encode

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

Class description:
Implement the Encoder class.

Method signatures and docstrings:
- def __init__(self, unique_values, is_categorical): Constructor of an Encoder using one-hot-encoding
- def __get_stdev_band(self, unique_values): Get the lower bound and upper bound for the standard devaitation band for continuous value.
- def __normalize(self, value, lower_bound, upper_bound): Normalize the value to the lower bound and upper bound by the max & min
- def encode(self, value): Get one-hot encoding for a value based on the unique values in this encoder. Return a list of 0s except 1 at the index that matches the unique value index.

Implement the Python class `Encoder` described below.

Class description:
Implement the Encoder class.

Method signatures and docstrings:
- def __init__(self, unique_values, is_categorical): Constructor of an Encoder using one-hot-encoding
- def __get_stdev_band(self, unique_values): Get the lower bound and upper bound for the standard devaitation band for continuous value.
- def __normalize(self, value, lower_bound, upper_bound): Normalize the value to the lower bound and upper bound by the max & min
- def encode(self, value): Get one-hot encoding for a value based on the unique values in this encoder. Return a list of 0s except 1 at the index that matches the unique value index.

<|skeleton|>
class Encoder:

    def __init__(self, unique_values, is_categorical):
        """Constructor of an Encoder using one-hot-encoding"""
        <|body_0|>

    def __get_stdev_band(self, unique_values):
        """Get the lower bound and upper bound for the standard devaitation band for continuous value."""
        <|body_1|>

    def __normalize(self, value, lower_bound, upper_bound):
        """Normalize the value to the lower bound and upper bound by the max & min"""
        <|body_2|>

    def encode(self, value):
        """Get one-hot encoding for a value based on the unique values in this encoder. Return a list of 0s except 1 at the index that matches the unique value index."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)
<|end_body_0|>

<|body_start_1|>
        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]
<|end_body_1|>

<|body_start_2|>
        min_max_diff = self.max - self.min
        bound_diff = upper_bound - lower_bound
        return (value - self.min) / min_max_diff * bound_diff + lower_bound
<|end_body_2|>

<|body_start_3|>
        encoded = []
        if self.is_categorical:
            if self.is_binary:
                encoded.append(0 if value == self.unique_values[0] else 1)
            else:
                for index in range(len(self.unique_values)):
                    unique = self.unique_values[index]
                    encoded.append(1 if value == unique else 0)
        else:
            normalized = self.__normalize(value, -1, 1)
            encoded.append(normalized)
        return encoded
<|end_body_3|>

Solution:
class Encoder:
    def __init__(self, unique_values, is_categorical):
        """Constructor of an Encoder using one-hot-encoding"""
        self.is_categorical = is_categorical
        self.is_binary = len(unique_values) == 2
        self.unique_values = unique_values
        self.min = min(unique_values)
        self.max = max(unique_values)

    def __get_stdev_band(self, unique_values):
        """Get the lower bound and upper bound for the standard devaitation band for continuous value."""
        mean = stats.mean(unique_values)
        stdev = stats.stdev(unique_values)
        return [mean - stdev, mean + stdev]

    def __normalize(self, value, lower_bound, upper_bound):
        """Normalize the value to the lower bound and upper bound by the max & min"""
        min_max_diff = self.max - self.min
        bound_diff = upper_bound - lower_bound
        return (value - self.min) / min_max_diff * bound_diff + lower_bound

    def encode(self, value):
        """Get one-hot encoding for a value based on the unique values in this encoder. Return a list of 0s except 1 at the index that matches the unique value index."""
        encoded = []
        if self.is_categorical:
            if self.is_binary:
                encoded.append(0 if value == self.unique_values[0] else 1)
            else:
                for index in range(len(self.unique_values)):
                    unique = self.unique_values[index]
                    encoded.append(1 if value == unique else 0)
        else:
            normalized = self.__normalize(value, -1, 1)
            encoded.append(normalized)
        return encoded


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

Class description:
活动设置

Method signatures and docstrings:
- def test_active_good(self): 活动好处
- def test_regular_desc(self): 活动规则

Implement the Python class `TestActivity` described below.

Class description:
活动设置

Method signatures and docstrings:
- def test_active_good(self): 活动好处
- def test_regular_desc(self): 活动规则

<|skeleton|>
class TestActivity:
    """活动设置"""

    def test_active_good(self):
        """活动好处"""
        <|body_0|>

    def test_regular_desc(self):
        """活动规则"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        login_page.LoginPage(self.driver).login()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).Iamlandlord()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).close_weiChat()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).activitymanager()
        sleep(1)
        landlord_nav_page.LandlordNavPage(self.driver).close_weiChat()
        po = landlord_activity_page.LandlordActivity(self.driver)
        print(po.text())
        sleep(2)
        po.active_good()
        sleep(2)
        function.insert_img(self.driver, 'activity_good.png')
        po.img_close()
<|end_body_0|>

<|body_start_1|>
        landlord_nav_page.LandlordNavPage(self.driver).Iamlandlord()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).activitymanager()
        sleep(1)
        po = landlord_activity_page.LandlordActivity(self.driver)
        sleep(2)
        po.regular_desc()
        function.insert_img(self.driver, 'regular_desc.png')
        print(po.regular_desc_text())
        po.regular_desc_close()
<|end_body_1|>

Solution:
class TestActivity:
    """活动设置"""

    def test_active_good(self):
        """活动好处"""
        login_page.LoginPage(self.driver).login()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).Iamlandlord()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).close_weiChat()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).activitymanager()
        sleep(1)
        landlord_nav_page.LandlordNavPage(self.driver).close_weiChat()
        po = landlord_activity_page.LandlordActivity(self.driver)
        print(po.text())
        sleep(2)
        po.active_good()
        sleep(2)
        function.insert_img(self.driver, 'activity_good.png')
        po.img_close()

    def test_regular_desc(self):
        """活动规则"""
        landlord_nav_page.LandlordNavPage(self.driver).Iamlandlord()
        sleep(2)
        landlord_nav_page.LandlordNavPage(self.driver).activitymanager()
        sleep(1)
        po = landlord_activity_page.LandlordActivity(self.driver)
        sleep(2)
        po.regular_desc()
        function.insert_img(self.driver, 'regular_desc.png')
        print(po.regular_desc_text())
        po.regular_desc_close()


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

Class description:
command-line options

Method signatures and docstrings:
- def add_arguments(self, parser): specify which function to run
- def handle(self, *args, **options): execute init

Implement the Python class `Command` described below.

Class description:
command-line options

Method signatures and docstrings:
- def add_arguments(self, parser): specify which function to run
- def handle(self, *args, **options): execute init

<|skeleton|>
class Command:
    """command-line options"""

    def add_arguments(self, parser):
        """specify which function to run"""
        <|body_0|>

    def handle(self, *args, **options):
        """execute init"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        parser.add_argument('--current', action='store_true', help='Version stored in database')
        parser.add_argument('--target', action='store_true', help='Version stored in settings')
        parser.add_argument('--update', action='store_true', help='Update database version')
<|end_body_0|>

<|body_start_1|>
        site = models.SiteSettings.objects.get()
        current = site.version or '0.0.1'
        target = VERSION
        if options.get('current'):
            print(current)
            return
        if options.get('target'):
            print(target)
            return
        if options.get('update'):
            site.version = target
            site.save()
            return
        if current != target:
            print(f'{current}/{target}')
        else:
            print(current)
<|end_body_1|>

Solution:
class Command:
    """command-line options"""

    def add_arguments(self, parser):
        """specify which function to run"""
        parser.add_argument('--current', action='store_true', help='Version stored in database')
        parser.add_argument('--target', action='store_true', help='Version stored in settings')
        parser.add_argument('--update', action='store_true', help='Update database version')

    def handle(self, *args, **options):
        """execute init"""
        site = models.SiteSettings.objects.get()
        current = site.version or '0.0.1'
        target = VERSION
        if options.get('current'):
            print(current)
            return
        if options.get('target'):
            print(target)
            return
        if options.get('update'):
            site.version = target
            site.save()
            return
        if current != target:
            print(f'{current}/{target}')
        else:
            print(current)


put

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

Class description:
Resource for managing org login options.

Method signatures and docstrings:
- def get(org_id): Retrieve the set of payment settings associated with the specified org.
- def post(org_id): Create a new login type for the specified org.
- def put(org_id): Update a new login type for the specified org.

Implement the Python class `OrgLoginOptions` described below.

Class description:
Resource for managing org login options.

Method signatures and docstrings:
- def get(org_id): Retrieve the set of payment settings associated with the specified org.
- def post(org_id): Create a new login type for the specified org.
- def put(org_id): Update a new login type for the specified org.

