60m Snapshot deploy_influxdb_grafana"""
        self.check_run('deploy_influxdb_grafana')
        self.env.revert_snapshot('ready_with_3_slaves')
        self.prepare_plugin()
        self.helpers.create_cluster(name=self.__class__.__name__)
        self.activate_plugin()
        self.helpers.deploy_cluster(self.base_nodes)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.env.make_snapshot('deploy_influxdb_grafana', is_make=True)

    def deploy_ha_influxdb_grafana(self):
        """Deploy a cluster with the InfluxDB-Grafana plugin in HA mode Scenario: 1. Upload the InfluxDB/Grafana plugin to the master node 2. Install the plugin 3. Create the cluster 4. Add 3 nodes with controller role 5. Add 3 nodes with compute and cinder roles 6. Add 3 nodes with influxdb_grafana role 7. Deploy the cluster 8. Check that InfluxDB/Grafana are running 9. Run OSTF Duration 120m Snapshot deploy_ha_influxdb_grafana"""
        self.check_run('deploy_ha_influxdb_grafana')
        self.env.revert_snapshot('ready_with_9_slaves')
        self.prepare_plugin()
        self.helpers.create_cluster(name=self.__class__.__name__)
        self.activate_plugin()
        self.helpers.deploy_cluster(self.full_ha_nodes)
        self.check_plugin_online()
        self.helpers.run_ostf()
        self.env.make_snapshot('deploy_ha_influxdb_grafana', is_make=True)

    def uninstall_influxdb_grafana(self):
        """Uninstall the InfluxDB-Grafana plugin Scenario: 1. Install the plugin. 2. Remove the plugin. Duration 5m"""
        self.env.revert_snapshot('ready_with_3_slaves')
        self.prepare_plugin()
        self.uninstall_plugin()

    def uninstall_deployed_influxdb_grafana(self):
        """Uninstall the InfluxDB-Grafana plugin with a deployed environment Scenario: 1. Try to remove the plugin using the Fuel CLI 2. Check plugin can't be uninstalled on deployed cluster. 3. Remove the environment. 4. Remove the plugin. Duration 20m"""
        self.env.revert_snapshot('deploy_influxdb_grafana')
        self.check_uninstall_failure()
        self.fuel_web.delete_env_wait(self.helpers.cluster_id)
        self.uninstall_plugin()


numSubarrayBoundedMax

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numSubarrayBoundedMax(self, A: List[int], L: int, R: int) -> int: 09/17/2020 16:36
- def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int: Segment tree. TLE Time complexity: O(n^2 * log(n))
- def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int: Count the number of valid intervals that end at i If ith number is smaller than `left`, possible starting indexes are between the last index whose value is larger than the `right` and the last index whose value is between `left` and `right`. If ith number is between `left` and `right`, any indexes before i can be starting index. If ith number is larget than `right`, non is possible. Need to keep the last index whose value is between `left` and `right` and the last index whoe value is larger than `right` Time complexity: O(n)

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def numSubarrayBoundedMax(self, A: List[int], L: int, R: int) -> int: 09/17/2020 16:36
- def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int: Segment tree. TLE Time complexity: O(n^2 * log(n))
- def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int: Count the number of valid intervals that end at i If ith number is smaller than `left`, possible starting indexes are between the last index whose value is larger than the `right` and the last index whose value is between `left` and `right`. If ith number is between `left` and `right`, any indexes before i can be starting index. If ith number is larget than `right`, non is possible. Need to keep the last index whose value is between `left` and `right` and the last index whoe value is larger than `right` Time complexity: O(n)

<|skeleton|>
class Solution:

    def numSubarrayBoundedMax(self, A: List[int], L: int, R: int) -> int:
        """09/17/2020 16:36"""
        <|body_0|>

    def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int:
        """Segment tree. TLE Time complexity: O(n^2 * log(n))"""
        <|body_1|>

    def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int:
        """Count the number of valid intervals that end at i If ith number is smaller than `left`, possible starting indexes are between the last index whose value is larger than the `right` and the last index whose value is between `left` and `right`. If ith number is between `left` and `right`, any indexes before i can be starting index. If ith number is larget than `right`, non is possible. Need to keep the last index whose value is between `left` and `right` and the last index whoe value is larger than `right` Time complexity: O(n)"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        cnt = 0
        p = -1
        s = -1
        for i, n in enumerate(A):
            if n > R:
                if p >= 0:
                    cnt += (i - p - 1) * (p - s)
                s = i
                p = -1
            elif L <= n <= R:
                if p >= 0:
                    cnt += (i - p - 1) * (p - s)
                cnt += i - s
                p = i
        if p >= 0:
            cnt += (len(A) - p - 1) * (p - s)
        return cnt
<|end_body_0|>

<|body_start_1|>
        if not nums:
            return 0
        tree = SegmentTree(nums, merge=max)
        cnt = 0
        for i in range(len(nums)):
            for j in range(i, len(nums)):
                if left <= tree.query(i, j) <= right:
                    cnt += 1
        return cnt
<|end_body_1|>

<|body_start_2|>
        cnt = 0
        p = -1
        s = -1
        for i, n in enumerate(nums):
            if n < left:
                if p >= 0:
                    cnt += p - s
            elif left <= n <= right:
                cnt += i - s
                p = i
            else:
                p = -1
                s = i
        return cnt
<|end_body_2|>

Solution:
class Solution:
    def numSubarrayBoundedMax(self, A: List[int], L: int, R: int) -> int:
        """09/17/2020 16:36"""
        cnt = 0
        p = -1
        s = -1
        for i, n in enumerate(A):
            if n > R:
                if p >= 0:
                    cnt += (i - p - 1) * (p - s)
                s = i
                p = -1
            elif L <= n <= R:
                if p >= 0:
                    cnt += (i - p - 1) * (p - s)
                cnt += i - s
                p = i
        if p >= 0:
            cnt += (len(A) - p - 1) * (p - s)
        return cnt

    def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int:
        """Segment tree. TLE Time complexity: O(n^2 * log(n))"""
        if not nums:
            return 0
        tree = SegmentTree(nums, merge=max)
        cnt = 0
        for i in range(len(nums)):
            for j in range(i, len(nums)):
                if left <= tree.query(i, j) <= right:
                    cnt += 1
        return cnt

    def numSubarrayBoundedMax(self, nums: List[int], left: int, right: int) -> int:
        """Count the number of valid intervals that end at i If ith number is smaller than `left`, possible starting indexes are between the last index whose value is larger than the `right` and the last index whose value is between `left` and `right`. If ith number is between `left` and `right`, any indexes before i can be starting index. If ith number is larget than `right`, non is possible. Need to keep the last index whose value is between `left` and `right` and the last index whoe value is larger than `right` Time complexity: O(n)"""
        cnt = 0
        p = -1
        s = -1
        for i, n in enumerate(nums):
            if n < left:
                if p >= 0:
                    cnt += p - s
            elif left <= n <= right:
                cnt += i - s
                p = i
            else:
                p = -1
                s = i
        return cnt


addTwoNumbers

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def addTwoNumbers(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def addTwoNumbers(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode

<|skeleton|>
class Solution:

    def reverseList(self, head):
        """:type head: ListNode :rtype: ListNode"""
        <|body_0|>

    def addTwoNumbers(self, l1, l2):
        """:type l1: ListNode :type l2: ListNode :rtype: ListNode"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        pre = None
        cur = head
        while cur:
            tmp = cur.next
            cur.next = pre
            pre = cur
            cur = tmp
        return pre
<|end_body_0|>

<|body_start_1|>
        n_l1, n_l2 = (self.reverseList(l1), self.reverseList(l2))
        carry = 0
        dummy = cur = ListNode(0)
        while n_l1 or n_l2:
            if not n_l1:
                val1 = 0
            else:
                val1 = n_l1.val
            if not n_l2:
                val2 = 0
            else:
                val2 = n_l2.val
            val = val1 + val2 + carry
            if val >= 10:
                carry = 1
            else:
                carry = 0
            val = val % 10
            cur.next = ListNode(val)
            cur = cur.next
            if n_l1:
                n_l1 = n_l1.next
            if n_l2:
                n_l2 = n_l2.next
        if carry:
            cur.next = ListNode(1)
        res = self.reverseList(dummy.next)
        return res
<|end_body_1|>

Solution:
class Solution:
    def reverseList(self, head):
        """:type head: ListNode :rtype: ListNode"""
        pre = None
        cur = head
        while cur:
            tmp = cur.next
            cur.next = pre
            pre = cur
            cur = tmp
        return pre

    def addTwoNumbers(self, l1, l2):
        """:type l1: ListNode :type l2: ListNode :rtype: ListNode"""
        n_l1, n_l2 = (self.reverseList(l1), self.reverseList(l2))
        carry = 0
        dummy = cur = ListNode(0)
        while n_l1 or n_l2:
            if not n_l1:
                val1 = 0
            else:
                val1 = n_l1.val
            if not n_l2:
                val2 = 0
            else:
                val2 = n_l2.val
            val = val1 + val2 + carry
            if val >= 10:
                carry = 1
            else:
                carry = 0
            val = val % 10
            cur.next = ListNode(val)
            cur = cur.next
            if n_l1:
                n_l1 = n_l1.next
            if n_l2:
                n_l2 = n_l2.next
        if carry:
            cur.next = ListNode(1)
        res = self.reverseList(dummy.next)
        return res


Prompt:
Implement the Python class `AdminTenants` described below.

Class description:
Implement the AdminTenants class.

Method signatures and docstrings:
- def get(self, request): Get all tenants
- def post(self, request): Create a new tenant

Implement the Python class `AdminTenants` described below.

Class description:
Implement the AdminTenants class.

