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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def findMaxForm1(self, strs: List[str], m: int, n: int) -> int: 思路：记忆化递归 @param strs: @param m: @param n: @return:
- def findMaxForm2(self, strs: List[str], m: int, n: int) -> int: 思路：动态规划法 @param strs: @param m: @param n: @return:

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def findMaxForm1(self, strs: List[str], m: int, n: int) -> int: 思路：记忆化递归 @param strs: @param m: @param n: @return:
- def findMaxForm2(self, strs: List[str], m: int, n: int) -> int: 思路：动态规划法 @param strs: @param m: @param n: @return:

<|skeleton|>
class Solution:

    def findMaxForm1(self, strs: List[str], m: int, n: int) -> int:
        """思路：记忆化递归 @param strs: @param m: @param n: @return:"""
        <|body_0|>

    def findMaxForm2(self, strs: List[str], m: int, n: int) -> int:
        """思路：动态规划法 @param strs: @param m: @param n: @return:"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        length = len(strs)
        arr = [(word.count('1'), word.count('0')) for word in strs]
        memo = {}

        def dfs(one, zero, index):
            if index == length:
                return 0
            if (one, zero, index) in memo:
                return memo[one, zero, index]
            ans = 0
            cnt1, cnt0 = arr[index]
            if one + cnt1 <= n and zero + cnt0 <= m:
                ans = 1 + dfs(one + cnt1, zero + cnt0, index + 1)
            ans = max(ans, dfs(one, zero, index + 1))
            memo[one, zero, index] = ans
            return ans
        return dfs(0, 0, 0)
<|end_body_0|>

<|body_start_1|>
        dp = [[0] * (m + 1) for _ in range(n + 1)]
        for word in strs:
            one = word.count('1')
            zero = word.count('0')
            for i in range(n, one - 1, -1):
                for j in range(m, zero - 1, -1):
                    dp[i][j] = max(dp[i][j], dp[i - one][j - zero] + 1)
        return dp[-1][-1]
<|end_body_1|>

Solution:
class Solution:
    def findMaxForm1(self, strs: List[str], m: int, n: int) -> int:
        """思路：记忆化递归 @param strs: @param m: @param n: @return:"""
        length = len(strs)
        arr = [(word.count('1'), word.count('0')) for word in strs]
        memo = {}

        def dfs(one, zero, index):
            if index == length:
                return 0
            if (one, zero, index) in memo:
                return memo[one, zero, index]
            ans = 0
            cnt1, cnt0 = arr[index]
            if one + cnt1 <= n and zero + cnt0 <= m:
                ans = 1 + dfs(one + cnt1, zero + cnt0, index + 1)
            ans = max(ans, dfs(one, zero, index + 1))
            memo[one, zero, index] = ans
            return ans
        return dfs(0, 0, 0)

    def findMaxForm2(self, strs: List[str], m: int, n: int) -> int:
        """思路：动态规划法 @param strs: @param m: @param n: @return:"""
        dp = [[0] * (m + 1) for _ in range(n + 1)]
        for word in strs:
            one = word.count('1')
            zero = word.count('0')
            for i in range(n, one - 1, -1):
                for j in range(m, zero - 1, -1):
                    dp[i][j] = max(dp[i][j], dp[i - one][j - zero] + 1)
        return dp[-1][-1]


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

Class description:
An argument or value is not of the specified type. `DoxhooksTypeError` extends `DoxhooksDataError`. Magic Methods ------------- __str__ Override `DoxhooksDataError.__str__` to compose a message.

Method signatures and docstrings:
- def __init__(self, value, identifier, hint): Initialise the error with parameters of the error message. The `DoxhooksTypeError` constructor overrides the `DoxhooksDataError` constructor with parameters of the error message. Parameters ---------- value The value that caused the error. identifier : str An identifier that the user associates with the value. hint : str, type or Tuple[type, ...] A hint as to the expected type. Attributes ---------- value The argument of `value`. identifier : str The argument of `identifier`. hint : str, type or Tuple[type, ...] The argument of `hint`.
- def __str__(self): Return an error message composed from the error attributes. Overrides `DoxhooksDataError.__str__`. Returns ------- str The error message. Examples -------- >>> from doxhooks.errors import DoxhooksTypeError >>> str(DoxhooksTypeError(1, "my_arg", str)) 'Bad type for `my_arg`: int. (Should be str.)' >>> str(DoxhooksTypeError(1, "my_arg", (str, type(None)))) 'Bad type for `my_arg`: int. (Should be str, NoneType.)' >>> str(DoxhooksTypeError(1, "my_arg", "str or None")) 'Bad type for `my_arg`: int. (Should be str or None.)'