<|skeleton|>
class OrgLoginOptions:
    """Resource for managing org login options."""

    def get(org_id):
        """Retrieve the set of payment settings associated with the specified org."""
        <|body_0|>

    def post(org_id):
        """Create a new login type for the specified org."""
        <|body_1|>

    def put(org_id):
        """Update a new login type for the specified org."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        try:
            login_options = OrgService.get_login_options_for_org(org_id, allowed_roles=ALL_ALLOWED_ROLES)
            response, status = (jsonify({'loginOption': login_options.login_source if login_options else None}), http_status.HTTP_200_OK)
        except BusinessException as exception:
            response, status = ({'code': exception.code, 'message': exception.message}, exception.status_code)
        return (response, status)
<|end_body_0|>

<|body_start_1|>
        request_json = request.get_json()
        login_option_val = request_json.get('loginOption')
        try:
            login_option = OrgService.add_login_option(org_id, login_option_val)
            response, status = (jsonify({'login_option': login_option.login_source}), http_status.HTTP_201_CREATED)
        except BusinessException as exception:
            response, status = ({'code': exception.code, 'message': exception.message}, exception.status_code)
        return (response, status)
<|end_body_1|>

<|body_start_2|>
        request_json = request.get_json()
        login_option_val = request_json.get('loginOption')
        try:
            login_option = OrgService.update_login_option(org_id, login_option_val)
            response, status = (jsonify({'login_option': login_option.login_source}), http_status.HTTP_201_CREATED)
        except BusinessException as exception:
            response, status = ({'code': exception.code, 'message': exception.message}, exception.status_code)
        return (response, status)
<|end_body_2|>

Solution:
class OrgLoginOptions:
    """Resource for managing org login options."""

    def get(org_id):
        """Retrieve the set of payment settings associated with the specified org."""
        try:
            login_options = OrgService.get_login_options_for_org(org_id, allowed_roles=ALL_ALLOWED_ROLES)
            response, status = (jsonify({'loginOption': login_options.login_source if login_options else None}), http_status.HTTP_200_OK)
        except BusinessException as exception:
            response, status = ({'code': exception.code, 'message': exception.message}, exception.status_code)
        return (response, status)

    def post(org_id):
        """Create a new login type for the specified org."""
        request_json = request.get_json()
        login_option_val = request_json.get('loginOption')
        try:
            login_option = OrgService.add_login_option(org_id, login_option_val)
            response, status = (jsonify({'login_option': login_option.login_source}), http_status.HTTP_201_CREATED)
        except BusinessException as exception:
            response, status = ({'code': exception.code, 'message': exception.message}, exception.status_code)
        return (response, status)

    def put(org_id):
        """Update a new login type for the specified org."""
        request_json = request.get_json()
        login_option_val = request_json.get('loginOption')
        try:
            login_option = OrgService.update_login_option(org_id, login_option_val)
            response, status = (jsonify({'login_option': login_option.login_source}), http_status.HTTP_201_CREATED)
        except BusinessException as exception:
            response, status = ({'code': exception.code, 'message': exception.message}, exception.status_code)
        return (response, status)


backward

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

Class description:
Group feature with given index.

Method signatures and docstrings:
- def forward(ctx, features: torch.Tensor, indices: torch.Tensor, features_batch_cnt: Optional[torch.Tensor]=None, indices_batch_cnt: Optional[torch.Tensor]=None) -> torch.Tensor: Args: features (Tensor): Tensor of features to group, input shape is (B, C, N) or stacked inputs (N1 + N2 ..., C). indices (Tensor): The indices of features to group with, input shape is (B, npoint, nsample) or stacked inputs (M1 + M2 ..., nsample). features_batch_cnt (Tensor, optional): Input features nums in each batch, just like (N1, N2, ...). Defaults to None. New in version 1.7.0. indices_batch_cnt (Tensor, optional): Input indices nums in each batch, just like (M1, M2, ...). Defaults to None. New in version 1.7.0. Returns: Tensor: Grouped features, the shape is (B, C, npoint, nsample) or (M1 + M2 ..., C, nsample).
- def backward(ctx, grad_out: torch.Tensor) -> Tuple: Args: grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients of the output from forward. Returns: Tensor: (B, C, N) gradient of the features.

Implement the Python class `GroupingOperation` described below.

Class description:
Group feature with given index.

Method signatures and docstrings:
- def forward(ctx, features: torch.Tensor, indices: torch.Tensor, features_batch_cnt: Optional[torch.Tensor]=None, indices_batch_cnt: Optional[torch.Tensor]=None) -> torch.Tensor: Args: features (Tensor): Tensor of features to group, input shape is (B, C, N) or stacked inputs (N1 + N2 ..., C). indices (Tensor): The indices of features to group with, input shape is (B, npoint, nsample) or stacked inputs (M1 + M2 ..., nsample). features_batch_cnt (Tensor, optional): Input features nums in each batch, just like (N1, N2, ...). Defaults to None. New in version 1.7.0. indices_batch_cnt (Tensor, optional): Input indices nums in each batch, just like (M1, M2, ...). Defaults to None. New in version 1.7.0. Returns: Tensor: Grouped features, the shape is (B, C, npoint, nsample) or (M1 + M2 ..., C, nsample).
- def backward(ctx, grad_out: torch.Tensor) -> Tuple: Args: grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients of the output from forward. Returns: Tensor: (B, C, N) gradient of the features.

<|skeleton|>
class GroupingOperation:
    """Group feature with given index."""

    def forward(ctx, features: torch.Tensor, indices: torch.Tensor, features_batch_cnt: Optional[torch.Tensor]=None, indices_batch_cnt: Optional[torch.Tensor]=None) -> torch.Tensor:
        """Args: features (Tensor): Tensor of features to group, input shape is (B, C, N) or stacked inputs (N1 + N2 ..., C). indices (Tensor): The indices of features to group with, input shape is (B, npoint, nsample) or stacked inputs (M1 + M2 ..., nsample). features_batch_cnt (Tensor, optional): Input features nums in each batch, just like (N1, N2, ...). Defaults to None. New in version 1.7.0. indices_batch_cnt (Tensor, optional): Input indices nums in each batch, just like (M1, M2, ...). Defaults to None. New in version 1.7.0. Returns: Tensor: Grouped features, the shape is (B, C, npoint, nsample) or (M1 + M2 ..., C, nsample)."""
        <|body_0|>

    def backward(ctx, grad_out: torch.Tensor) -> Tuple:
        """Args: grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients of the output from forward. Returns: Tensor: (B, C, N) gradient of the features."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        features = features.contiguous()
        indices = indices.contiguous()
        if features_batch_cnt is not None and indices_batch_cnt is not None:
            assert features_batch_cnt.dtype == torch.int
            assert indices_batch_cnt.dtype == torch.int
            M, nsample = indices.size()
            N, C = features.size()
            B = indices_batch_cnt.shape[0]
            output = features.new_zeros((M, C, nsample))
            ext_module.stack_group_points_forward(features, features_batch_cnt, indices, indices_batch_cnt, output, b=B, m=M, c=C, nsample=nsample)
            ctx.for_backwards = (B, N, indices, features_batch_cnt, indices_batch_cnt)
        else:
            B, nfeatures, nsample = indices.size()
            _, C, N = features.size()
            output = features.new_zeros(B, C, nfeatures, nsample)
            ext_module.group_points_forward(features, indices, output, b=B, c=C, n=N, npoints=nfeatures, nsample=nsample)
            ctx.for_backwards = (indices, N)
        return output
<|end_body_0|>

<|body_start_1|>
        if len(ctx.for_backwards) != 5:
            idx, N = ctx.for_backwards
            B, C, npoint, nsample = grad_out.size()
            grad_features = grad_out.new_zeros(B, C, N)
            grad_out_data = grad_out.data.contiguous()
            ext_module.group_points_backward(grad_out_data, idx, grad_features.data, b=B, c=C, n=N, npoints=npoint, nsample=nsample)
            return (grad_features, None)
        else:
            B, N, idx, features_batch_cnt, idx_batch_cnt = ctx.for_backwards
            M, C, nsample = grad_out.size()
            grad_features = grad_out.new_zeros(N, C)
            grad_out_data = grad_out.data.contiguous()
            ext_module.stack_group_points_backward(grad_out_data, idx, idx_batch_cnt, features_batch_cnt, grad_features.data, b=B, c=C, m=M, n=N, nsample=nsample)
            return (grad_features, None, None, None)
<|end_body_1|>

Solution:
class GroupingOperation:
    """Group feature with given index."""

    def forward(ctx, features: torch.Tensor, indices: torch.Tensor, features_batch_cnt: Optional[torch.Tensor]=None, indices_batch_cnt: Optional[torch.Tensor]=None) -> torch.Tensor:
        """Args: features (Tensor): Tensor of features to group, input shape is (B, C, N) or stacked inputs (N1 + N2 ..., C). indices (Tensor): The indices of features to group with, input shape is (B, npoint, nsample) or stacked inputs (M1 + M2 ..., nsample). features_batch_cnt (Tensor, optional): Input features nums in each batch, just like (N1, N2, ...). Defaults to None. New in version 1.7.0. indices_batch_cnt (Tensor, optional): Input indices nums in each batch, just like (M1, M2, ...). Defaults to None. New in version 1.7.0. Returns: Tensor: Grouped features, the shape is (B, C, npoint, nsample) or (M1 + M2 ..., C, nsample)."""
        features = features.contiguous()
        indices = indices.contiguous()
        if features_batch_cnt is not None and indices_batch_cnt is not None:
            assert features_batch_cnt.dtype == torch.int
            assert indices_batch_cnt.dtype == torch.int
            M, nsample = indices.size()
            N, C = features.size()
            B = indices_batch_cnt.shape[0]
            output = features.new_zeros((M, C, nsample))
            ext_module.stack_group_points_forward(features, features_batch_cnt, indices, indices_batch_cnt, output, b=B, m=M, c=C, nsample=nsample)
            ctx.for_backwards = (B, N, indices, features_batch_cnt, indices_batch_cnt)
        else:
            B, nfeatures, nsample = indices.size()
            _, C, N = features.size()
            output = features.new_zeros(B, C, nfeatures, nsample)
            ext_module.group_points_forward(features, indices, output, b=B, c=C, n=N, npoints=nfeatures, nsample=nsample)
            ctx.for_backwards = (indices, N)
        return output

    def backward(ctx, grad_out: torch.Tensor) -> Tuple:
        """Args: grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients of the output from forward. Returns: Tensor: (B, C, N) gradient of the features."""
        if len(ctx.for_backwards) != 5:
            idx, N = ctx.for_backwards
            B, C, npoint, nsample = grad_out.size()
            grad_features = grad_out.new_zeros(B, C, N)
            grad_out_data = grad_out.data.contiguous()
            ext_module.group_points_backward(grad_out_data, idx, grad_features.data, b=B, c=C, n=N, npoints=npoint, nsample=nsample)
            return (grad_features, None)
        else:
            B, N, idx, features_batch_cnt, idx_batch_cnt = ctx.for_backwards
            M, C, nsample = grad_out.size()
            grad_features = grad_out.new_zeros(N, C)
            grad_out_data = grad_out.data.contiguous()
            ext_module.stack_group_points_backward(grad_out_data, idx, idx_batch_cnt, features_batch_cnt, grad_features.data, b=B, c=C, m=M, n=N, nsample=nsample)
            return (grad_features, None, None, None)


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

Class description:
Implement the Gait class.