Method signatures and docstrings:
- def get(self, request): Get all tenants
- def post(self, request): Create a new tenant

<|skeleton|>
class AdminTenants:

    def get(self, request):
        """Get all tenants"""
        <|body_0|>

    def post(self, request):
        """Create a new tenant"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        insts = Tenant.objects.all()
        list_insts = []
        for tenant in insts:
            list_insts.append({'id': tenant.id, 'name': tenant.name, 'ctime': tenant.create_time, 'space_quota': TenantQuota.objects.get_or_none(tenant=tenant), 'space_usage': get_tenant_space_usage(tenant)})
        return api_response(data={'insts': list_insts})
<|end_body_0|>

<|body_start_1|>
        inst_name = request.POST.get('name').strip()
        if not inst_name:
            return api_error(code=400, msg=_('Tenant name is required.'))
        try:
            Tenant.objects.get(name=inst_name)
            return api_error(code=400, msg=_('Tenant existed. Please choose another name for the tenant.'))
        except Tenant.DoesNotExist:
            pass
        try:
            Tenant.objects.create(name=inst_name)
            return api_response(code=200, msg=_('New tenant created successfully.'))
        except Exception as e:
            logger.error(e)
            return api_error(code=500, msg=_('Internal server error.'))
<|end_body_1|>

Solution:
class AdminTenants:
    def get(self, request):
        """Get all tenants"""
        insts = Tenant.objects.all()
        list_insts = []
        for tenant in insts:
            list_insts.append({'id': tenant.id, 'name': tenant.name, 'ctime': tenant.create_time, 'space_quota': TenantQuota.objects.get_or_none(tenant=tenant), 'space_usage': get_tenant_space_usage(tenant)})
        return api_response(data={'insts': list_insts})

    def post(self, request):
        """Create a new tenant"""
        inst_name = request.POST.get('name').strip()
        if not inst_name:
            return api_error(code=400, msg=_('Tenant name is required.'))
        try:
            Tenant.objects.get(name=inst_name)
            return api_error(code=400, msg=_('Tenant existed. Please choose another name for the tenant.'))
        except Tenant.DoesNotExist:
            pass
        try:
            Tenant.objects.create(name=inst_name)
            return api_response(code=200, msg=_('New tenant created successfully.'))
        except Exception as e:
            logger.error(e)
            return api_error(code=500, msg=_('Internal server error.'))


call

Prompt:
Implement the Python class `DecoderBlock` described below.

Class description:
Class representation of a decoder block for a transformer

Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): dm: Dimensionality of the model h: Number of heads hidden: Number of hidden units in the fully connected layer drop_rate: Dropout rate
- def call(self, x, encoder_output, training, look_ahead_mask, padding_mask): x: tensor of shape (batch, target_seq_len, dm)containing the input to the decoder block encoder_output: tensor of shape (batch, input_seq_len, dm)containing the output of the encoder training: boolean to determine if the model is training look_ahead_mask: mask to be applied to the first multi head attention layer padding_mask: mask to be applied to the second multi head attention layer Returns: tensor of shape (batch, target_seq_len, dm) containing the block's output

Implement the Python class `DecoderBlock` described below.

Class description:
Class representation of a decoder block for a transformer

Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): dm: Dimensionality of the model h: Number of heads hidden: Number of hidden units in the fully connected layer drop_rate: Dropout rate
- def call(self, x, encoder_output, training, look_ahead_mask, padding_mask): x: tensor of shape (batch, target_seq_len, dm)containing the input to the decoder block encoder_output: tensor of shape (batch, input_seq_len, dm)containing the output of the encoder training: boolean to determine if the model is training look_ahead_mask: mask to be applied to the first multi head attention layer padding_mask: mask to be applied to the second multi head attention layer Returns: tensor of shape (batch, target_seq_len, dm) containing the block's output

<|skeleton|>
class DecoderBlock:
    """Class representation of a decoder block for a transformer"""

    def __init__(self, dm, h, hidden, drop_rate=0.1):
        """dm: Dimensionality of the model h: Number of heads hidden: Number of hidden units in the fully connected layer drop_rate: Dropout rate"""
        <|body_0|>

    def call(self, x, encoder_output, training, look_ahead_mask, padding_mask):
        """x: tensor of shape (batch, target_seq_len, dm)containing the input to the decoder block encoder_output: tensor of shape (batch, input_seq_len, dm)containing the output of the encoder training: boolean to determine if the model is training look_ahead_mask: mask to be applied to the first multi head attention layer padding_mask: mask to be applied to the second multi head attention layer Returns: tensor of shape (batch, target_seq_len, dm) containing the block's output"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(DecoderBlock, self).__init__()
        self.mha1 = MultiHeadAttention(dm, h)
        self.mha2 = MultiHeadAttention(dm, h)
        self.dense_hidden = tf.keras.layers.Dense(units=hidden, activation='relu')
        self.dense_output = tf.keras.layers.Dense(units=dm)
        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.dropout1 = tf.keras.layers.Dropout(drop_rate)
        self.dropout2 = tf.keras.layers.Dropout(drop_rate)
        self.dropout3 = tf.keras.layers.Dropout(drop_rate)
<|end_body_0|>

<|body_start_1|>
        attn_out, _ = self.mha1(x, x, x, look_ahead_mask)
        attn_out = self.dropout1(attn_out, training=training)
        out1 = self.layernorm1(x + attn_out)
        attn_out, _ = self.mha2(out1, encoder_output, encoder_output, padding_mask)
        attn_out = self.dropout2(attn_out, training=training)
        out2 = self.layernorm2(out1 + attn_out)
        dense_output = self.dense_hidden(out2)
        dense_output = self.dense_output(dense_output)
        dense_output = self.dropout3(dense_output, training=training)
        out3 = self.layernorm3(out2 + dense_output)
        return out3
<|end_body_1|>

Solution:
class DecoderBlock:
    """Class representation of a decoder block for a transformer"""

    def __init__(self, dm, h, hidden, drop_rate=0.1):
        """dm: Dimensionality of the model h: Number of heads hidden: Number of hidden units in the fully connected layer drop_rate: Dropout rate"""
        super(DecoderBlock, self).__init__()
        self.mha1 = MultiHeadAttention(dm, h)
        self.mha2 = MultiHeadAttention(dm, h)
        self.dense_hidden = tf.keras.layers.Dense(units=hidden, activation='relu')
        self.dense_output = tf.keras.layers.Dense(units=dm)
        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-06)
        self.dropout1 = tf.keras.layers.Dropout(drop_rate)
        self.dropout2 = tf.keras.layers.Dropout(drop_rate)
        self.dropout3 = tf.keras.layers.Dropout(drop_rate)

    def call(self, x, encoder_output, training, look_ahead_mask, padding_mask):
        """x: tensor of shape (batch, target_seq_len, dm)containing the input to the decoder block encoder_output: tensor of shape (batch, input_seq_len, dm)containing the output of the encoder training: boolean to determine if the model is training look_ahead_mask: mask to be applied to the first multi head attention layer padding_mask: mask to be applied to the second multi head attention layer Returns: tensor of shape (batch, target_seq_len, dm) containing the block's output"""
        attn_out, _ = self.mha1(x, x, x, look_ahead_mask)
        attn_out = self.dropout1(attn_out, training=training)
        out1 = self.layernorm1(x + attn_out)
        attn_out, _ = self.mha2(out1, encoder_output, encoder_output, padding_mask)
        attn_out = self.dropout2(attn_out, training=training)
        out2 = self.layernorm2(out1 + attn_out)
        dense_output = self.dense_hidden(out2)
        dense_output = self.dense_output(dense_output)
        dense_output = self.dropout3(dense_output, training=training)
        out3 = self.layernorm3(out2 + dense_output)
        return out3


Prompt:
Implement the Python class `XmlEnumeration` described below.

Class description:
Provides ``to_xml()`` and ``from_xml()`` methods in addition to base enumeration features

Method signatures and docstrings:
- def from_xml(cls, xml_val): Return the enumeration member corresponding to the XML value *xml_val*.
- def to_xml(cls, enum_val): Return the XML value of the enumeration value *enum_val*.

Implement the Python class `XmlEnumeration` described below.

Class description:
Provides ``to_xml()`` and ``from_xml()`` methods in addition to base enumeration features

Method signatures and docstrings:
- def from_xml(cls, xml_val): Return the enumeration member corresponding to the XML value *xml_val*.
- def to_xml(cls, enum_val): Return the XML value of the enumeration value *enum_val*.

<|skeleton|>
class XmlEnumeration:
    """Provides ``to_xml()`` and ``from_xml()`` methods in addition to base enumeration features"""

    def from_xml(cls, xml_val):
        """Return the enumeration member corresponding to the XML value *xml_val*."""
        <|body_0|>

    def to_xml(cls, enum_val):
        """Return the XML value of the enumeration value *enum_val*."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if xml_val not in cls._xml_to_member:
            raise InvalidXmlError("attribute value '%s' not valid for this type" % xml_val)
        return cls._xml_to_member[xml_val]
<|end_body_0|>

<|body_start_1|>
        if enum_val not in cls._member_to_xml:
            raise ValueError("value '%s' not in enumeration %s" % (enum_val, cls.__name__))
        return cls._member_to_xml[enum_val]
<|end_body_1|>

Solution:
class XmlEnumeration:
    """Provides ``to_xml()`` and ``from_xml()`` methods in addition to base enumeration features"""

    def from_xml(cls, xml_val):
        """Return the enumeration member corresponding to the XML value *xml_val*."""
        if xml_val not in cls._xml_to_member:
            raise InvalidXmlError("attribute value '%s' not valid for this type" % xml_val)
        return cls._xml_to_member[xml_val]

    def to_xml(cls, enum_val):
        """Return the XML value of the enumeration value *enum_val*."""
        if enum_val not in cls._member_to_xml:
            raise ValueError("value '%s' not in enumeration %s" % (enum_val, cls.__name__))
        return cls._member_to_xml[enum_val]


Prompt:
Implement the Python class `FunctionTest` described below.

Class description:
verify that functions works fine

Method signatures and docstrings:
- def test_anagram(self): verify anagram works fine
- def test_anagram_dd(self): verify anagram_dd works fine
- def test_book_index(self): verify that book_index works fine

Implement the Python class `FunctionTest` described below.