Implement the Python class `DoxhooksTypeError` described below.

Class description:
An argument or value is not of the specified type. `DoxhooksTypeError` extends `DoxhooksDataError`. Magic Methods ------------- __str__ Override `DoxhooksDataError.__str__` to compose a message.

Method signatures and docstrings:
- def __init__(self, value, identifier, hint): Initialise the error with parameters of the error message. The `DoxhooksTypeError` constructor overrides the `DoxhooksDataError` constructor with parameters of the error message. Parameters ---------- value The value that caused the error. identifier : str An identifier that the user associates with the value. hint : str, type or Tuple[type, ...] A hint as to the expected type. Attributes ---------- value The argument of `value`. identifier : str The argument of `identifier`. hint : str, type or Tuple[type, ...] The argument of `hint`.
- def __str__(self): Return an error message composed from the error attributes. Overrides `DoxhooksDataError.__str__`. Returns ------- str The error message. Examples -------- >>> from doxhooks.errors import DoxhooksTypeError >>> str(DoxhooksTypeError(1, "my_arg", str)) 'Bad type for `my_arg`: int. (Should be str.)' >>> str(DoxhooksTypeError(1, "my_arg", (str, type(None)))) 'Bad type for `my_arg`: int. (Should be str, NoneType.)' >>> str(DoxhooksTypeError(1, "my_arg", "str or None")) 'Bad type for `my_arg`: int. (Should be str or None.)'

<|skeleton|>
class DoxhooksTypeError:
    """An argument or value is not of the specified type. `DoxhooksTypeError` extends `DoxhooksDataError`. Magic Methods ------------- __str__ Override `DoxhooksDataError.__str__` to compose a message."""

    def __init__(self, value, identifier, hint):
        """Initialise the error with parameters of the error message. The `DoxhooksTypeError` constructor overrides the `DoxhooksDataError` constructor with parameters of the error message. Parameters ---------- value The value that caused the error. identifier : str An identifier that the user associates with the value. hint : str, type or Tuple[type, ...] A hint as to the expected type. Attributes ---------- value The argument of `value`. identifier : str The argument of `identifier`. hint : str, type or Tuple[type, ...] The argument of `hint`."""
        <|body_0|>

    def __str__(self):
        """Return an error message composed from the error attributes. Overrides `DoxhooksDataError.__str__`. Returns ------- str The error message. Examples -------- >>> from doxhooks.errors import DoxhooksTypeError >>> str(DoxhooksTypeError(1, "my_arg", str)) 'Bad type for `my_arg`: int. (Should be str.)' >>> str(DoxhooksTypeError(1, "my_arg", (str, type(None)))) 'Bad type for `my_arg`: int. (Should be str, NoneType.)' >>> str(DoxhooksTypeError(1, "my_arg", "str or None")) 'Bad type for `my_arg`: int. (Should be str or None.)'"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.value = value
        self.identifier = identifier
        self.hint = hint
<|end_body_0|>

<|body_start_1|>
        hint = self.hint
        if isinstance(hint, type):
            type_hint = hint.__name__
        elif hint == ():
            type_hint = "'no type'"
        elif isinstance(hint, tuple):
            type_hint = ', '.join([type_.__name__ for type_ in hint])
        else:
            type_hint = hint
        bad_type_name = type(self.value).__name__
        return 'Bad type for `{}`: {}. (Should be {}.)'.format(self.identifier, bad_type_name, type_hint)
<|end_body_1|>

Solution:
class DoxhooksTypeError:
    """An argument or value is not of the specified type. `DoxhooksTypeError` extends `DoxhooksDataError`. Magic Methods ------------- __str__ Override `DoxhooksDataError.__str__` to compose a message."""