Method signatures and docstrings:
- def __init__(self, robot, relative_phase, v, a, R, amp_offset, phase_offset): Class containing default components of a gait. Formulation similar to Crespi2008 :param robot: Robot object :param relative_phase: phase difference between base joints :param v: frequency of each CPG - fixed and same right now :param R: amplitude of CPG - fixed and same right now :param a: positive constant :param phase_offset: Phase difference between first-second and second-third joint
- def generate_biases(self, relative_phase): Generates the bias matrix depending on the gait. relative_phase defines the phase difference between the 6 base joints

Implement the Python class `Gait` described below.

Class description:
Implement the Gait class.

Method signatures and docstrings:
- def __init__(self, robot, relative_phase, v, a, R, amp_offset, phase_offset): Class containing default components of a gait. Formulation similar to Crespi2008 :param robot: Robot object :param relative_phase: phase difference between base joints :param v: frequency of each CPG - fixed and same right now :param R: amplitude of CPG - fixed and same right now :param a: positive constant :param phase_offset: Phase difference between first-second and second-third joint
- def generate_biases(self, relative_phase): Generates the bias matrix depending on the gait. relative_phase defines the phase difference between the 6 base joints

<|skeleton|>
class Gait:

    def __init__(self, robot, relative_phase, v, a, R, amp_offset, phase_offset):
        """Class containing default components of a gait. Formulation similar to Crespi2008 :param robot: Robot object :param relative_phase: phase difference between base joints :param v: frequency of each CPG - fixed and same right now :param R: amplitude of CPG - fixed and same right now :param a: positive constant :param phase_offset: Phase difference between first-second and second-third joint"""
        <|body_0|>

    def generate_biases(self, relative_phase):
        """Generates the bias matrix depending on the gait. relative_phase defines the phase difference between the 6 base joints"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.robot = robot
        self.relative_phase = relative_phase
        self.v = v
        self.a = a
        self.R = R
        self.amp_offset = amp_offset
        self.phase_offset = phase_offset
        self.phase_biases = self.generate_biases(relative_phase)
<|end_body_0|>

<|body_start_1|>
        phase_biases = np.zeros((self.robot.n_oscillators, self.robot.n_oscillators))
        for i in range(self.robot.n_legs):
            phase_biases[i][i + self.robot.n_legs] = -self.phase_offset[0][i]
            phase_biases[i][i + 2 * self.robot.n_legs] = -self.phase_offset[1][i]
            phase_biases[i + self.robot.n_legs][i] = self.phase_offset[0][i]
            phase_biases[i + 2 * self.robot.n_legs][i] = self.phase_offset[1][i]
            for j in range(self.robot.n_legs):
                phase_biases[i][j] = relative_phase[i][j]
        return phase_biases
<|end_body_1|>

Solution:
class Gait:
    def __init__(self, robot, relative_phase, v, a, R, amp_offset, phase_offset):
        """Class containing default components of a gait. Formulation similar to Crespi2008 :param robot: Robot object :param relative_phase: phase difference between base joints :param v: frequency of each CPG - fixed and same right now :param R: amplitude of CPG - fixed and same right now :param a: positive constant :param phase_offset: Phase difference between first-second and second-third joint"""
        self.robot = robot
        self.relative_phase = relative_phase
        self.v = v
        self.a = a
        self.R = R
        self.amp_offset = amp_offset
        self.phase_offset = phase_offset
        self.phase_biases = self.generate_biases(relative_phase)

    def generate_biases(self, relative_phase):
        """Generates the bias matrix depending on the gait. relative_phase defines the phase difference between the 6 base joints"""
        phase_biases = np.zeros((self.robot.n_oscillators, self.robot.n_oscillators))
        for i in range(self.robot.n_legs):
            phase_biases[i][i + self.robot.n_legs] = -self.phase_offset[0][i]
            phase_biases[i][i + 2 * self.robot.n_legs] = -self.phase_offset[1][i]
            phase_biases[i + self.robot.n_legs][i] = self.phase_offset[0][i]
            phase_biases[i + 2 * self.robot.n_legs][i] = self.phase_offset[1][i]
            for j in range(self.robot.n_legs):
                phase_biases[i][j] = relative_phase[i][j]
        return phase_biases


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

Class description:
transform object and string

Method signatures and docstrings:
- def pickle_dumps_to_str(cls, obj): from object to str
- def pickle_loads_from_str(cls, obj_str): from str to object

Implement the Python class `ObjectTransform` described below.

Class description:
transform object and string

Method signatures and docstrings:
- def pickle_dumps_to_str(cls, obj): from object to str
- def pickle_loads_from_str(cls, obj_str): from str to object

<|skeleton|>
class ObjectTransform:
    """transform object and string"""

    def pickle_dumps_to_str(cls, obj):
        """from object to str"""
        <|body_0|>

    def pickle_loads_from_str(cls, obj_str):
        """from str to object"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            return base64.b64encode(pickle.dumps(obj)).decode()
        except pickle.PicklingError:
            pass
<|end_body_0|>

<|body_start_1|>
        try:
            return pickle.loads(base64.b64decode(obj_str.encode()))
        except pickle.UnpicklingError:
            pass
<|end_body_1|>

Solution:
class ObjectTransform:
    """transform object and string"""

    def pickle_dumps_to_str(cls, obj):
        """from object to str"""
        try:
            return base64.b64encode(pickle.dumps(obj)).decode()
        except pickle.PicklingError:
            pass

    def pickle_loads_from_str(cls, obj_str):
        """from str to object"""
        try:
            return pickle.loads(base64.b64decode(obj_str.encode()))
        except pickle.UnpicklingError:
            pass


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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def largestValues(self, root): :type root: TreeNode :rtype: List[int]
- def largestValues2(self, root): :type root: TreeNode :rtype: List[int]

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def largestValues(self, root): :type root: TreeNode :rtype: List[int]
- def largestValues2(self, root): :type root: TreeNode :rtype: List[int]

<|skeleton|>
class Solution:

    def largestValues(self, root):
        """:type root: TreeNode :rtype: List[int]"""
        <|body_0|>

    def largestValues2(self, root):
        """:type root: TreeNode :rtype: List[int]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        nodes = [root]
        res = []
        while any(nodes):
            nodes = [node for node in nodes if node]
            res.append(max([node.val for node in nodes]))
            nodes = [n for node in nodes for n in (node.left, node.right)]
        return res
<|end_body_0|>

<|body_start_1|>
        if not root:
            return []
        nodes = [(root, 0)]
        high_level = 0
        dict = {}
        while nodes:
            cur, h = nodes.pop(0)
            if h not in dict:
                dict[h] = [cur.val]
            else:
                dict[h].append(cur.val)
            if h > high_level:
                high_level = h
            if cur.left:
                nodes.append((cur.left, h + 1))
            if cur.right:
                nodes.append((cur.right, h + 1))
        res = [max(value) for value in dict.values()]
        return res
<|end_body_1|>

Solution:
class Solution:
    def largestValues(self, root):
        """:type root: TreeNode :rtype: List[int]"""
        nodes = [root]
        res = []
        while any(nodes):
            nodes = [node for node in nodes if node]
            res.append(max([node.val for node in nodes]))
            nodes = [n for node in nodes for n in (node.left, node.right)]
        return res

    def largestValues2(self, root):
        """:type root: TreeNode :rtype: List[int]"""
        if not root:
            return []
        nodes = [(root, 0)]
        high_level = 0
        dict = {}
        while nodes:
            cur, h = nodes.pop(0)
            if h not in dict:
                dict[h] = [cur.val]
            else:
                dict[h].append(cur.val)
            if h > high_level:
                high_level = h
            if cur.left:
                nodes.append((cur.left, h + 1))
            if cur.right:
                nodes.append((cur.right, h + 1))
        res = [max(value) for value in dict.values()]
        return res

515. Find Largest Value in Each Tree Row/largest.py


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

Class description:
Implement the WindowFooterPanel class.

Method signatures and docstrings:
- def __init__(self, commander_window): constructor initialize all footer panel elements Keyword arguments: :param commander_window: an initialized instance (parent main window) of CommanderWindow class
- def setup_footer_panel(self): This method is meant to create all the footer elements on this class used only from constructor
- def create_footer_push_button(self, text, shortcut=None, connection=None): This method will create a button into footer layout Keyword arguments: text -- text to be set in the action (ie. "View") :param shortcut: string sequence of keys or single key for shortcut (ie. "F3", default None) :param connection: connection method that will be triggered when Action is clicked (default: None)

Implement the Python class `WindowFooterPanel` described below.

Class description:
Implement the WindowFooterPanel class.