Class description:
verify that functions works fine

Method signatures and docstrings:
- def test_anagram(self): verify anagram works fine
- def test_anagram_dd(self): verify anagram_dd works fine
- def test_book_index(self): verify that book_index works fine

<|skeleton|>
class FunctionTest:
    """verify that functions works fine"""

    def test_anagram(self):
        """verify anagram works fine"""
        <|body_0|>

    def test_anagram_dd(self):
        """verify anagram_dd works fine"""
        <|body_1|>

    def test_book_index(self):
        """verify that book_index works fine"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.assertTrue(anagram('cinema', 'iceman'))
        self.assertTrue(anagram('dormitory', 'dirtyroom'))
        self.assertFalse(anagram('hello', 'lohae'))
        self.assertFalse(anagram('ill', 'like'))
        self.assertFalse(anagram('illness', 'nes'))
<|end_body_0|>

<|body_start_1|>
        self.assertTrue(anagram_dd('cinema', 'iceman'))
        self.assertTrue(anagram_dd('dormitory', 'dirtyroom'))
        self.assertFalse(anagram_dd('hello', 'lohae'))
        self.assertFalse(anagram_dd('ill', 'like'))
        self.assertFalse(anagram('illness', 'nes'))
<|end_body_1|>

<|body_start_2|>
        woodchucks = [('how', 3), ('much', 3), ('wood', 3), ('would', 2), ('a', 1), ('woodchuck', 1), ('chuck', 3), ('if', 1), ('a', 1), ('woodchuck', 2), ('could', 2), ('chuck', 1), ('wood', 1)]
        result1 = [['a', [1]], ['chuck', [1, 3]], ['could', [2]], ['how', [3]], ['if', [1]], ['much', [3]], ['wood', [1, 3]], ['woodchuck', [1, 2]], ['would', [2]]]
        self.assertEqual(book_index(woodchucks), result1)
<|end_body_2|>

Solution:
class FunctionTest:
    """verify that functions works fine"""

    def test_anagram(self):
        """verify anagram works fine"""
        self.assertTrue(anagram('cinema', 'iceman'))
        self.assertTrue(anagram('dormitory', 'dirtyroom'))
        self.assertFalse(anagram('hello', 'lohae'))
        self.assertFalse(anagram('ill', 'like'))
        self.assertFalse(anagram('illness', 'nes'))

    def test_anagram_dd(self):
        """verify anagram_dd works fine"""
        self.assertTrue(anagram_dd('cinema', 'iceman'))
        self.assertTrue(anagram_dd('dormitory', 'dirtyroom'))
        self.assertFalse(anagram_dd('hello', 'lohae'))
        self.assertFalse(anagram_dd('ill', 'like'))
        self.assertFalse(anagram('illness', 'nes'))

    def test_book_index(self):
        """verify that book_index works fine"""
        woodchucks = [('how', 3), ('much', 3), ('wood', 3), ('would', 2), ('a', 1), ('woodchuck', 1), ('chuck', 3), ('if', 1), ('a', 1), ('woodchuck', 2), ('could', 2), ('chuck', 1), ('wood', 1)]
        result1 = [['a', [1]], ['chuck', [1, 3]], ['could', [2]], ['how', [3]], ['if', [1]], ['much', [3]], ['wood', [1, 3]], ['woodchuck', [1, 2]], ['would', [2]]]
        self.assertEqual(book_index(woodchucks), result1)


Prompt:
Implement the Python class `Bond` described below.

Class description:
Representation of a Bond

Method signatures and docstrings:
- def __init__(self, payment_dates, payment_steps, coupon_rates, frequency=2): Initialize a Bond object Parameters ---------- payment_dates : array_like of shape (M, ) with datetime payment dates payment_steps : array_like of shape (M, ) with integer payment steps that corresponds to the tree coupon_rates : scalar or array_like of shape (M, ) with the coupon rates frequency : integer scalar that specifies the number of payment for each year, default value is 2
- def build_hw_tree(self, hw_tree): Helper function that builds and calibrate a Hull-White tree Parameters ---------- hw_tree: a HWTree class instance
- def get_price(self, hw_tree): This function computes the bond price Parameters ---------- hw_tree: an HWTree class instance Return ------ out: float scalar that specifies the bond price

Implement the Python class `Bond` described below.

Class description:
Representation of a Bond

Method signatures and docstrings:
- def __init__(self, payment_dates, payment_steps, coupon_rates, frequency=2): Initialize a Bond object Parameters ---------- payment_dates : array_like of shape (M, ) with datetime payment dates payment_steps : array_like of shape (M, ) with integer payment steps that corresponds to the tree coupon_rates : scalar or array_like of shape (M, ) with the coupon rates frequency : integer scalar that specifies the number of payment for each year, default value is 2
- def build_hw_tree(self, hw_tree): Helper function that builds and calibrate a Hull-White tree Parameters ---------- hw_tree: a HWTree class instance
- def get_price(self, hw_tree): This function computes the bond price Parameters ---------- hw_tree: an HWTree class instance Return ------ out: float scalar that specifies the bond price

<|skeleton|>
class Bond:
    """Representation of a Bond"""

    def __init__(self, payment_dates, payment_steps, coupon_rates, frequency=2):
        """Initialize a Bond object Parameters ---------- payment_dates : array_like of shape (M, ) with datetime payment dates payment_steps : array_like of shape (M, ) with integer payment steps that corresponds to the tree coupon_rates : scalar or array_like of shape (M, ) with the coupon rates frequency : integer scalar that specifies the number of payment for each year, default value is 2"""
        <|body_0|>

    def build_hw_tree(self, hw_tree):
        """Helper function that builds and calibrate a Hull-White tree Parameters ---------- hw_tree: a HWTree class instance"""
        <|body_1|>

    def get_price(self, hw_tree):
        """This function computes the bond price Parameters ---------- hw_tree: an HWTree class instance Return ------ out: float scalar that specifies the bond price"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self._payment_dates = payment_dates
        self._payment_steps = payment_steps
        self._maturity = payment_dates[len(payment_dates) - 1]
        self._steps = payment_steps[len(payment_steps) - 1]
        self._coupon_rates = coupon_rates
        self._frequency = frequency
        self._bond_tree = {}
<|end_body_0|>

<|body_start_1|>
        if not hw_tree._is_built:
            hw_tree.hw_prob()
            hw_tree.calibrate()
<|end_body_1|>

<|body_start_2|>
        self.build_hw_tree(hw_tree)
        for i in reversed(range(self._steps + 1)):
            for j in range(-i, i + 1):
                if i == self._steps:
                    index = self._payment_steps.tolist().index(i)
                    self._bond_tree[i, j] = 1 + self._coupon_rates[index] / self._frequency
                    continue
                else:
                    discounted_expected_value = 0
                    for k in range(j - 2, j + 3):
                        if k <= i + 1 and k >= -i - 1:
                            discounted_expected_value += hw_tree.prob(j, k) * self._bond_tree[i + 1, k]
                        else:
                            continue
                    discounted_expected_value *= hw_tree._discount_factor_tree[i, j]
                    if i in self._payment_steps.tolist():
                        index = self._payment_steps.tolist().index(i)
                        self._bond_tree[i, j] = discounted_expected_value + self._coupon_rates[index] / self._frequency
                    else:
                        self._bond_tree[i, j] = discounted_expected_value
        return self._bond_tree[0, 0]
<|end_body_2|>

Solution:
class Bond:
    """Representation of a Bond"""

    def __init__(self, payment_dates, payment_steps, coupon_rates, frequency=2):
        """Initialize a Bond object Parameters ---------- payment_dates : array_like of shape (M, ) with datetime payment dates payment_steps : array_like of shape (M, ) with integer payment steps that corresponds to the tree coupon_rates : scalar or array_like of shape (M, ) with the coupon rates frequency : integer scalar that specifies the number of payment for each year, default value is 2"""
        self._payment_dates = payment_dates
        self._payment_steps = payment_steps
        self._maturity = payment_dates[len(payment_dates) - 1]
        self._steps = payment_steps[len(payment_steps) - 1]
        self._coupon_rates = coupon_rates
        self._frequency = frequency
        self._bond_tree = {}

    def build_hw_tree(self, hw_tree):
        """Helper function that builds and calibrate a Hull-White tree Parameters ---------- hw_tree: a HWTree class instance"""
        if not hw_tree._is_built:
            hw_tree.hw_prob()
            hw_tree.calibrate()

    def get_price(self, hw_tree):
        """This function computes the bond price Parameters ---------- hw_tree: an HWTree class instance Return ------ out: float scalar that specifies the bond price"""
        self.build_hw_tree(hw_tree)
        for i in reversed(range(self._steps + 1)):
            for j in range(-i, i + 1):
                if i == self._steps:
                    index = self._payment_steps.tolist().index(i)
                    self._bond_tree[i, j] = 1 + self._coupon_rates[index] / self._frequency
                    continue
                else:
                    discounted_expected_value = 0
                    for k in range(j - 2, j + 3):
                        if k <= i + 1 and k >= -i - 1:
                            discounted_expected_value += hw_tree.prob(j, k) * self._bond_tree[i + 1, k]
                        else:
                            continue
                    discounted_expected_value *= hw_tree._discount_factor_tree[i, j]
                    if i in self._payment_steps.tolist():
                        index = self._payment_steps.tolist().index(i)
                        self._bond_tree[i, j] = discounted_expected_value + self._coupon_rates[index] / self._frequency
                    else:
                        self._bond_tree[i, j] = discounted_expected_value
        return self._bond_tree[0, 0]


Prompt:
Implement the Python class `XMLTree` described below.

Class description:
Implement the XMLTree class.