    def __init__(self, value, identifier, hint):
        """Initialise the error with parameters of the error message. The `DoxhooksTypeError` constructor overrides the `DoxhooksDataError` constructor with parameters of the error message. Parameters ---------- value The value that caused the error. identifier : str An identifier that the user associates with the value. hint : str, type or Tuple[type, ...] A hint as to the expected type. Attributes ---------- value The argument of `value`. identifier : str The argument of `identifier`. hint : str, type or Tuple[type, ...] The argument of `hint`."""
        self.value = value
        self.identifier = identifier
        self.hint = hint

    def __str__(self):
        """Return an error message composed from the error attributes. Overrides `DoxhooksDataError.__str__`. Returns ------- str The error message. Examples -------- >>> from doxhooks.errors import DoxhooksTypeError >>> str(DoxhooksTypeError(1, "my_arg", str)) 'Bad type for `my_arg`: int. (Should be str.)' >>> str(DoxhooksTypeError(1, "my_arg", (str, type(None)))) 'Bad type for `my_arg`: int. (Should be str, NoneType.)' >>> str(DoxhooksTypeError(1, "my_arg", "str or None")) 'Bad type for `my_arg`: int. (Should be str or None.)'"""
        hint = self.hint
        if isinstance(hint, type):
            type_hint = hint.__name__
        elif hint == ():
            type_hint = "'no type'"
        elif isinstance(hint, tuple):
            type_hint = ', '.join([type_.__name__ for type_ in hint])
        else:
            type_hint = hint
        bad_type_name = type(self.value).__name__
        return 'Bad type for `{}`: {}. (Should be {}.)'.format(self.identifier, bad_type_name, type_hint)


Prompt:
Implement the Python class `Test060Describe` described below.

Class description:
Implement the Test060Describe class.

Method signatures and docstrings:
- def test_010_describe_by_invalid_pid(self): 404 NotFound when attempting to get description for non-existing object.
- def test_020_describe_by_valid_pid(self): Successful describe for known objects. - Verify that required headers are present. - Verify that the object length reported by describe matches what was reported by listObjects. - Verify that date header contains a valid date. - Verify that date header matches what was reported by listObjects.

Implement the Python class `Test060Describe` described below.

Class description:
Implement the Test060Describe class.

Method signatures and docstrings:
- def test_010_describe_by_invalid_pid(self): 404 NotFound when attempting to get description for non-existing object.
- def test_020_describe_by_valid_pid(self): Successful describe for known objects. - Verify that required headers are present. - Verify that the object length reported by describe matches what was reported by listObjects. - Verify that date header contains a valid date. - Verify that date header matches what was reported by listObjects.

<|skeleton|>
class Test060Describe:

    def test_010_describe_by_invalid_pid(self):
        """404 NotFound when attempting to get description for non-existing object."""
        <|body_0|>

    def test_020_describe_by_valid_pid(self):
        """Successful describe for known objects. - Verify that required headers are present. - Verify that the object length reported by describe matches what was reported by listObjects. - Verify that date header contains a valid date. - Verify that date header matches what was reported by listObjects."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        client = test_client.TestClient(context.node['baseurl'])
        with pytest.raises(xml.parsers.expat.ExpatError):
            client.describe(context.TOKEN, '_invalid_pid_')
<|end_body_0|>

<|body_start_1|>
        for object_list in context.slices:
            for object_info in object_list.objectInfo:
                client = test_client.TestClient(context.node['baseurl'])
                pid = object_info.identifier.value()
                response = client.describe(context.TOKEN, pid)
                headers = response.getheaders()
                headers_lower = dict(((header.lower(), value) for header, value in headers))
                assert 'date' in headers_lower
                assert 'content-type' in headers_lower
                assert 'content-length' in headers_lower
                assert int(headers_lower['content-length']) == object_info.size
                assert d1_common.date_time.dt_from_iso8601_str(headers_lower['date'])
<|end_body_1|>