Method signatures and docstrings:
- def __init__(self, commander_window): constructor initialize all footer panel elements Keyword arguments: :param commander_window: an initialized instance (parent main window) of CommanderWindow class
- def setup_footer_panel(self): This method is meant to create all the footer elements on this class used only from constructor
- def create_footer_push_button(self, text, shortcut=None, connection=None): This method will create a button into footer layout Keyword arguments: text -- text to be set in the action (ie. "View") :param shortcut: string sequence of keys or single key for shortcut (ie. "F3", default None) :param connection: connection method that will be triggered when Action is clicked (default: None)

<|skeleton|>
class WindowFooterPanel:

    def __init__(self, commander_window):
        """constructor initialize all footer panel elements Keyword arguments: :param commander_window: an initialized instance (parent main window) of CommanderWindow class"""
        <|body_0|>

    def setup_footer_panel(self):
        """This method is meant to create all the footer elements on this class used only from constructor"""
        <|body_1|>

    def create_footer_push_button(self, text, shortcut=None, connection=None):
        """This method will create a button into footer layout Keyword arguments: text -- text to be set in the action (ie. "View") :param shortcut: string sequence of keys or single key for shortcut (ie. "F3", default None) :param connection: connection method that will be triggered when Action is clicked (default: None)"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.commander_window = commander_window
        self.font = QtGui.QFont(self)
        self.font.setBold(True)
        self.font.setWeight(75)
        self.setup_footer_panel()
<|end_body_0|>

<|body_start_1|>
        self.create_footer_push_button('F3 View', 'F3')
        self.create_footer_push_button('F4 Edit', 'F4')
        self.create_footer_push_button('F5 Copy', 'F5')
        self.create_footer_push_button('F6 move', 'F6')
        self.create_footer_push_button('F7 New Folder', 'F7')
        self.create_footer_push_button('F8 Delete', 'F8')
        self.create_footer_push_button('ALT+F4 Exit')
<|end_body_1|>

<|body_start_2|>
        button = QtGui.QPushButton(self.commander_window.footer_container)
        button.setFont(self.font)
        button.setText(text)
        if shortcut is not None:
            button.setShortcut(shortcut)
        if connection is not None:
            button.triggered.connect(connection)
        self.commander_window.footer_layout.addWidget(button)
        return button
<|end_body_2|>

Solution:
class WindowFooterPanel:
    def __init__(self, commander_window):
        """constructor initialize all footer panel elements Keyword arguments: :param commander_window: an initialized instance (parent main window) of CommanderWindow class"""
        self.commander_window = commander_window
        self.font = QtGui.QFont(self)
        self.font.setBold(True)
        self.font.setWeight(75)
        self.setup_footer_panel()

    def setup_footer_panel(self):
        """This method is meant to create all the footer elements on this class used only from constructor"""
        self.create_footer_push_button('F3 View', 'F3')
        self.create_footer_push_button('F4 Edit', 'F4')
        self.create_footer_push_button('F5 Copy', 'F5')
        self.create_footer_push_button('F6 move', 'F6')
        self.create_footer_push_button('F7 New Folder', 'F7')
        self.create_footer_push_button('F8 Delete', 'F8')
        self.create_footer_push_button('ALT+F4 Exit')

    def create_footer_push_button(self, text, shortcut=None, connection=None):
        """This method will create a button into footer layout Keyword arguments: text -- text to be set in the action (ie. "View") :param shortcut: string sequence of keys or single key for shortcut (ie. "F3", default None) :param connection: connection method that will be triggered when Action is clicked (default: None)"""
        button = QtGui.QPushButton(self.commander_window.footer_container)
        button.setFont(self.font)
        button.setText(text)
        if shortcut is not None:
            button.setShortcut(shortcut)
        if connection is not None:
            button.triggered.connect(connection)
        self.commander_window.footer_layout.addWidget(button)
        return button


removeElement

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

Class description:
给出一个列表和一个值，从列表中删除给定的值，返回最终的数组长度 Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this by modifying the input array in-place with O(1) extra memory. The order of elements can be changed. It doesn't matter what you leave beyond the new length. Example 1: Given nums = [3,2,2,3], val = 3, Your function should return length = 2, with the first two elements of nums being 2. It doesn't matter what you leave beyond the returned length. Example 2: Given nums = [0,1,2,2,3,0,4,2], val = 2, Your function should return length = 5, with the first five elements of nums containing 0, 1, 3, 0

Method signatures and docstrings:
- def symb(self, nums, val): :type nums: List[int] :type val: int :rtype: int
- def removeElement(self, nums, val): 从左开始往右移动，每碰到一个目标值，就将其交换到列表尾部 Starting from the left every time we find a value that is the target value we swap it out with an item starting from the right. We decrement end each time as we know that the final item is the target value and only increment start once we know the value is ok. Once start reaches end we know all items after that point are the target value so we can stop there.

Implement the Python class `Solution` described below.

Class description:
给出一个列表和一个值，从列表中删除给定的值，返回最终的数组长度 Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this by modifying the input array in-place with O(1) extra memory. The order of elements can be changed. It doesn't matter what you leave beyond the new length. Example 1: Given nums = [3,2,2,3], val = 3, Your function should return length = 2, with the first two elements of nums being 2. It doesn't matter what you leave beyond the returned length. Example 2: Given nums = [0,1,2,2,3,0,4,2], val = 2, Your function should return length = 5, with the first five elements of nums containing 0, 1, 3, 0

Method signatures and docstrings:
- def symb(self, nums, val): :type nums: List[int] :type val: int :rtype: int
- def removeElement(self, nums, val): 从左开始往右移动，每碰到一个目标值，就将其交换到列表尾部 Starting from the left every time we find a value that is the target value we swap it out with an item starting from the right. We decrement end each time as we know that the final item is the target value and only increment start once we know the value is ok. Once start reaches end we know all items after that point are the target value so we can stop there.

<|skeleton|>
class Solution:
    """给出一个列表和一个值，从列表中删除给定的值，返回最终的数组长度 Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this by modifying the input array in-place with O(1) extra memory. The order of elements can be changed. It doesn't matter what you leave beyond the new length. Example 1: Given nums = [3,2,2,3], val = 3, Your function should return length = 2, with the first two elements of nums being 2. It doesn't matter what you leave beyond the returned length. Example 2: Given nums = [0,1,2,2,3,0,4,2], val = 2, Your function should return length = 5, with the first five elements of nums containing 0, 1, 3, 0"""

    def symb(self, nums, val):
        """:type nums: List[int] :type val: int :rtype: int"""
        <|body_0|>

    def removeElement(self, nums, val):
        """从左开始往右移动，每碰到一个目标值，就将其交换到列表尾部 Starting from the left every time we find a value that is the target value we swap it out with an item starting from the right. We decrement end each time as we know that the final item is the target value and only increment start once we know the value is ok. Once start reaches end we know all items after that point are the target value so we can stop there."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        result = 0
        for index, value in enumerate(nums):
            if value == val:
                continue
            if result != index:
                nums[result] = value
            result += 1
        return result
<|end_body_0|>

<|body_start_1|>
        start, end = (0, len(nums) - 1)
        while start <= end:
            if nums[start] == val:
                nums[start], nums[end], end = (nums[end], nums[start], end - 1)
            else:
                start += 1
        return start
<|end_body_1|>

Solution:
class Solution:
    """给出一个列表和一个值，从列表中删除给定的值，返回最终的数组长度 Given an array nums and a value val, remove all instances of that value in-place and return the new length. Do not allocate extra space for another array, you must do this by modifying the input array in-place with O(1) extra memory. The order of elements can be changed. It doesn't matter what you leave beyond the new length. Example 1: Given nums = [3,2,2,3], val = 3, Your function should return length = 2, with the first two elements of nums being 2. It doesn't matter what you leave beyond the returned length. Example 2: Given nums = [0,1,2,2,3,0,4,2], val = 2, Your function should return length = 5, with the first five elements of nums containing 0, 1, 3, 0"""

    def symb(self, nums, val):
        """:type nums: List[int] :type val: int :rtype: int"""
        result = 0
        for index, value in enumerate(nums):
            if value == val:
                continue
            if result != index:
                nums[result] = value
            result += 1
        return result

    def removeElement(self, nums, val):
        """从左开始往右移动，每碰到一个目标值，就将其交换到列表尾部 Starting from the left every time we find a value that is the target value we swap it out with an item starting from the right. We decrement end each time as we know that the final item is the target value and only increment start once we know the value is ok. Once start reaches end we know all items after that point are the target value so we can stop there."""
        start, end = (0, len(nums) - 1)
        while start <= end:
            if nums[start] == val:
                nums[start], nums[end], end = (nums[end], nums[start], end - 1)
            else:
                start += 1
        return start


computeReturn

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

Class description:
A class to compute X-minute returns of trades and mid-quotes Attributes ------ _ts : list A list of time stamps from the data _pr : list A list of prices from the data Methods ------ computeReturn(x) Return the X-time lag return of the trade price/mid quote price for trade/quote data

Method signatures and docstrings:
- def __init__(self, filePath, ty): Parameters ------ filePath : str The file path of the data ty : str type of the data, either 'trades' or 'quotes'
- def computeReturn(self, x): Compute the X-minute return Parameters ------ x : int Minute

Implement the Python class `Computer` described below.

Class description:
A class to compute X-minute returns of trades and mid-quotes Attributes ------ _ts : list A list of time stamps from the data _pr : list A list of prices from the data Methods ------ computeReturn(x) Return the X-time lag return of the trade price/mid quote price for trade/quote data

Method signatures and docstrings:
- def __init__(self, filePath, ty): Parameters ------ filePath : str The file path of the data ty : str type of the data, either 'trades' or 'quotes'
- def computeReturn(self, x): Compute the X-minute return Parameters ------ x : int Minute

<|skeleton|>
class Computer:
    """A class to compute X-minute returns of trades and mid-quotes Attributes ------ _ts : list A list of time stamps from the data _pr : list A list of prices from the data Methods ------ computeReturn(x) Return the X-time lag return of the trade price/mid quote price for trade/quote data"""

    def __init__(self, filePath, ty):
        """Parameters ------ filePath : str The file path of the data ty : str type of the data, either 'trades' or 'quotes'"""
        <|body_0|>

    def computeReturn(self, x):
        """Compute the X-minute return Parameters ------ x : int Minute"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if ty == 'trades':
            reader = BinReader(filePath, '>QIIf', 100)
            self._ts = []
            self._pr = []
            while reader.hasNext():
                now = reader.next()
                self._ts.append(now[0])
                self._pr.append(now[3])
        elif ty == 'quotes':
            reader = BinReader(filePath, '>QIIfIf', 100)
            self._ts = []
            self._pr = []
            while reader.hasNext():
                now = reader.next()
                self._ts.append(now[0])
                self._pr.append((now[3] + now[5]) / 2)
<|end_body_0|>

<|body_start_1|>
        xInMiliSec = 60 * 1000 * x
        r = []
        find_t = self._ts[0] + xInMiliSec
        pre_t = find_t
        pre_p = self._pr[0]
        base_t = self._ts[0]
        base_p = self._pr[0]
        for i, p in enumerate(self._pr):
            if self._ts[i] > find_t:
                if base_t == pre_t:
                    find_t = self._ts[i] + xInMiliSec
                else:
                    r.append(pre_p / base_p - 1)
                    find_t += xInMiliSec
                base_t = self._ts[i]
                base_p = p
            elif self._ts[i] == find_t:
                r.append(p / base_p - 1)
                base_p = p
                find_t += xInMiliSec
            pre_t = self._ts
            pre_p = p
        return r
<|end_body_1|>