Method signatures and docstrings:
- def __init__(self, data): The XML tree representation of the data. Allows performing operations on this tree or querying it. :param data: The data to parse. :type data: str
- def is_data_unclean(self): Ensure that the tree parses as XML and that it contains only div elements in the ``http://www.w3.org/1999/xhtml`` namespace, no processing instructions, no attributes in any namespace and no attribute other than ``class`` or ``data-wed-*``. :returns: Evaluates to False if the tree is clean, True if not. When unclean the value returned is a diagnosis message. .. warning:: This method is security-critical. In theory it would be possible for one user of the system to include JavaScript in the data they send to BTW. This JavaScript could then be loaded in someone else's browser and executed there.
- def extract_headword(self): Extracts the headword from the XML tree. This is the contents of the btw:lemma element. :returns: The headword. :rtype: str
- def extract_authority(self): Extracts the authority from the XML tree. This is the contents of the authority attribute on the top element. :returns: The authority :rtype: str

Implement the Python class `XMLTree` described below.

Class description:
Implement the XMLTree class.

Method signatures and docstrings:
- def __init__(self, data): The XML tree representation of the data. Allows performing operations on this tree or querying it. :param data: The data to parse. :type data: str
- def is_data_unclean(self): Ensure that the tree parses as XML and that it contains only div elements in the ``http://www.w3.org/1999/xhtml`` namespace, no processing instructions, no attributes in any namespace and no attribute other than ``class`` or ``data-wed-*``. :returns: Evaluates to False if the tree is clean, True if not. When unclean the value returned is a diagnosis message. .. warning:: This method is security-critical. In theory it would be possible for one user of the system to include JavaScript in the data they send to BTW. This JavaScript could then be loaded in someone else's browser and executed there.
- def extract_headword(self): Extracts the headword from the XML tree. This is the contents of the btw:lemma element. :returns: The headword. :rtype: str
- def extract_authority(self): Extracts the authority from the XML tree. This is the contents of the authority attribute on the top element. :returns: The authority :rtype: str

<|skeleton|>
class XMLTree:

    def __init__(self, data):
        """The XML tree representation of the data. Allows performing operations on this tree or querying it. :param data: The data to parse. :type data: str"""
        <|body_0|>

    def is_data_unclean(self):
        """Ensure that the tree parses as XML and that it contains only div elements in the ``http://www.w3.org/1999/xhtml`` namespace, no processing instructions, no attributes in any namespace and no attribute other than ``class`` or ``data-wed-*``. :returns: Evaluates to False if the tree is clean, True if not. When unclean the value returned is a diagnosis message. .. warning:: This method is security-critical. In theory it would be possible for one user of the system to include JavaScript in the data they send to BTW. This JavaScript could then be loaded in someone else's browser and executed there."""
        <|body_1|>

    def extract_headword(self):
        """Extracts the headword from the XML tree. This is the contents of the btw:lemma element. :returns: The headword. :rtype: str"""
        <|body_2|>

    def extract_authority(self):
        """Extracts the authority from the XML tree. This is the contents of the authority attribute on the top element. :returns: The authority :rtype: str"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.parsing_error = None
        self.tree = None
        try:
            self.tree = lxml.etree.fromstring(data)
        except lxml.etree.XMLSyntaxError as ex:
            self.parsing_error = 'Parsing error: ' + str(ex)
<|end_body_0|>

<|body_start_1|>
        if self.parsing_error:
            return self.parsing_error
        for node in self.tree.iter():
            if isinstance(node, lxml.etree._ProcessingInstruction):
                return 'Processing instruction found.'
            elif isinstance(node, lxml.etree._Element):
                if node.tag != '{http://www.w3.org/1999/xhtml}div':
                    return 'Element outside the xhtml namespace: ' + node.tag
                for attr in node.attrib.keys():
                    if attr == 'xmlns':
                        if node.attrib[attr] != 'http://www.w3.org/1999/xhtml':
                            return 'Attribute xmlns with invalid value: ' + node.attrib[attr] + '.'
                    elif attr != 'class' and (not attr.startswith('data-wed-')):
                        return 'Invalid attribute: ' + attr + '.'
        return False
<|end_body_1|>

<|body_start_2|>
        class_sought = 'btw:lemma'
        lemma = self.tree.xpath("xhtml:div[contains(@class, '" + class_sought + "')]", namespaces={'xhtml': 'http://www.w3.org/1999/xhtml'})
        if len(lemma):
            classes = lemma[0].get('class').strip().split()
            if not any((x == class_sought for x in classes)):
                lemma = []
        if not len(lemma):
            return None
        lemma = lemma[0].text
        if lemma is None:
            return None
        lemma = lemma.strip()
        if len(lemma) == 0:
            return None
        return lemma
<|end_body_2|>

<|body_start_3|>
        authority = self.tree.get('data-wed-authority')
        if authority is None:
            raise ValueError("can't find the authority in the data passed")
        return authority.strip()
<|end_body_3|>

Solution:
class XMLTree:
    def __init__(self, data):
        """The XML tree representation of the data. Allows performing operations on this tree or querying it. :param data: The data to parse. :type data: str"""
        self.parsing_error = None
        self.tree = None
        try:
            self.tree = lxml.etree.fromstring(data)
        except lxml.etree.XMLSyntaxError as ex:
            self.parsing_error = 'Parsing error: ' + str(ex)

    def is_data_unclean(self):
        """Ensure that the tree parses as XML and that it contains only div elements in the ``http://www.w3.org/1999/xhtml`` namespace, no processing instructions, no attributes in any namespace and no attribute other than ``class`` or ``data-wed-*``. :returns: Evaluates to False if the tree is clean, True if not. When unclean the value returned is a diagnosis message. .. warning:: This method is security-critical. In theory it would be possible for one user of the system to include JavaScript in the data they send to BTW. This JavaScript could then be loaded in someone else's browser and executed there."""
        if self.parsing_error:
            return self.parsing_error
        for node in self.tree.iter():
            if isinstance(node, lxml.etree._ProcessingInstruction):
                return 'Processing instruction found.'
            elif isinstance(node, lxml.etree._Element):
                if node.tag != '{http://www.w3.org/1999/xhtml}div':
                    return 'Element outside the xhtml namespace: ' + node.tag
                for attr in node.attrib.keys():
                    if attr == 'xmlns':
                        if node.attrib[attr] != 'http://www.w3.org/1999/xhtml':
                            return 'Attribute xmlns with invalid value: ' + node.attrib[attr] + '.'
                    elif attr != 'class' and (not attr.startswith('data-wed-')):
                        return 'Invalid attribute: ' + attr + '.'
        return False

    def extract_headword(self):
        """Extracts the headword from the XML tree. This is the contents of the btw:lemma element. :returns: The headword. :rtype: str"""
        class_sought = 'btw:lemma'
        lemma = self.tree.xpath("xhtml:div[contains(@class, '" + class_sought + "')]", namespaces={'xhtml': 'http://www.w3.org/1999/xhtml'})
        if len(lemma):
            classes = lemma[0].get('class').strip().split()
            if not any((x == class_sought for x in classes)):
                lemma = []
        if not len(lemma):
            return None
        lemma = lemma[0].text
        if lemma is None:
            return None
        lemma = lemma.strip()
        if len(lemma) == 0:
            return None
        return lemma

    def extract_authority(self):
        """Extracts the authority from the XML tree. This is the contents of the authority attribute on the top element. :returns: The authority :rtype: str"""
        authority = self.tree.get('data-wed-authority')
        if authority is None:
            raise ValueError("can't find the authority in the data passed")
        return authority.strip()


post

Prompt:
Implement the Python class `BrowserObjectView` described below.

Class description:
Handle uploads from browser

Method signatures and docstrings:
- def handle_post_file(self, post_file) -> Tuple[Object, bool]: Handle upload of a single file, computes hashes and returns existing Upload instance and False as tuple if file was uploaded already. Otherwise, new Upload instance is created and returned in a tuple with True.
- def post(self, request: HttpRequest) -> HttpResponse: Create Upload objects from request

Implement the Python class `BrowserObjectView` described below.

Class description:
Handle uploads from browser

Method signatures and docstrings:
- def handle_post_file(self, post_file) -> Tuple[Object, bool]: Handle upload of a single file, computes hashes and returns existing Upload instance and False as tuple if file was uploaded already. Otherwise, new Upload instance is created and returned in a tuple with True.
- def post(self, request: HttpRequest) -> HttpResponse: Create Upload objects from request

<|skeleton|>
class BrowserObjectView:
    """Handle uploads from browser"""

    def handle_post_file(self, post_file) -> Tuple[Object, bool]:
        """Handle upload of a single file, computes hashes and returns existing Upload instance and False as tuple if file was uploaded already. Otherwise, new Upload instance is created and returned in a tuple with True."""
        <|body_0|>

    def post(self, request: HttpRequest) -> HttpResponse:
        """Create Upload objects from request"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        _, ext = os.path.splitext(post_file.name)
        ext = ext[1:] if ext.startswith('.') else ext
        hashes = generate_hashes(post_file)
        post_file.seek(0)
        existing = Object.objects.filter(sha512=hashes.get('sha512'))
        if existing.exists():
            LOGGER.debug('De-duped existing upload %s', existing.first().filename)
            return (existing.first(), False)
        new_upload = Object(file=save_from_post(post_file.read(), extension=ext))
        new_upload.save()
        LOGGER.info('Uploaded %s', new_upload.filename)
        return (new_upload, True)
<|end_body_0|>

<|body_start_1|>
        for __, _file in request.FILES.items():
            new_upload, _created = self.handle_post_file(_file)
            new_upload.user = request.user
            new_upload.save()
            ObjectViewFile.count_view(new_upload, request)
            LOGGER.info('Uploaded %s', new_upload.filename)
        return HttpResponse(status=204)
<|end_body_1|>

Solution:
class BrowserObjectView:
    """Handle uploads from browser"""

    def handle_post_file(self, post_file) -> Tuple[Object, bool]:
        """Handle upload of a single file, computes hashes and returns existing Upload instance and False as tuple if file was uploaded already. Otherwise, new Upload instance is created and returned in a tuple with True."""
        _, ext = os.path.splitext(post_file.name)
        ext = ext[1:] if ext.startswith('.') else ext
        hashes = generate_hashes(post_file)
        post_file.seek(0)
        existing = Object.objects.filter(sha512=hashes.get('sha512'))
        if existing.exists():
            LOGGER.debug('De-duped existing upload %s', existing.first().filename)
            return (existing.first(), False)
        new_upload = Object(file=save_from_post(post_file.read(), extension=ext))
        new_upload.save()
        LOGGER.info('Uploaded %s', new_upload.filename)
        return (new_upload, True)

    def post(self, request: HttpRequest) -> HttpResponse:
        """Create Upload objects from request"""
        for __, _file in request.FILES.items():
            new_upload, _created = self.handle_post_file(_file)
            new_upload.user = request.user
            new_upload.save()
            ObjectViewFile.count_view(new_upload, request)
            LOGGER.info('Uploaded %s', new_upload.filename)
        return HttpResponse(status=204)


testExtensibleDefaultTypeValid

Prompt:
Implement the Python class `UnionTest` described below.

Class description:
Tests union parsing behavior.

Method signatures and docstrings:
- def testExtensibleMustHaveDefault(self): Verifies that extensible unions must have a default field.
- def testExtensibleSingleDefault(self): Verifies that extensible unions must not have multiple default fields.
- def testExtensibleDefaultTypeValid(self): Verifies that an extensible union's default field must be nullable or integral type.

Implement the Python class `UnionTest` described below.

Class description:
Tests union parsing behavior.

Method signatures and docstrings:
- def testExtensibleMustHaveDefault(self): Verifies that extensible unions must have a default field.
- def testExtensibleSingleDefault(self): Verifies that extensible unions must not have multiple default fields.
- def testExtensibleDefaultTypeValid(self): Verifies that an extensible union's default field must be nullable or integral type.

<|skeleton|>
class UnionTest:
    """Tests union parsing behavior."""

    def testExtensibleMustHaveDefault(self):
        """Verifies that extensible unions must have a default field."""
        <|body_0|>

    def testExtensibleSingleDefault(self):
        """Verifies that extensible unions must not have multiple default fields."""
        <|body_1|>

    def testExtensibleDefaultTypeValid(self):
        """Verifies that an extensible union's default field must be nullable or integral type."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        mojom = 'foo.mojom'
        self.WriteFile(mojom, 'module foo; [Extensible] union U { bool x; };')
        with self.assertRaisesRegexp(Exception, 'must specify a \\[Default\\]'):
            self.ParseMojoms([mojom])
<|end_body_0|>

<|body_start_1|>
        mojom = 'foo.mojom'
        self.WriteFile(mojom, '               module foo;\n               [Extensible] union U {\n                 [Default] bool x;\n                 [Default] bool y;\n               };\n               ')
        with self.assertRaisesRegexp(Exception, 'Multiple \\[Default\\] fields'):
            self.ParseMojoms([mojom])
<|end_body_1|>

<|body_start_2|>
        mojom = 'foo.mojom'
        self.WriteFile(mojom, '               module foo;\n               [Extensible] union U {\n                 [Default] handle<message_pipe> p;\n               };\n               ')
        with self.assertRaisesRegexp(Exception, 'must be nullable or integral'):
            self.ParseMojoms([mojom])
<|end_body_2|>

Solution:
class UnionTest:
    """Tests union parsing behavior."""

    def testExtensibleMustHaveDefault(self):
        """Verifies that extensible unions must have a default field."""
        mojom = 'foo.mojom'
        self.WriteFile(mojom, 'module foo; [Extensible] union U { bool x; };')
        with self.assertRaisesRegexp(Exception, 'must specify a \\[Default\\]'):
            self.ParseMojoms([mojom])

    def testExtensibleSingleDefault(self):
        """Verifies that extensible unions must not have multiple default fields."""
        mojom = 'foo.mojom'
        self.WriteFile(mojom, '               module foo;\n               [Extensible] union U {\n                 [Default] bool x;\n                 [Default] bool y;\n               };\n               ')
        with self.assertRaisesRegexp(Exception, 'Multiple \\[Default\\] fields'):
            self.ParseMojoms([mojom])

    def testExtensibleDefaultTypeValid(self):
        """Verifies that an extensible union's default field must be nullable or integral type."""
        mojom = 'foo.mojom'
        self.WriteFile(mojom, '               module foo;\n               [Extensible] union U {\n                 [Default] handle<message_pipe> p;\n               };\n               ')
        with self.assertRaisesRegexp(Exception, 'must be nullable or integral'):
            self.ParseMojoms([mojom])


buildTree

Prompt:
Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def buildTree(self, preorder, inorder): :type preorder: List[int] :type inorder: List[int] :rtype: TreeNode
- def buildTruee(self, preorder, inorder): memory limit exceeded %>_<%

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def buildTree(self, preorder, inorder): :type preorder: List[int] :type inorder: List[int] :rtype: TreeNode
- def buildTruee(self, preorder, inorder): memory limit exceeded %>_<%

<|skeleton|>
class Solution:

    def buildTree(self, preorder, inorder):
        """:type preorder: List[int] :type inorder: List[int] :rtype: TreeNode"""
        <|body_0|>

    def buildTruee(self, preorder, inorder):
        """memory limit exceeded %>_<%"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        def _build(pre_start, pre_end, in_start, in_end):
            if pre_start == pre_end:
                return None
            root_val = preorder[pre_start]
            idx = inorder[in_start:in_end].index(root_val)
            root_node = TreeNode(root_val)
            left_node = _build(pre_start + 1, pre_start + 1 + idx, in_start, in_start + idx)
            right_node = _build(pre_start + idx + 1, pre_end, in_start + idx + 1, in_end)
            root_node.left = left_node
            root_node.right = right_node
            return root_node
        return _build(0, len(preorder), 0, len(inorder))
<|end_body_0|>

<|body_start_1|>
        if not preorder:
            return None
        root = preorder[0]
        idx = inorder.index(root)
        root_node = TreeNode(root)
        left_node = self.buildTree(preorder[1:idx + 1], inorder[:idx])
        right_node = self.buildTree(preorder[idx + 1:], inorder[idx + 1:])
        root_node.left = left_node
        root_node.right = right_node
        return root_node
<|end_body_1|>

Solution:
class Solution:
    def buildTree(self, preorder, inorder):
        """:type preorder: List[int] :type inorder: List[int] :rtype: TreeNode"""
        def _build(pre_start, pre_end, in_start, in_end):
            if pre_start == pre_end:
                return None
            root_val = preorder[pre_start]
            idx = inorder[in_start:in_end].index(root_val)
            root_node = TreeNode(root_val)
            left_node = _build(pre_start + 1, pre_start + 1 + idx, in_start, in_start + idx)
            right_node = _build(pre_start + idx + 1, pre_end, in_start + idx + 1, in_end)
            root_node.left = left_node
            root_node.right = right_node
            return root_node
        return _build(0, len(preorder), 0, len(inorder))

    def buildTruee(self, preorder, inorder):
        """memory limit exceeded %>_<%"""
        if not preorder:
            return None
        root = preorder[0]
        idx = inorder.index(root)
        root_node = TreeNode(root)
        left_node = self.buildTree(preorder[1:idx + 1], inorder[:idx])
        right_node = self.buildTree(preorder[idx + 1:], inorder[idx + 1:])
        root_node.left = left_node
        root_node.right = right_node
        return root_node

algorithms/105. Construct Binary Tree from Preorder and Inorder Traversal/main.py


forward

Prompt:
Implement the Python class `_UpsampleBlock` described below.

Class description:
Implement the _UpsampleBlock class.

Method signatures and docstrings:
- def __init__(self, in_feats, Norm, Conv, Deconv): Args: in_feats (int): Number of input channels. Norm (nn.Module): Normalization layer constructor. Conv (nn.Module): Convolutional layer constructor. Deconv (nn.Module): Transposed convolutional layer constructor.
- def forward(self, x): Performs a forward pass through the upsampling block. Args: x (torch.Tensor): Input tensor of shape (batch_size, in_feats, height, width). Returns: torch.Tensor: Output tensor of shape (batch_size, in_feats//4, 2*height, 2*width).

Implement the Python class `_UpsampleBlock` described below.

Class description:
Implement the _UpsampleBlock class.

Method signatures and docstrings:
- def __init__(self, in_feats, Norm, Conv, Deconv): Args: in_feats (int): Number of input channels. Norm (nn.Module): Normalization layer constructor. Conv (nn.Module): Convolutional layer constructor. Deconv (nn.Module): Transposed convolutional layer constructor.
- def forward(self, x): Performs a forward pass through the upsampling block. Args: x (torch.Tensor): Input tensor of shape (batch_size, in_feats, height, width). Returns: torch.Tensor: Output tensor of shape (batch_size, in_feats//4, 2*height, 2*width).