Solution:
class Computer:
    """A class to compute X-minute returns of trades and mid-quotes Attributes ------ _ts : list A list of time stamps from the data _pr : list A list of prices from the data Methods ------ computeReturn(x) Return the X-time lag return of the trade price/mid quote price for trade/quote data"""

    def __init__(self, filePath, ty):
        """Parameters ------ filePath : str The file path of the data ty : str type of the data, either 'trades' or 'quotes'"""
        if ty == 'trades':
            reader = BinReader(filePath, '>QIIf', 100)
            self._ts = []
            self._pr = []
            while reader.hasNext():
                now = reader.next()
                self._ts.append(now[0])
                self._pr.append(now[3])
        elif ty == 'quotes':
            reader = BinReader(filePath, '>QIIfIf', 100)
            self._ts = []
            self._pr = []
            while reader.hasNext():
                now = reader.next()
                self._ts.append(now[0])
                self._pr.append((now[3] + now[5]) / 2)

    def computeReturn(self, x):
        """Compute the X-minute return Parameters ------ x : int Minute"""
        xInMiliSec = 60 * 1000 * x
        r = []
        find_t = self._ts[0] + xInMiliSec
        pre_t = find_t
        pre_p = self._pr[0]
        base_t = self._ts[0]
        base_p = self._pr[0]
        for i, p in enumerate(self._pr):
            if self._ts[i] > find_t:
                if base_t == pre_t:
                    find_t = self._ts[i] + xInMiliSec
                else:
                    r.append(pre_p / base_p - 1)
                    find_t += xInMiliSec
                base_t = self._ts[i]
                base_p = p
            elif self._ts[i] == find_t:
                r.append(p / base_p - 1)
                base_p = p
                find_t += xInMiliSec
            pre_t = self._ts
            pre_p = p
        return r


Run

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

Class description:
Disable a service for consumption for a project. This command disables one or more previously-enabled services for consumption. To see a list of the enabled services for a project, run: $ {parent_command} list More information on listing services can be found at: https://cloud.google.com/service-usage/docs/list-services and on disabling a service at: https://cloud.google.com/service-usage/docs/enable-disable ## EXAMPLES To disable a service called `my-consumed-service` for the active project, run: $ {command} my-consumed-service To run the same command asynchronously (non-blocking), run: $ {command} my-consumed-service --async

Method signatures and docstrings:
- def Args(parser): Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed.
- def Run(self, args): Run 'services disable'. Args: args: argparse.Namespace, The arguments that this command was invoked with. Returns: Nothing.

Implement the Python class `Disable` described below.

Class description:
Disable a service for consumption for a project. This command disables one or more previously-enabled services for consumption. To see a list of the enabled services for a project, run: $ {parent_command} list More information on listing services can be found at: https://cloud.google.com/service-usage/docs/list-services and on disabling a service at: https://cloud.google.com/service-usage/docs/enable-disable ## EXAMPLES To disable a service called `my-consumed-service` for the active project, run: $ {command} my-consumed-service To run the same command asynchronously (non-blocking), run: $ {command} my-consumed-service --async

Method signatures and docstrings:
- def Args(parser): Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed.
- def Run(self, args): Run 'services disable'. Args: args: argparse.Namespace, The arguments that this command was invoked with. Returns: Nothing.

<|skeleton|>
class Disable:
    """Disable a service for consumption for a project. This command disables one or more previously-enabled services for consumption. To see a list of the enabled services for a project, run: $ {parent_command} list More information on listing services can be found at: https://cloud.google.com/service-usage/docs/list-services and on disabling a service at: https://cloud.google.com/service-usage/docs/enable-disable ## EXAMPLES To disable a service called `my-consumed-service` for the active project, run: $ {command} my-consumed-service To run the same command asynchronously (non-blocking), run: $ {command} my-consumed-service --async"""

    def Args(parser):
        """Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed."""
        <|body_0|>

    def Run(self, args):
        """Run 'services disable'. Args: args: argparse.Namespace, The arguments that this command was invoked with. Returns: Nothing."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        common_flags.consumer_service_flag(suffix='to disable').AddToParser(parser)
        base.ASYNC_FLAG.AddToParser(parser)
        parser.add_argument('--force', action='store_true', help='If specified, the disable call will proceed even if there are enabled services which depend on the service to be disabled. Forcing the call means that the services which depend on the service to be disabled will also be disabled.')
<|end_body_0|>

<|body_start_1|>
        project = properties.VALUES.core.project.Get(required=True)
        for service_name in args.service:
            service_name = arg_parsers.GetServiceNameFromArg(service_name)
            protected_msg = serviceusage.GetProtectedServiceWarning(service_name)
            if protected_msg:
                if args.IsSpecified('quiet'):
                    raise console_io.RequiredPromptError()
                do_disable = console_io.PromptContinue(protected_msg, default=False, throw_if_unattended=True)
                if not do_disable:
                    continue
            op = serviceusage.DisableApiCall(project, service_name, args.force)
            if op.done:
                return
            if args.async_:
                cmd = OP_WAIT_CMD.format(op.name)
                log.status.Print('Asynchronous operation is in progress... Use the following command to wait for its completion:\n {0}'.format(cmd))
                return
            op = services_util.WaitOperation(op.name, serviceusage.GetOperation)
            services_util.PrintOperation(op)
<|end_body_1|>

Solution:
class Disable:
    """Disable a service for consumption for a project. This command disables one or more previously-enabled services for consumption. To see a list of the enabled services for a project, run: $ {parent_command} list More information on listing services can be found at: https://cloud.google.com/service-usage/docs/list-services and on disabling a service at: https://cloud.google.com/service-usage/docs/enable-disable ## EXAMPLES To disable a service called `my-consumed-service` for the active project, run: $ {command} my-consumed-service To run the same command asynchronously (non-blocking), run: $ {command} my-consumed-service --async"""

    def Args(parser):
        """Args is called by calliope to gather arguments for this command. Args: parser: An argparse parser that you can use to add arguments that go on the command line after this command. Positional arguments are allowed."""
        common_flags.consumer_service_flag(suffix='to disable').AddToParser(parser)
        base.ASYNC_FLAG.AddToParser(parser)
        parser.add_argument('--force', action='store_true', help='If specified, the disable call will proceed even if there are enabled services which depend on the service to be disabled. Forcing the call means that the services which depend on the service to be disabled will also be disabled.')

    def Run(self, args):
        """Run 'services disable'. Args: args: argparse.Namespace, The arguments that this command was invoked with. Returns: Nothing."""
        project = properties.VALUES.core.project.Get(required=True)
        for service_name in args.service:
            service_name = arg_parsers.GetServiceNameFromArg(service_name)
            protected_msg = serviceusage.GetProtectedServiceWarning(service_name)
            if protected_msg:
                if args.IsSpecified('quiet'):
                    raise console_io.RequiredPromptError()
                do_disable = console_io.PromptContinue(protected_msg, default=False, throw_if_unattended=True)
                if not do_disable:
                    continue
            op = serviceusage.DisableApiCall(project, service_name, args.force)
            if op.done:
                return
            if args.async_:
                cmd = OP_WAIT_CMD.format(op.name)
                log.status.Print('Asynchronous operation is in progress... Use the following command to wait for its completion:\n {0}'.format(cmd))
                return
            op = services_util.WaitOperation(op.name, serviceusage.GetOperation)
            services_util.PrintOperation(op)


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

Class description:
Generates an input_fn that works with TFDS datasets.

Method signatures and docstrings:
- def __init__(self, dataset_name, split, *args, data_dir=None, **kwargs): Creates an input_fn for a TFDS dataset. Args: dataset_name: The name of the TFDS dataset, passed as the `name` argument of tfds.load(). split: The split name passed as the `split` argument to tfds.load(). *args: Arguments passed on to CommonInput. data_dir: The directory passed as the `data_dir` argument to tfds.load(). **kwargs: Keyword arguments passed on to CommonInput.
- def dataset_parser(self, value): Parses a TFDS datum tuple into an InputData instance. Args: value: A dictionary with keys 'image' (or 'video') and 'label'. Returns: An InputData consisting of an image and label. Note: If `value` contains a 'video' key, then the returned images entry will be set to it.
- def make_source_dataset(self, current_host_index, num_hosts): Makes a dataset of dictionaries of images and labels. Args: current_host_index: current host index. num_hosts: total number of hosts. Returns: A `tf.data.Dataset` object where each dataset element is a dictionary. For image classification datasets, the dictionary will contain an 'image' key with a decoded uint8 image (of shape [height, width, channels]) and a 'label' key with an int64 label. For video classification datasets, the dictionary will contain a 'video' key with a decoded uint8 video (of shape [frames, height, width, channels]) and a 'label' key with an int64 label.

Implement the Python class `TfdsInput` described below.

Class description:
Generates an input_fn that works with TFDS datasets.

Method signatures and docstrings:
- def __init__(self, dataset_name, split, *args, data_dir=None, **kwargs): Creates an input_fn for a TFDS dataset. Args: dataset_name: The name of the TFDS dataset, passed as the `name` argument of tfds.load(). split: The split name passed as the `split` argument to tfds.load(). *args: Arguments passed on to CommonInput. data_dir: The directory passed as the `data_dir` argument to tfds.load(). **kwargs: Keyword arguments passed on to CommonInput.
- def dataset_parser(self, value): Parses a TFDS datum tuple into an InputData instance. Args: value: A dictionary with keys 'image' (or 'video') and 'label'. Returns: An InputData consisting of an image and label. Note: If `value` contains a 'video' key, then the returned images entry will be set to it.
- def make_source_dataset(self, current_host_index, num_hosts): Makes a dataset of dictionaries of images and labels. Args: current_host_index: current host index. num_hosts: total number of hosts. Returns: A `tf.data.Dataset` object where each dataset element is a dictionary. For image classification datasets, the dictionary will contain an 'image' key with a decoded uint8 image (of shape [height, width, channels]) and a 'label' key with an int64 label. For video classification datasets, the dictionary will contain a 'video' key with a decoded uint8 video (of shape [frames, height, width, channels]) and a 'label' key with an int64 label.