<|skeleton|>
class _UpsampleBlock:

    def __init__(self, in_feats, Norm, Conv, Deconv):
        """Args: in_feats (int): Number of input channels. Norm (nn.Module): Normalization layer constructor. Conv (nn.Module): Convolutional layer constructor. Deconv (nn.Module): Transposed convolutional layer constructor."""
        <|body_0|>

    def forward(self, x):
        """Performs a forward pass through the upsampling block. Args: x (torch.Tensor): Input tensor of shape (batch_size, in_feats, height, width). Returns: torch.Tensor: Output tensor of shape (batch_size, in_feats//4, 2*height, 2*width)."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        self.add_module('conv1', Conv(in_channels=in_feats, out_channels=in_feats // 2, kernel_size=3, stride=1, padding=1, bias=False))
        self.add_module('norm1', Norm(in_feats // 2))
        self.add_module('relu1', nn.ReLU(inplace=True))
        self.add_module('conv2', Conv(in_channels=in_feats // 2, out_channels=in_feats // 2, kernel_size=3, stride=1, padding=1, bias=False))
        self.add_module('norm2', Norm(in_feats // 2))
        self.add_module('relu2', nn.ReLU(inplace=True))
        self.add_module('deconv', Deconv(in_channels=in_feats // 2, out_channels=in_feats // 4, kernel_size=2, stride=2, padding=0, output_padding=0))
<|end_body_0|>

<|body_start_1|>
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.norm2(x)
        x = self.relu2(x)
        x = self.deconv(x)
        return x
<|end_body_1|>

Solution:
class _UpsampleBlock:
    def __init__(self, in_feats, Norm, Conv, Deconv):
        """Args: in_feats (int): Number of input channels. Norm (nn.Module): Normalization layer constructor. Conv (nn.Module): Convolutional layer constructor. Deconv (nn.Module): Transposed convolutional layer constructor."""
        super().__init__()
        self.add_module('conv1', Conv(in_channels=in_feats, out_channels=in_feats // 2, kernel_size=3, stride=1, padding=1, bias=False))
        self.add_module('norm1', Norm(in_feats // 2))
        self.add_module('relu1', nn.ReLU(inplace=True))
        self.add_module('conv2', Conv(in_channels=in_feats // 2, out_channels=in_feats // 2, kernel_size=3, stride=1, padding=1, bias=False))
        self.add_module('norm2', Norm(in_feats // 2))
        self.add_module('relu2', nn.ReLU(inplace=True))
        self.add_module('deconv', Deconv(in_channels=in_feats // 2, out_channels=in_feats // 4, kernel_size=2, stride=2, padding=0, output_padding=0))

    def forward(self, x):
        """Performs a forward pass through the upsampling block. Args: x (torch.Tensor): Input tensor of shape (batch_size, in_feats, height, width). Returns: torch.Tensor: Output tensor of shape (batch_size, in_feats//4, 2*height, 2*width)."""
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.norm2(x)
        x = self.relu2(x)
        x = self.deconv(x)
        return x


delete

Prompt:
Implement the Python class `DTableIOStatus` described below.

Class description:
Implement the DTableIOStatus class.

Method signatures and docstrings:
- def get(self, request): Get task status by task id :param request: :return:
- def delete(self, request): Delete task by task_id :param request: :return:

Implement the Python class `DTableIOStatus` described below.

Class description:
Implement the DTableIOStatus class.

Method signatures and docstrings:
- def get(self, request): Get task status by task id :param request: :return:
- def delete(self, request): Delete task by task_id :param request: :return:

<|skeleton|>
class DTableIOStatus:

    def get(self, request):
        """Get task status by task id :param request: :return:"""
        <|body_0|>

    def delete(self, request):
        """Delete task by task_id :param request: :return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        task_id = request.GET.get('task_id', '')
        if not task_id:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        resp = query_dtable_io_status(task_id)
        if resp.status_code == 400:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        if not resp.ok:
            logger.error(resp.content)
            return api_error(status.HTTP_500_INTERNAL_SERVER_ERROR, 'Internal Server Error')
        is_finished = json.loads(resp.content)['is_finished']
        return Response({'is_finished': is_finished})
<|end_body_0|>

<|body_start_1|>
        task_id = request.query_params.get('task_id', '')
        if not task_id:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        task_type = request.query_params.get('task_type', '')
        if task_type not in ['export', 'import']:
            error_msg = 'task_type invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        dtable_uuid = request.query_params.get('dtable_uuid', '')
        if not dtable_uuid:
            error_msg = 'dtable_uuid invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        resp = cancel_dtable_io_task(task_id, dtable_uuid)
        if resp.status_code == 400:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        if not resp.ok:
            logger.error(resp.content)
            return api_error(status.HTTP_500_INTERNAL_SERVER_ERROR, 'Internal Server Error')
        if task_type == 'import':
            try:
                dtable = DTables.objects.get(uuid=dtable_uuid)
                dtable.delete()
            except Exception:
                pass
        tmp_dir = os.path.join('/tmp/dtable-io', dtable_uuid)
        if os.path.exists(tmp_dir):
            shutil.rmtree(tmp_dir)
        return Response({'success': True})
<|end_body_1|>

Solution:
class DTableIOStatus:
    def get(self, request):
        """Get task status by task id :param request: :return:"""
        task_id = request.GET.get('task_id', '')
        if not task_id:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        resp = query_dtable_io_status(task_id)
        if resp.status_code == 400:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        if not resp.ok:
            logger.error(resp.content)
            return api_error(status.HTTP_500_INTERNAL_SERVER_ERROR, 'Internal Server Error')
        is_finished = json.loads(resp.content)['is_finished']
        return Response({'is_finished': is_finished})

    def delete(self, request):
        """Delete task by task_id :param request: :return:"""
        task_id = request.query_params.get('task_id', '')
        if not task_id:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        task_type = request.query_params.get('task_type', '')
        if task_type not in ['export', 'import']:
            error_msg = 'task_type invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        dtable_uuid = request.query_params.get('dtable_uuid', '')
        if not dtable_uuid:
            error_msg = 'dtable_uuid invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        resp = cancel_dtable_io_task(task_id, dtable_uuid)
        if resp.status_code == 400:
            error_msg = 'task_id invalid.'
            return api_error(status.HTTP_400_BAD_REQUEST, error_msg)
        if not resp.ok:
            logger.error(resp.content)
            return api_error(status.HTTP_500_INTERNAL_SERVER_ERROR, 'Internal Server Error')
        if task_type == 'import':
            try:
                dtable = DTables.objects.get(uuid=dtable_uuid)
                dtable.delete()
            except Exception:
                pass
        tmp_dir = os.path.join('/tmp/dtable-io', dtable_uuid)
        if os.path.exists(tmp_dir):
            shutil.rmtree(tmp_dir)
        return Response({'success': True})


Prompt:
Implement the Python class `ToonBinarySensor` described below.

Class description:
Defines an Toon binary sensor.

Method signatures and docstrings:
- def __init__(self, coordinator: ToonDataUpdateCoordinator, description: ToonBinarySensorEntityDescription) -> None: Initialize the Toon sensor.
- def is_on(self) -> bool | None: Return the status of the binary sensor.

Implement the Python class `ToonBinarySensor` described below.

Class description:
Defines an Toon binary sensor.

Method signatures and docstrings:
- def __init__(self, coordinator: ToonDataUpdateCoordinator, description: ToonBinarySensorEntityDescription) -> None: Initialize the Toon sensor.
- def is_on(self) -> bool | None: Return the status of the binary sensor.

<|skeleton|>
class ToonBinarySensor:
    """Defines an Toon binary sensor."""

    def __init__(self, coordinator: ToonDataUpdateCoordinator, description: ToonBinarySensorEntityDescription) -> None:
        """Initialize the Toon sensor."""
        <|body_0|>

    def is_on(self) -> bool | None:
        """Return the status of the binary sensor."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(coordinator)
        self.entity_description = description
        self._attr_unique_id = f'{DOMAIN}_{coordinator.data.agreement.agreement_id}_binary_sensor_{description.key}'
<|end_body_0|>

<|body_start_1|>
        section = getattr(self.coordinator.data, self.entity_description.section)
        value = getattr(section, self.entity_description.measurement)
        if value is None:
            return None
        if self.entity_description.inverted:
            return not value
        return value
<|end_body_1|>

Solution:
class ToonBinarySensor:
    """Defines an Toon binary sensor."""

    def __init__(self, coordinator: ToonDataUpdateCoordinator, description: ToonBinarySensorEntityDescription) -> None:
        """Initialize the Toon sensor."""
        super().__init__(coordinator)
        self.entity_description = description
        self._attr_unique_id = f'{DOMAIN}_{coordinator.data.agreement.agreement_id}_binary_sensor_{description.key}'

    def is_on(self) -> bool | None:
        """Return the status of the binary sensor."""
        section = getattr(self.coordinator.data, self.entity_description.section)
        value = getattr(section, self.entity_description.measurement)
        if value is None:
            return None
        if self.entity_description.inverted:
            return not value
        return value


dfs

Prompt:
Implement the Python class `SolutionError` described below.

Class description:
Implement the SolutionError class.

Method signatures and docstrings:
- def canIWin(self, maxChoosableInteger, desiredTotal): :type maxChoosableInteger: int :type desiredTotal: int :rtype: bool
- def dfs(self, choose, target): :type choose: int :type target: int :rtype: int

Implement the Python class `SolutionError` described below.

Class description:
Implement the SolutionError class.

Method signatures and docstrings:
- def canIWin(self, maxChoosableInteger, desiredTotal): :type maxChoosableInteger: int :type desiredTotal: int :rtype: bool
- def dfs(self, choose, target): :type choose: int :type target: int :rtype: int

<|skeleton|>
class SolutionError:

    def canIWin(self, maxChoosableInteger, desiredTotal):
        """:type maxChoosableInteger: int :type desiredTotal: int :rtype: bool"""
        <|body_0|>

    def dfs(self, choose, target):
        """:type choose: int :type target: int :rtype: int"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.matrix = dict()
        self.max_num = maxChoosableInteger
        if (maxChoosableInteger + 1) * maxChoosableInteger / 2 <= desiredTotal:
            return False
        if maxChoosableInteger >= desiredTotal:
            return True
        always_win = False
        for i in range(1, maxChoosableInteger + 1):
            result = self.dfs(1 << i, desiredTotal - i)
            if result != 1:
                always_win = True
        return always_win
<|end_body_0|>

<|body_start_1|>
        return_data = 0
        for i in range(1, self.max_num + 1)[::-1]:
            if choose ^ 2 ** i < choose:
                continue
            next_choose = choose ^ 2 ** i
            if next_choose in self.matrix:
                return self.matrix[next_choose]
            if i >= target:
                self.matrix[choose] = 1
                return 1
            else:
                next_target = target - i
                result = self.dfs(next_choose, next_target)
                if result != 1:
                    return_data = 1
                    break
        self.matrix[choose] = return_data
        return return_data
<|end_body_1|>

Solution:
class SolutionError:
    def canIWin(self, maxChoosableInteger, desiredTotal):
        """:type maxChoosableInteger: int :type desiredTotal: int :rtype: bool"""
        self.matrix = dict()
        self.max_num = maxChoosableInteger
        if (maxChoosableInteger + 1) * maxChoosableInteger / 2 <= desiredTotal:
            return False
        if maxChoosableInteger >= desiredTotal:
            return True
        always_win = False
        for i in range(1, maxChoosableInteger + 1):
            result = self.dfs(1 << i, desiredTotal - i)
            if result != 1:
                always_win = True
        return always_win

    def dfs(self, choose, target):
        """:type choose: int :type target: int :rtype: int"""
        return_data = 0
        for i in range(1, self.max_num + 1)[::-1]:
            if choose ^ 2 ** i < choose:
                continue
            next_choose = choose ^ 2 ** i
            if next_choose in self.matrix:
                return self.matrix[next_choose]
            if i >= target:
                self.matrix[choose] = 1
                return 1
            else:
                next_target = target - i
                result = self.dfs(next_choose, next_target)
                if result != 1:
                    return_data = 1
                    break
        self.matrix[choose] = return_data
        return return_data


Prompt:
Implement the Python class `TransformerRegistry` described below.

Class description:
Registry for all of the block structure transformers that have been made available. All block structure transformers should implement `BlockStructureTransformer`.

Method signatures and docstrings:
- def get_registered_transformers(cls): Returns a set of all registered transformers. Returns: {BlockStructureTransformer} - All transformers that are registered with the platform's PluginManager.
- def get_write_version_hash(cls): Returns a deterministic hash value of the WRITE_VERSION of all registered transformers.
- def find_unregistered(cls, transformers): Find and returns the names of all the transformers from the given list that aren't registered with the platform's PluginManager. Arguments: transformers ([BlockStructureTransformer] - List of transformers to check in the registry. Returns: set([string]) - Set of names of a subset of the given transformers that weren't found in the registry.

Implement the Python class `TransformerRegistry` described below.

Class description:
Registry for all of the block structure transformers that have been made available. All block structure transformers should implement `BlockStructureTransformer`.

Method signatures and docstrings:
- def get_registered_transformers(cls): Returns a set of all registered transformers. Returns: {BlockStructureTransformer} - All transformers that are registered with the platform's PluginManager.
- def get_write_version_hash(cls): Returns a deterministic hash value of the WRITE_VERSION of all registered transformers.
- def find_unregistered(cls, transformers): Find and returns the names of all the transformers from the given list that aren't registered with the platform's PluginManager. Arguments: transformers ([BlockStructureTransformer] - List of transformers to check in the registry. Returns: set([string]) - Set of names of a subset of the given transformers that weren't found in the registry.

<|skeleton|>
class TransformerRegistry:
    """Registry for all of the block structure transformers that have been made available. All block structure transformers should implement `BlockStructureTransformer`."""

    def get_registered_transformers(cls):
        """Returns a set of all registered transformers. Returns: {BlockStructureTransformer} - All transformers that are registered with the platform's PluginManager."""
        <|body_0|>

    def get_write_version_hash(cls):
        """Returns a deterministic hash value of the WRITE_VERSION of all registered transformers."""
        <|body_1|>

    def find_unregistered(cls, transformers):
        """Find and returns the names of all the transformers from the given list that aren't registered with the platform's PluginManager. Arguments: transformers ([BlockStructureTransformer] - List of transformers to check in the registry. Returns: set([string]) - Set of names of a subset of the given transformers that weren't found in the registry."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if cls.USE_PLUGIN_MANAGER:
            return set(cls.get_available_plugins().values())
        else:
            return set()
<|end_body_0|>

<|body_start_1|>
        hash_obj = sha1()
        sorted_transformers = sorted(cls.get_registered_transformers(), key=lambda t: t.name())
        for transformer in sorted_transformers:
            hash_obj.update(transformer.name().encode())
            hash_obj.update(str(transformer.WRITE_VERSION).encode())
        return b64encode(hash_obj.digest()).decode('utf-8')
<|end_body_1|>

<|body_start_2|>
        registered_transformer_names = {reg_trans.name() for reg_trans in cls.get_registered_transformers()}
        requested_transformer_names = {transformer.name() for transformer in transformers}
        return requested_transformer_names - registered_transformer_names
<|end_body_2|>

Solution:
class TransformerRegistry:
    """Registry for all of the block structure transformers that have been made available. All block structure transformers should implement `BlockStructureTransformer`."""

    def get_registered_transformers(cls):
        """Returns a set of all registered transformers. Returns: {BlockStructureTransformer} - All transformers that are registered with the platform's PluginManager."""
        if cls.USE_PLUGIN_MANAGER:
            return set(cls.get_available_plugins().values())
        else:
            return set()

    def get_write_version_hash(cls):
        """Returns a deterministic hash value of the WRITE_VERSION of all registered transformers."""
        hash_obj = sha1()
        sorted_transformers = sorted(cls.get_registered_transformers(), key=lambda t: t.name())
        for transformer in sorted_transformers:
            hash_obj.update(transformer.name().encode())
            hash_obj.update(str(transformer.WRITE_VERSION).encode())
        return b64encode(hash_obj.digest()).decode('utf-8')

    def find_unregistered(cls, transformers):
        """Find and returns the names of all the transformers from the given list that aren't registered with the platform's PluginManager. Arguments: transformers ([BlockStructureTransformer] - List of transformers to check in the registry. Returns: set([string]) - Set of names of a subset of the given transformers that weren't found in the registry."""
        registered_transformer_names = {reg_trans.name() for reg_trans in cls.get_registered_transformers()}
        requested_transformer_names = {transformer.name() for transformer in transformers}
        return requested_transformer_names - registered_transformer_names


Prompt:
Implement the Python class `SummaryVariantFactory` described below.

Class description:
Factory for summary variants.

Method signatures and docstrings:
- def summary_allele_from_record(record: dict[str, Any], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryAllele: Build a summary allele from a dictionary (record).
- def summary_variant_from_records(records: list[dict[str, Any]], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryVariant: Build summary variant from a list of dictionaries (records).
- def summary_variant_from_vcf(vcf_variant: pysam.VariantRecord, summary_variant_index: int, transmission_type: TransmissionType) -> SummaryVariant: Build sumamry variant from a pysam VCF record.

Implement the Python class `SummaryVariantFactory` described below.

Class description:
Factory for summary variants.

Method signatures and docstrings:
- def summary_allele_from_record(record: dict[str, Any], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryAllele: Build a summary allele from a dictionary (record).
- def summary_variant_from_records(records: list[dict[str, Any]], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryVariant: Build summary variant from a list of dictionaries (records).
- def summary_variant_from_vcf(vcf_variant: pysam.VariantRecord, summary_variant_index: int, transmission_type: TransmissionType) -> SummaryVariant: Build sumamry variant from a pysam VCF record.

<|skeleton|>
class SummaryVariantFactory:
    """Factory for summary variants."""

    def summary_allele_from_record(record: dict[str, Any], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryAllele:
        """Build a summary allele from a dictionary (record)."""
        <|body_0|>

    def summary_variant_from_records(records: list[dict[str, Any]], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryVariant:
        """Build summary variant from a list of dictionaries (records)."""
        <|body_1|>

    def summary_variant_from_vcf(vcf_variant: pysam.VariantRecord, summary_variant_index: int, transmission_type: TransmissionType) -> SummaryVariant:
        """Build sumamry variant from a pysam VCF record."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if transmission_type is not None:
            record['transmission_type'] = transmission_type
        alternative = record['alternative']
        attributes = record
        if attr_filter:
            attributes = {k: v for k, v in record.items() if k not in attr_filter}
        if 'summary_variant_index' in record:
            summary_index = record['summary_variant_index']
        else:
            summary_index = record.get('summary_index')
        allele_index = record['allele_index']
        chrom = record['chrom']
        position = record['position']
        end_position = record.get('end_position')
        reference = record['reference']
        alternative = record.get('alternative')
        allele_type = record.get('variant_type', None)
        transmission_type = TransmissionType(record.get('transmission_type', TransmissionType.transmitted))
        if position is not None and end_position is not None and (reference is None) and (alternative is None) and (allele_type is None):
            allele_type = SummaryAllele.Type.position
        if allele_type is not None:
            allele_type = core.Allele.Type(allele_type)
        return SummaryAllele(chrom, position, reference, alternative=alternative, summary_index=summary_index, end_position=record.get('end_position', None), allele_type=allele_type, allele_index=allele_index, transmission_type=transmission_type, attributes=attributes)
<|end_body_0|>

<|body_start_1|>
        assert len(records) > 0
        alleles = []
        for record in records:
            allele = SummaryVariantFactory.summary_allele_from_record(record, transmission_type=transmission_type, attr_filter=attr_filter)
            alleles.append(allele)
        if not alleles[0].is_reference_allele:
            ref_allele = SummaryAllele.create_reference_allele(alleles[0])
            alleles.insert(0, ref_allele)
        allele_count = {'allele_count': len(alleles)}
        for allele in alleles:
            allele.update_attributes(allele_count)
        return SummaryVariant(alleles)
<|end_body_1|>

<|body_start_2|>
        records = []
        alts = vcf_variant.alts if vcf_variant.alts is not None else ['.']
        allele_count = len(alts) + 1
        records.append({'chrom': vcf_variant.chrom, 'position': vcf_variant.pos, 'reference': vcf_variant.ref, 'alternative': None, 'summary_variant_index': summary_variant_index, 'allele_index': 0, 'allele_count': allele_count})
        for allele_index, alt in enumerate(alts):
            records.append({'chrom': vcf_variant.chrom, 'position': vcf_variant.pos, 'reference': vcf_variant.ref, 'alternative': alt, 'summary_variant_index': summary_variant_index, 'allele_index': allele_index + 1, 'allele_count': allele_count})
        return SummaryVariantFactory.summary_variant_from_records(records, transmission_type=transmission_type)
<|end_body_2|>