<|skeleton|>
class TfdsInput:
    """Generates an input_fn that works with TFDS datasets."""

    def __init__(self, dataset_name, split, *args, data_dir=None, **kwargs):
        """Creates an input_fn for a TFDS dataset. Args: dataset_name: The name of the TFDS dataset, passed as the `name` argument of tfds.load(). split: The split name passed as the `split` argument to tfds.load(). *args: Arguments passed on to CommonInput. data_dir: The directory passed as the `data_dir` argument to tfds.load(). **kwargs: Keyword arguments passed on to CommonInput."""
        <|body_0|>

    def dataset_parser(self, value):
        """Parses a TFDS datum tuple into an InputData instance. Args: value: A dictionary with keys 'image' (or 'video') and 'label'. Returns: An InputData consisting of an image and label. Note: If `value` contains a 'video' key, then the returned images entry will be set to it."""
        <|body_1|>

    def make_source_dataset(self, current_host_index, num_hosts):
        """Makes a dataset of dictionaries of images and labels. Args: current_host_index: current host index. num_hosts: total number of hosts. Returns: A `tf.data.Dataset` object where each dataset element is a dictionary. For image classification datasets, the dictionary will contain an 'image' key with a decoded uint8 image (of shape [height, width, channels]) and a 'label' key with an int64 label. For video classification datasets, the dictionary will contain a 'video' key with a decoded uint8 video (of shape [frames, height, width, channels]) and a 'label' key with an int64 label."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(TfdsInput, self).__init__(*args, **kwargs)
        self.dataset_name = dataset_name
        self.split = split
        self.data_dir = data_dir
<|end_body_0|>

<|body_start_1|>
        if 'image' in value:
            images = value['image']
        elif 'video' in value:
            images = value['video']
        else:
            raise ValueError('No "image" or "video" key found in TFDS datum')
        return InputData(images=images, labels=value['label'])
<|end_body_1|>

<|body_start_2|>
        split = self.split
        if self.mode == enums.ModelMode.TRAIN and self.shard_per_host:
            split = tfds.even_splits(split, n=num_hosts)[current_host_index]
        return tfds.load(name=self.dataset_name, split=split, data_dir=self.data_dir, shuffle_files=False)
<|end_body_2|>

Solution:
class TfdsInput:
    """Generates an input_fn that works with TFDS datasets."""

    def __init__(self, dataset_name, split, *args, data_dir=None, **kwargs):
        """Creates an input_fn for a TFDS dataset. Args: dataset_name: The name of the TFDS dataset, passed as the `name` argument of tfds.load(). split: The split name passed as the `split` argument to tfds.load(). *args: Arguments passed on to CommonInput. data_dir: The directory passed as the `data_dir` argument to tfds.load(). **kwargs: Keyword arguments passed on to CommonInput."""
        super(TfdsInput, self).__init__(*args, **kwargs)
        self.dataset_name = dataset_name
        self.split = split
        self.data_dir = data_dir

    def dataset_parser(self, value):
        """Parses a TFDS datum tuple into an InputData instance. Args: value: A dictionary with keys 'image' (or 'video') and 'label'. Returns: An InputData consisting of an image and label. Note: If `value` contains a 'video' key, then the returned images entry will be set to it."""
        if 'image' in value:
            images = value['image']
        elif 'video' in value:
            images = value['video']
        else:
            raise ValueError('No "image" or "video" key found in TFDS datum')
        return InputData(images=images, labels=value['label'])

    def make_source_dataset(self, current_host_index, num_hosts):
        """Makes a dataset of dictionaries of images and labels. Args: current_host_index: current host index. num_hosts: total number of hosts. Returns: A `tf.data.Dataset` object where each dataset element is a dictionary. For image classification datasets, the dictionary will contain an 'image' key with a decoded uint8 image (of shape [height, width, channels]) and a 'label' key with an int64 label. For video classification datasets, the dictionary will contain a 'video' key with a decoded uint8 video (of shape [frames, height, width, channels]) and a 'label' key with an int64 label."""
        split = self.split
        if self.mode == enums.ModelMode.TRAIN and self.shard_per_host:
            split = tfds.even_splits(split, n=num_hosts)[current_host_index]
        return tfds.load(name=self.dataset_name, split=split, data_dir=self.data_dir, shuffle_files=False)


load

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

Class description:
The following class is derived from cox. https://github.com/MadryLab/cox/blob/master/cox/store.py Copyright (c) 2018 Andrew Ilyas, Logan Engstrom, licensed under the MIT license, cf. 3rd-party-licenses.txt file in the root directory of this source tree.

Method signatures and docstrings:
- def log_tb(self, table_name, update_dict, summary_type='scalar', step=None): Log to only tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint
- def log_table_and_tb(self, table_name, update_dict, summary_type='scalar', step=None): Log to a table and also a tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint
- def load(self, table: str, key: str, data_save_type: str, iteration: int=-1, **kwargs): Load data from store. table: name of table to load from key: key of value to load data_save_type: Type of the data to be loaded. One of 'object' 'state_dict', or 'pickle'. iteration: Iteration checkpoint to load kwargs: Returns:

Implement the Python class `CustomStore` described below.

Class description:
The following class is derived from cox. https://github.com/MadryLab/cox/blob/master/cox/store.py Copyright (c) 2018 Andrew Ilyas, Logan Engstrom, licensed under the MIT license, cf. 3rd-party-licenses.txt file in the root directory of this source tree.

Method signatures and docstrings:
- def log_tb(self, table_name, update_dict, summary_type='scalar', step=None): Log to only tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint
- def log_table_and_tb(self, table_name, update_dict, summary_type='scalar', step=None): Log to a table and also a tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint
- def load(self, table: str, key: str, data_save_type: str, iteration: int=-1, **kwargs): Load data from store. table: name of table to load from key: key of value to load data_save_type: Type of the data to be loaded. One of 'object' 'state_dict', or 'pickle'. iteration: Iteration checkpoint to load kwargs: Returns:

<|skeleton|>
class CustomStore:
    """The following class is derived from cox. https://github.com/MadryLab/cox/blob/master/cox/store.py Copyright (c) 2018 Andrew Ilyas, Logan Engstrom, licensed under the MIT license, cf. 3rd-party-licenses.txt file in the root directory of this source tree."""

    def log_tb(self, table_name, update_dict, summary_type='scalar', step=None):
        """Log to only tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint"""
        <|body_0|>

    def log_table_and_tb(self, table_name, update_dict, summary_type='scalar', step=None):
        """Log to a table and also a tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint"""
        <|body_1|>

    def load(self, table: str, key: str, data_save_type: str, iteration: int=-1, **kwargs):
        """Load data from store. table: name of table to load from key: key of value to load data_save_type: Type of the data to be loaded. One of 'object' 'state_dict', or 'pickle'. iteration: Iteration checkpoint to load kwargs: Returns:"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        table = self.tables[table_name]
        update_dict = _clean_dict(update_dict, table.schema)
        tb_func = getattr(self.tensorboard, 'add_%s' % summary_type)
        step = step if step else table.nrows
        for name, value in update_dict.items():
            tb_func('/'.join([table_name, name]), value, step)
<|end_body_0|>

<|body_start_1|>
        table = self.tables[table_name]
        update_dict = _clean_dict(update_dict, table.schema)
        tb_func = getattr(self.tensorboard, 'add_%s' % summary_type)
        step = step if step else table.nrows
        for name, value in update_dict.items():
            tb_func('/'.join([table_name, name]), value, step)
        table.update_row(update_dict)
<|end_body_1|>

<|body_start_2|>
        if data_save_type == 'object':
            return self.tables[table].get_object(self.tables[table].df[key].iloc[iteration], **kwargs)
        elif data_save_type == 'state_dict':
            return self.tables[table].get_state_dict(self.tables[table].df[key].iloc[iteration], **kwargs)
        elif data_save_type == 'pickle':
            return self.tables[table].get_pickle(self.tables[table].df[key].iloc[iteration], **kwargs)
        else:
            return self.tables[table].df[key].iloc[iteration]
<|end_body_2|>

Solution:
class CustomStore:
    """The following class is derived from cox. https://github.com/MadryLab/cox/blob/master/cox/store.py Copyright (c) 2018 Andrew Ilyas, Logan Engstrom, licensed under the MIT license, cf. 3rd-party-licenses.txt file in the root directory of this source tree."""

    def log_tb(self, table_name, update_dict, summary_type='scalar', step=None):
        """Log to only tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint"""
        table = self.tables[table_name]
        update_dict = _clean_dict(update_dict, table.schema)
        tb_func = getattr(self.tensorboard, 'add_%s' % summary_type)
        step = step if step else table.nrows
        for name, value in update_dict.items():
            tb_func('/'.join([table_name, name]), value, step)

    def log_table_and_tb(self, table_name, update_dict, summary_type='scalar', step=None):
        """Log to a table and also a tensorboard. Args: table_name (str) : which table to log to update_dict (dict) : values to log and store as a dictionary of column mapping to value. summary_type (str) : what type of summary to log to tensorboard as step: which step index to insert datapoint"""
        table = self.tables[table_name]
        update_dict = _clean_dict(update_dict, table.schema)
        tb_func = getattr(self.tensorboard, 'add_%s' % summary_type)
        step = step if step else table.nrows
        for name, value in update_dict.items():
            tb_func('/'.join([table_name, name]), value, step)
        table.update_row(update_dict)

    def load(self, table: str, key: str, data_save_type: str, iteration: int=-1, **kwargs):
        """Load data from store. table: name of table to load from key: key of value to load data_save_type: Type of the data to be loaded. One of 'object' 'state_dict', or 'pickle'. iteration: Iteration checkpoint to load kwargs: Returns:"""
        if data_save_type == 'object':
            return self.tables[table].get_object(self.tables[table].df[key].iloc[iteration], **kwargs)
        elif data_save_type == 'state_dict':
            return self.tables[table].get_state_dict(self.tables[table].df[key].iloc[iteration], **kwargs)
        elif data_save_type == 'pickle':
            return self.tables[table].get_pickle(self.tables[table].df[key].iloc[iteration], **kwargs)
        else:
            return self.tables[table].df[key].iloc[iteration]


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

Class description:
Implement the MessageCreateBaseView class.

Method signatures and docstrings:
- def get_queryset(self): Return context for standard paging.
- def get_context_data(self, **kwargs): Add additional useful information.

Implement the Python class `MessageCreateBaseView` described below.

Class description:
Implement the MessageCreateBaseView class.

Method signatures and docstrings:
- def get_queryset(self): Return context for standard paging.
- def get_context_data(self, **kwargs): Add additional useful information.

<|skeleton|>
class MessageCreateBaseView:

    def get_queryset(self):
        """Return context for standard paging."""
        <|body_0|>

    def get_context_data(self, **kwargs):
        """Add additional useful information."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        format_convert = {'headsUp': ['invite', 'Heads Up'], 'available': ['invite', 'Available?'], 'leave': ['leave', 'Left?'], 'return': ['return', 'Returned?'], 'info': ['info', None], 'broadcast': ['broadcast', None], 'test': ['test', 'Test']}
        initial = {}
        initial['author'] = self.request.user.pk
        initial['type'] = 'std_page'
        initial['format'] = 'page'
        members = None
        page_format = self.request.GET.get('page_format', self.page_format)
        period_format = format_convert[page_format][0]
        initial['period_format'] = period_format
        rsvp_name = format_convert[page_format][1]
        self.rsvp_template = None
        if rsvp_name is not None:
            try:
                self.rsvp_template = RsvpTemplate.objects.get(name=rsvp_name)
                initial['rsvp_template'] = self.rsvp_template
            except RsvpTemplate.DoesNotExist:
                logger.error('RsvpTemplate {} not found for format: {}'.format(rsvp_name, page_format))
        self.initial = initial
        return members
<|end_body_0|>

<|body_start_1|>
        context = super().get_context_data(**kwargs)
        if self.rsvp_template and self.initial['type'] != 'repage':
            self.initial['input'] = '{} {}'.format(self.initial['input'], self.rsvp_template.text)
        instructions = {'invite': 'Page the team to invite them to the OP. Members already signed up still get a page.', 'info': 'Send an informational page to people signed up for the OP. No response expected.', 'broadcast': 'Send an informational page to the whole team. No response expected.', 'leave': 'Transit page to people signed up for the event. Responses will mark the participant as departed.', 'return': 'Transit page to people signed up for the event. Responses will mark the participant as returned home.', 'test': 'Test page. DO NOT USE IN A REAL CALLOUT.'}
        self.initial['instructions'] = instructions.get(self.initial['period_format'], 'WARNING: Unknown period_format')
        return {**context, **self.initial}
<|end_body_1|>

Solution:
class MessageCreateBaseView:
    def get_queryset(self):
        """Return context for standard paging."""
        format_convert = {'headsUp': ['invite', 'Heads Up'], 'available': ['invite', 'Available?'], 'leave': ['leave', 'Left?'], 'return': ['return', 'Returned?'], 'info': ['info', None], 'broadcast': ['broadcast', None], 'test': ['test', 'Test']}
        initial = {}
        initial['author'] = self.request.user.pk
        initial['type'] = 'std_page'
        initial['format'] = 'page'
        members = None
        page_format = self.request.GET.get('page_format', self.page_format)
        period_format = format_convert[page_format][0]
        initial['period_format'] = period_format
        rsvp_name = format_convert[page_format][1]
        self.rsvp_template = None
        if rsvp_name is not None:
            try:
                self.rsvp_template = RsvpTemplate.objects.get(name=rsvp_name)
                initial['rsvp_template'] = self.rsvp_template
            except RsvpTemplate.DoesNotExist:
                logger.error('RsvpTemplate {} not found for format: {}'.format(rsvp_name, page_format))
        self.initial = initial
        return members

    def get_context_data(self, **kwargs):
        """Add additional useful information."""
        context = super().get_context_data(**kwargs)
        if self.rsvp_template and self.initial['type'] != 'repage':
            self.initial['input'] = '{} {}'.format(self.initial['input'], self.rsvp_template.text)
        instructions = {'invite': 'Page the team to invite them to the OP. Members already signed up still get a page.', 'info': 'Send an informational page to people signed up for the OP. No response expected.', 'broadcast': 'Send an informational page to the whole team. No response expected.', 'leave': 'Transit page to people signed up for the event. Responses will mark the participant as departed.', 'return': 'Transit page to people signed up for the event. Responses will mark the participant as returned home.', 'test': 'Test page. DO NOT USE IN A REAL CALLOUT.'}
        self.initial['instructions'] = instructions.get(self.initial['period_format'], 'WARNING: Unknown period_format')
        return {**context, **self.initial}


tokenize

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

Class description:
Runs basic tokenization with jumanpp morphological parser.

Method signatures and docstrings:
- def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False): Constructs a JumanppTokenizer. Args: **do_lower_case**: (*optional*) boolean (default True) Whether to lowercase the input. **never_split**: (*optional*) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of tokens not to split. **normalize_text**: (*optional*) boolean (default True) Whether to apply unicode normalization to text before tokenization. **trim_whitespace**: (*optional*) boolean (default False) Whether to trim all whitespace, tab, newline from tokens.
- def tokenize(self, text, never_split=None, **kwargs): Tokenizes a piece of text.

Implement the Python class `JumanppTokenizer` described below.

Class description:
Runs basic tokenization with jumanpp morphological parser.

Method signatures and docstrings:
- def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False): Constructs a JumanppTokenizer. Args: **do_lower_case**: (*optional*) boolean (default True) Whether to lowercase the input. **never_split**: (*optional*) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of tokens not to split. **normalize_text**: (*optional*) boolean (default True) Whether to apply unicode normalization to text before tokenization. **trim_whitespace**: (*optional*) boolean (default False) Whether to trim all whitespace, tab, newline from tokens.
- def tokenize(self, text, never_split=None, **kwargs): Tokenizes a piece of text.

<|skeleton|>
class JumanppTokenizer:
    """Runs basic tokenization with jumanpp morphological parser."""

    def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False):
        """Constructs a JumanppTokenizer. Args: **do_lower_case**: (*optional*) boolean (default True) Whether to lowercase the input. **never_split**: (*optional*) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of tokens not to split. **normalize_text**: (*optional*) boolean (default True) Whether to apply unicode normalization to text before tokenization. **trim_whitespace**: (*optional*) boolean (default False) Whether to trim all whitespace, tab, newline from tokens."""
        <|body_0|>

    def tokenize(self, text, never_split=None, **kwargs):
        """Tokenizes a piece of text."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.do_lower_case = do_lower_case
        self.never_split = never_split if never_split is not None else []
        self.normalize_text = normalize_text
        self.trim_whitespace = trim_whitespace
        try:
            import rhoknp
        except ImportError:
            raise ImportError('You need to install rhoknp to use JumanppTokenizer. See https://github.com/ku-nlp/rhoknp for installation.')
        self.juman = rhoknp.Jumanpp()
<|end_body_0|>

<|body_start_1|>
        if self.normalize_text:
            text = unicodedata.normalize('NFKC', text)
        text = text.strip()
        never_split = self.never_split + (never_split if never_split is not None else [])
        tokens = []
        for mrph in self.juman.apply_to_sentence(text).morphemes:
            token = mrph.text
            if self.do_lower_case and token not in never_split:
                token = token.lower()
            if self.trim_whitespace:
                if token.strip() == '':
                    continue
                else:
                    token = token.strip()
            tokens.append(token)
        return tokens
<|end_body_1|>

Solution:
class JumanppTokenizer:
    """Runs basic tokenization with jumanpp morphological parser."""

    def __init__(self, do_lower_case=False, never_split=None, normalize_text=True, trim_whitespace=False):
        """Constructs a JumanppTokenizer. Args: **do_lower_case**: (*optional*) boolean (default True) Whether to lowercase the input. **never_split**: (*optional*) list of str Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of tokens not to split. **normalize_text**: (*optional*) boolean (default True) Whether to apply unicode normalization to text before tokenization. **trim_whitespace**: (*optional*) boolean (default False) Whether to trim all whitespace, tab, newline from tokens."""
        self.do_lower_case = do_lower_case
        self.never_split = never_split if never_split is not None else []
        self.normalize_text = normalize_text
        self.trim_whitespace = trim_whitespace
        try:
            import rhoknp
        except ImportError:
            raise ImportError('You need to install rhoknp to use JumanppTokenizer. See https://github.com/ku-nlp/rhoknp for installation.')
        self.juman = rhoknp.Jumanpp()

    def tokenize(self, text, never_split=None, **kwargs):
        """Tokenizes a piece of text."""
        if self.normalize_text:
            text = unicodedata.normalize('NFKC', text)
        text = text.strip()
        never_split = self.never_split + (never_split if never_split is not None else [])
        tokens = []
        for mrph in self.juman.apply_to_sentence(text).morphemes:
            token = mrph.text
            if self.do_lower_case and token not in never_split:
                token = token.lower()
            if self.trim_whitespace:
                if token.strip() == '':
                    continue
                else:
                    token = token.strip()
            tokens.append(token)
        return tokens


moveZeroesRefined

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def moveZeroes(self, nums) -> None: Do not return anything, modify nums in-place instead. NOTE: This solution is inefficient, in the sense that the move for the trailing zeros should be skipped.
- def moveZeroesRefined(self, nums) -> None: Do not return anything, modify nums in-place instead.

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def moveZeroes(self, nums) -> None: Do not return anything, modify nums in-place instead. NOTE: This solution is inefficient, in the sense that the move for the trailing zeros should be skipped.
- def moveZeroesRefined(self, nums) -> None: Do not return anything, modify nums in-place instead.