Solution:
class SummaryVariantFactory:
    """Factory for summary variants."""

    def summary_allele_from_record(record: dict[str, Any], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryAllele:
        """Build a summary allele from a dictionary (record)."""
        if transmission_type is not None:
            record['transmission_type'] = transmission_type
        alternative = record['alternative']
        attributes = record
        if attr_filter:
            attributes = {k: v for k, v in record.items() if k not in attr_filter}
        if 'summary_variant_index' in record:
            summary_index = record['summary_variant_index']
        else:
            summary_index = record.get('summary_index')
        allele_index = record['allele_index']
        chrom = record['chrom']
        position = record['position']
        end_position = record.get('end_position')
        reference = record['reference']
        alternative = record.get('alternative')
        allele_type = record.get('variant_type', None)
        transmission_type = TransmissionType(record.get('transmission_type', TransmissionType.transmitted))
        if position is not None and end_position is not None and (reference is None) and (alternative is None) and (allele_type is None):
            allele_type = SummaryAllele.Type.position
        if allele_type is not None:
            allele_type = core.Allele.Type(allele_type)
        return SummaryAllele(chrom, position, reference, alternative=alternative, summary_index=summary_index, end_position=record.get('end_position', None), allele_type=allele_type, allele_index=allele_index, transmission_type=transmission_type, attributes=attributes)

    def summary_variant_from_records(records: list[dict[str, Any]], transmission_type: Optional[TransmissionType]=None, attr_filter: Optional[set[str]]=None) -> SummaryVariant:
        """Build summary variant from a list of dictionaries (records)."""
        assert len(records) > 0
        alleles = []
        for record in records:
            allele = SummaryVariantFactory.summary_allele_from_record(record, transmission_type=transmission_type, attr_filter=attr_filter)
            alleles.append(allele)
        if not alleles[0].is_reference_allele:
            ref_allele = SummaryAllele.create_reference_allele(alleles[0])
            alleles.insert(0, ref_allele)
        allele_count = {'allele_count': len(alleles)}
        for allele in alleles:
            allele.update_attributes(allele_count)
        return SummaryVariant(alleles)

    def summary_variant_from_vcf(vcf_variant: pysam.VariantRecord, summary_variant_index: int, transmission_type: TransmissionType) -> SummaryVariant:
        """Build sumamry variant from a pysam VCF record."""
        records = []
        alts = vcf_variant.alts if vcf_variant.alts is not None else ['.']
        allele_count = len(alts) + 1
        records.append({'chrom': vcf_variant.chrom, 'position': vcf_variant.pos, 'reference': vcf_variant.ref, 'alternative': None, 'summary_variant_index': summary_variant_index, 'allele_index': 0, 'allele_count': allele_count})
        for allele_index, alt in enumerate(alts):
            records.append({'chrom': vcf_variant.chrom, 'position': vcf_variant.pos, 'reference': vcf_variant.ref, 'alternative': alt, 'summary_variant_index': summary_variant_index, 'allele_index': allele_index + 1, 'allele_count': allele_count})
        return SummaryVariantFactory.summary_variant_from_records(records, transmission_type=transmission_type)


生成所有要抓取的URL地址,交给调度器入队列

Prompt:
Implement the Python class `DoubanSpider` described below.

Class description:
Implement the DoubanSpider class.

Method signatures and docstrings:
- def start_requests(self): 生成所有要抓取的URL地址,交给调度器入队列
- def parse(self, response): 解析提取数据

Implement the Python class `DoubanSpider` described below.

Class description:
Implement the DoubanSpider class.

Method signatures and docstrings:
- def start_requests(self): 生成所有要抓取的URL地址,交给调度器入队列
- def parse(self, response): 解析提取数据

<|skeleton|>
class DoubanSpider:

    def start_requests(self):
        """生成所有要抓取的URL地址,交给调度器入队列"""
        <|body_0|>

    def parse(self, response):
        """解析提取数据"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        for start in range(0, 101, 20):
            page_url = self.url.format(start)
            yield scrapy.Request(url=page_url, callback=self.parse)
<|end_body_0|>

<|body_start_1|>
        html = json.loads(response.text)
        for one_dict in html:
            item = DoubanItem()
            item['rank'] = one_dict['rank']
            item['title'] = one_dict['title']
            item['score'] = one_dict['score']
            yield item
<|end_body_1|>

Solution:
class DoubanSpider:
    def start_requests(self):
        """生成所有要抓取的URL地址,交给调度器入队列"""
        for start in range(0, 101, 20):
            page_url = self.url.format(start)
            yield scrapy.Request(url=page_url, callback=self.parse)

    def parse(self, response):
        """解析提取数据"""
        html = json.loads(response.text)
        for one_dict in html:
            item = DoubanItem()
            item['rank'] = one_dict['rank']
            item['title'] = one_dict['title']
            item['score'] = one_dict['score']
            yield item


Prompt:
Implement the Python class `Experiment` described below.

Class description:
Implement the Experiment class.

Method signatures and docstrings:
- def __init__(self, name, cmd, param_generator=(), env_vars=None): :param cmd: base command to append the parameters to :param param_generator: iterable of parameter dicts
- def generate_experiments(self, experiment_arg_name, customize_experiment_name, param_prefix): Yields tuples of (cmd, experiment_name)

Implement the Python class `Experiment` described below.

Class description:
Implement the Experiment class.

Method signatures and docstrings:
- def __init__(self, name, cmd, param_generator=(), env_vars=None): :param cmd: base command to append the parameters to :param param_generator: iterable of parameter dicts
- def generate_experiments(self, experiment_arg_name, customize_experiment_name, param_prefix): Yields tuples of (cmd, experiment_name)

<|skeleton|>
class Experiment:

    def __init__(self, name, cmd, param_generator=(), env_vars=None):
        """:param cmd: base command to append the parameters to :param param_generator: iterable of parameter dicts"""
        <|body_0|>

    def generate_experiments(self, experiment_arg_name, customize_experiment_name, param_prefix):
        """Yields tuples of (cmd, experiment_name)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.base_name = name
        self.cmd = cmd
        self.params = list(param_generator)
        self.env_vars = env_vars
<|end_body_0|>

<|body_start_1|>
        num_experiments = 1 if len(self.params) == 0 else len(self.params)
        for experiment_idx in range(num_experiments):
            cmd_tokens = [self.cmd]
            experiment_name_tokens = [self.base_name]
            param_shorthands = []
            if len(self.params) > 0:
                params = self.params[experiment_idx]
                for param, value in params.items():
                    param_str = f'{param_prefix}{param}={value}'
                    cmd_tokens.append(param_str)
                    param_tokens = re.split('[._-]', param)
                    shorthand_tokens = [t[0] for t in param_tokens[:-1]]
                    last_token_l = min(3, len(param_tokens[-1]))
                    shorthand = '.'.join(shorthand_tokens + [param_tokens[-1][:last_token_l]])
                    while last_token_l <= len(param_tokens[-1]) and shorthand in param_shorthands:
                        last_token_l += 1
                        shorthand = '.'.join(shorthand_tokens + [param_tokens[-1][:last_token_l]])
                    param_shorthands.append(shorthand)
                    experiment_name_token = f'{shorthand}_{value}'
                    experiment_name_tokens.append(experiment_name_token)
            if customize_experiment_name:
                experiment_name = f'{experiment_idx:02d}_' + '_'.join(experiment_name_tokens)
                if len(experiment_name) > 100:
                    log.warning('Experiment name is extra long! (%d characters)', len(experiment_name))
            else:
                experiment_name = f'{experiment_idx:02d}_{self.base_name}'
            cmd_tokens.append(f'{experiment_arg_name}={experiment_name}')
            param_str = ' '.join(cmd_tokens)
            yield (param_str, experiment_name)
<|end_body_1|>

Solution:
class Experiment:
    def __init__(self, name, cmd, param_generator=(), env_vars=None):
        """:param cmd: base command to append the parameters to :param param_generator: iterable of parameter dicts"""
        self.base_name = name
        self.cmd = cmd
        self.params = list(param_generator)
        self.env_vars = env_vars

    def generate_experiments(self, experiment_arg_name, customize_experiment_name, param_prefix):
        """Yields tuples of (cmd, experiment_name)"""
        num_experiments = 1 if len(self.params) == 0 else len(self.params)
        for experiment_idx in range(num_experiments):
            cmd_tokens = [self.cmd]
            experiment_name_tokens = [self.base_name]
            param_shorthands = []
            if len(self.params) > 0:
                params = self.params[experiment_idx]
                for param, value in params.items():
                    param_str = f'{param_prefix}{param}={value}'
                    cmd_tokens.append(param_str)
                    param_tokens = re.split('[._-]', param)
                    shorthand_tokens = [t[0] for t in param_tokens[:-1]]
                    last_token_l = min(3, len(param_tokens[-1]))
                    shorthand = '.'.join(shorthand_tokens + [param_tokens[-1][:last_token_l]])
                    while last_token_l <= len(param_tokens[-1]) and shorthand in param_shorthands:
                        last_token_l += 1
                        shorthand = '.'.join(shorthand_tokens + [param_tokens[-1][:last_token_l]])
                    param_shorthands.append(shorthand)
                    experiment_name_token = f'{shorthand}_{value}'
                    experiment_name_tokens.append(experiment_name_token)
            if customize_experiment_name:
                experiment_name = f'{experiment_idx:02d}_' + '_'.join(experiment_name_tokens)
                if len(experiment_name) > 100:
                    log.warning('Experiment name is extra long! (%d characters)', len(experiment_name))
            else:
                experiment_name = f'{experiment_idx:02d}_{self.base_name}'
            cmd_tokens.append(f'{experiment_arg_name}={experiment_name}')
            param_str = ' '.join(cmd_tokens)
            yield (param_str, experiment_name)