<|skeleton|>
class Solution:

    def moveZeroes(self, nums) -> None:
        """Do not return anything, modify nums in-place instead. NOTE: This solution is inefficient, in the sense that the move for the trailing zeros should be skipped."""
        <|body_0|>

    def moveZeroesRefined(self, nums) -> None:
        """Do not return anything, modify nums in-place instead."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        moveCnt = 0
        k = len(nums) - 1
        while k >= 0:
            if nums[k] == 0:
                nums.pop(k)
                nums.append(0)
                moveCnt = moveCnt + 1
            k = k - 1
        print('Totally, {0} moves'.format(moveCnt))
<|end_body_0|>

<|body_start_1|>
        moveEnable = False
        moveCnt = 0
        k = len(nums) - 1
        while k >= 0:
            if nums[k] != 0:
                moveEnable = True
            if nums[k] == 0 and moveEnable == True:
                nums.pop(k)
                nums.append(0)
                moveCnt = moveCnt + 1
            k = k - 1
        print('Totally, {0} moves'.format(moveCnt))
<|end_body_1|>

Solution:
class Solution:
    def moveZeroes(self, nums) -> None:
        """Do not return anything, modify nums in-place instead. NOTE: This solution is inefficient, in the sense that the move for the trailing zeros should be skipped."""
        moveCnt = 0
        k = len(nums) - 1
        while k >= 0:
            if nums[k] == 0:
                nums.pop(k)
                nums.append(0)
                moveCnt = moveCnt + 1
            k = k - 1
        print('Totally, {0} moves'.format(moveCnt))

    def moveZeroesRefined(self, nums) -> None:
        """Do not return anything, modify nums in-place instead."""
        moveEnable = False
        moveCnt = 0
        k = len(nums) - 1
        while k >= 0:
            if nums[k] != 0:
                moveEnable = True
            if nums[k] == 0 and moveEnable == True:
                nums.pop(k)
                nums.append(0)
                moveCnt = moveCnt + 1
            k = k - 1
        print('Totally, {0} moves'.format(moveCnt))


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

Class description:
Tests for the database module

Method signatures and docstrings:
- def test_import_data(self): Tests the import_data function
- def test_import_data_thread(self): Tests the import_data function

Implement the Python class `LinearTests` described below.

Class description:
Tests for the database module

Method signatures and docstrings:
- def test_import_data(self): Tests the import_data function
- def test_import_data_thread(self): Tests the import_data function

<|skeleton|>
class LinearTests:
    """Tests for the database module"""

    def test_import_data(self):
        """Tests the import_data function"""
        <|body_0|>

    def test_import_data_thread(self):
        """Tests the import_data function"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        directory_path = 'data'
        parallel.drop_data()
        list1 = parallel.import_data(directory_path, 'products.csv', 'customers.csv')
        self.assertEqual(list1[0][0], 999)
        self.assertEqual(list1[0][1], 0)
        self.assertEqual(list1[0][2], 999)
        self.assertTrue(list1[0][3] > 0)
        self.assertEqual(list1[1][0], 999)
        self.assertEqual(list1[1][1], 0)
        self.assertEqual(list1[1][2], 999)
        self.assertTrue(list1[1][3] > 0)
        parallel.drop_data()
        list2 = parallel.import_data(directory_path, 'products.csv', 'nothing.csv')
        self.assertEqual(list2[0][0], 999)
        self.assertEqual(list2[0][1], 0)
        self.assertEqual(list2[0][2], 999)
        self.assertTrue(list2[0][3] > 0)
        self.assertEqual(list2[1], None)
<|end_body_0|>

<|body_start_1|>
        directory_path = 'data'
        parallel.drop_data()
        list1 = parallel.import_data_thread(directory_path, 'products.csv', 'customers.csv')
        self.assertEqual(list1[0][0], 999)
        self.assertEqual(list1[0][1], 0)
        self.assertEqual(list1[0][2], 999)
        self.assertTrue(list1[0][3] > 0)
        self.assertEqual(list1[1][0], 999)
        self.assertEqual(list1[1][1], 0)
        self.assertEqual(list1[1][2], 999)
        self.assertTrue(list1[1][3] > 0)
        parallel.drop_data()
        list2 = parallel.import_data_thread(directory_path, 'products.csv', 'nothing.csv')
        self.assertEqual(list2[0][0], 999)
        self.assertEqual(list2[0][1], 0)
        self.assertEqual(list2[0][2], 999)
        self.assertTrue(list2[0][3] > 0)
        self.assertEqual(list2[1], None)
<|end_body_1|>

Solution:
class LinearTests:
    """Tests for the database module"""

    def test_import_data(self):
        """Tests the import_data function"""
        directory_path = 'data'
        parallel.drop_data()
        list1 = parallel.import_data(directory_path, 'products.csv', 'customers.csv')
        self.assertEqual(list1[0][0], 999)
        self.assertEqual(list1[0][1], 0)
        self.assertEqual(list1[0][2], 999)
        self.assertTrue(list1[0][3] > 0)
        self.assertEqual(list1[1][0], 999)
        self.assertEqual(list1[1][1], 0)
        self.assertEqual(list1[1][2], 999)
        self.assertTrue(list1[1][3] > 0)
        parallel.drop_data()
        list2 = parallel.import_data(directory_path, 'products.csv', 'nothing.csv')
        self.assertEqual(list2[0][0], 999)
        self.assertEqual(list2[0][1], 0)
        self.assertEqual(list2[0][2], 999)
        self.assertTrue(list2[0][3] > 0)
        self.assertEqual(list2[1], None)

    def test_import_data_thread(self):
        """Tests the import_data function"""
        directory_path = 'data'
        parallel.drop_data()
        list1 = parallel.import_data_thread(directory_path, 'products.csv', 'customers.csv')
        self.assertEqual(list1[0][0], 999)
        self.assertEqual(list1[0][1], 0)
        self.assertEqual(list1[0][2], 999)
        self.assertTrue(list1[0][3] > 0)
        self.assertEqual(list1[1][0], 999)
        self.assertEqual(list1[1][1], 0)
        self.assertEqual(list1[1][2], 999)
        self.assertTrue(list1[1][3] > 0)
        parallel.drop_data()
        list2 = parallel.import_data_thread(directory_path, 'products.csv', 'nothing.csv')
        self.assertEqual(list2[0][0], 999)
        self.assertEqual(list2[0][1], 0)
        self.assertEqual(list2[0][2], 999)
        self.assertTrue(list2[0][3] > 0)
        self.assertEqual(list2[1], None)


postprocess

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

Class description:
Implement the DialogIntentPredictionPipeline class.

Method signatures and docstrings:
- def __init__(self, model: Union[SpaceForDialogIntent, str], preprocessor: DialogIntentPredictionPreprocessor=None, **kwargs): Use `model` and `preprocessor` to create a dialog intent prediction pipeline Args: model (str or SpaceForDialogIntent): Supply either a local model dir or a model id from the model hub, or a SpaceForDialogIntent instance. preprocessor (DialogIntentPredictionPreprocessor): An optional preprocessor instance.
- def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]: process the prediction results Args: inputs (Dict[str, Any]): _description_ Returns: Dict[str, str]: the prediction results

Implement the Python class `DialogIntentPredictionPipeline` described below.

Class description:
Implement the DialogIntentPredictionPipeline class.

Method signatures and docstrings:
- def __init__(self, model: Union[SpaceForDialogIntent, str], preprocessor: DialogIntentPredictionPreprocessor=None, **kwargs): Use `model` and `preprocessor` to create a dialog intent prediction pipeline Args: model (str or SpaceForDialogIntent): Supply either a local model dir or a model id from the model hub, or a SpaceForDialogIntent instance. preprocessor (DialogIntentPredictionPreprocessor): An optional preprocessor instance.
- def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]: process the prediction results Args: inputs (Dict[str, Any]): _description_ Returns: Dict[str, str]: the prediction results

<|skeleton|>
class DialogIntentPredictionPipeline:

    def __init__(self, model: Union[SpaceForDialogIntent, str], preprocessor: DialogIntentPredictionPreprocessor=None, **kwargs):
        """Use `model` and `preprocessor` to create a dialog intent prediction pipeline Args: model (str or SpaceForDialogIntent): Supply either a local model dir or a model id from the model hub, or a SpaceForDialogIntent instance. preprocessor (DialogIntentPredictionPreprocessor): An optional preprocessor instance."""
        <|body_0|>

    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        """process the prediction results Args: inputs (Dict[str, Any]): _description_ Returns: Dict[str, str]: the prediction results"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        model = model if isinstance(model, SpaceForDialogIntent) else Model.from_pretrained(model)
        if preprocessor is None:
            preprocessor = DialogIntentPredictionPreprocessor(model.model_dir)
        self.model = model
        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
        self.categories = preprocessor.categories
<|end_body_0|>

<|body_start_1|>
        import numpy as np
        pred = inputs['pred']
        pos = np.where(pred == np.max(pred))
        return {OutputKeys.OUTPUT: {OutputKeys.PREDICTION: pred, OutputKeys.LABEL_POS: pos[0], OutputKeys.LABEL: self.categories[pos[0][0]]}}
<|end_body_1|>

Solution:
class DialogIntentPredictionPipeline:
    def __init__(self, model: Union[SpaceForDialogIntent, str], preprocessor: DialogIntentPredictionPreprocessor=None, **kwargs):
        """Use `model` and `preprocessor` to create a dialog intent prediction pipeline Args: model (str or SpaceForDialogIntent): Supply either a local model dir or a model id from the model hub, or a SpaceForDialogIntent instance. preprocessor (DialogIntentPredictionPreprocessor): An optional preprocessor instance."""
        model = model if isinstance(model, SpaceForDialogIntent) else Model.from_pretrained(model)
        if preprocessor is None:
            preprocessor = DialogIntentPredictionPreprocessor(model.model_dir)
        self.model = model
        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
        self.categories = preprocessor.categories

    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        """process the prediction results Args: inputs (Dict[str, Any]): _description_ Returns: Dict[str, str]: the prediction results"""
        import numpy as np
        pred = inputs['pred']
        pos = np.where(pred == np.max(pred))
        return {OutputKeys.OUTPUT: {OutputKeys.PREDICTION: pred, OutputKeys.LABEL_POS: pos[0], OutputKeys.LABEL: self.categories[pos[0][0]]}}