Solution:
class Test060Describe:
    def test_010_describe_by_invalid_pid(self):
        """404 NotFound when attempting to get description for non-existing object."""
        client = test_client.TestClient(context.node['baseurl'])
        with pytest.raises(xml.parsers.expat.ExpatError):
            client.describe(context.TOKEN, '_invalid_pid_')

    def test_020_describe_by_valid_pid(self):
        """Successful describe for known objects. - Verify that required headers are present. - Verify that the object length reported by describe matches what was reported by listObjects. - Verify that date header contains a valid date. - Verify that date header matches what was reported by listObjects."""
        for object_list in context.slices:
            for object_info in object_list.objectInfo:
                client = test_client.TestClient(context.node['baseurl'])
                pid = object_info.identifier.value()
                response = client.describe(context.TOKEN, pid)
                headers = response.getheaders()
                headers_lower = dict(((header.lower(), value) for header, value in headers))
                assert 'date' in headers_lower
                assert 'content-type' in headers_lower
                assert 'content-length' in headers_lower
                assert int(headers_lower['content-length']) == object_info.size
                assert d1_common.date_time.dt_from_iso8601_str(headers_lower['date'])


kr

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

Class description:
Triggers when a value changes. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] }

Method signatures and docstrings:
- def ar(cls, source=None, threshold=0): Constructs an audio-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph.
- def kr(cls, source=None, threshold=0): Constructs a control-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.kr( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef e2436271176995c6a0a5cac6d1553f8b { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph.

Implement the Python class `Changed` described below.

Class description:
Triggers when a value changes. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] }

Method signatures and docstrings:
- def ar(cls, source=None, threshold=0): Constructs an audio-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph.
- def kr(cls, source=None, threshold=0): Constructs a control-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.kr( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef e2436271176995c6a0a5cac6d1553f8b { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph.

<|skeleton|>
class Changed:
    """Triggers when a value changes. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] }"""

    def ar(cls, source=None, threshold=0):
        """Constructs an audio-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph."""
        <|body_0|>

    def kr(cls, source=None, threshold=0):
        """Constructs a control-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.kr( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef e2436271176995c6a0a5cac6d1553f8b { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        from supriya.tools import ugentools
        ugen = abs(ugentools.HPZ1.ar(source=source)) > threshold
        return ugen
<|end_body_0|>

<|body_start_1|>
        from supriya.tools import ugentools
        ugen = abs(ugentools.HPZ1.kr(source=source)) > threshold
        return ugen
<|end_body_1|>

Solution:
class Changed:
    """Triggers when a value changes. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] }"""

    def ar(cls, source=None, threshold=0):
        """Constructs an audio-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.ar( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef 39e1f9d61589c4acaaf297cc961d65e4 { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph."""
        from supriya.tools import ugentools
        ugen = abs(ugentools.HPZ1.ar(source=source)) > threshold
        return ugen

    def kr(cls, source=None, threshold=0):
        """Constructs a control-rate Changed. :: >>> source = ugentools.In.ar(bus=0) >>> changed = ugentools.Changed.kr( ... source=source, ... threshold=0, ... ) >>> print(str(changed)) SynthDef e2436271176995c6a0a5cac6d1553f8b { const_0:0.0 -> 0_In[0:bus] 0_In[0] -> 1_HPZ1[0:source] 1_HPZ1[0] -> 2_UnaryOpUGen:ABSOLUTE_VALUE[0:source] 2_UnaryOpUGen:ABSOLUTE_VALUE[0] -> 3_BinaryOpUGen:GREATER_THAN[0:left] const_0:0.0 -> 3_BinaryOpUGen:GREATER_THAN[1:right] } Returns ugen graph."""
        from supriya.tools import ugentools
        ugen = abs(ugentools.HPZ1.kr(source=source)) > threshold
        return ugen


testFunctionMemoizedBasedOnArgs

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

Class description:
Implement the MemoizeDecoratorTest class.

Method signatures and docstrings:
- def testFunctionExceptionNotMemoized(self): Tests that |Memoize| decorator does not cache exception results.
- def testFunctionResultMemoized(self): Tests that |Memoize| decorator caches results.
- def testFunctionMemoizedBasedOnArgs(self): Tests that |Memoize| caches results based on args and kwargs.

Implement the Python class `MemoizeDecoratorTest` described below.

Class description:
Implement the MemoizeDecoratorTest class.

Method signatures and docstrings:
- def testFunctionExceptionNotMemoized(self): Tests that |Memoize| decorator does not cache exception results.
- def testFunctionResultMemoized(self): Tests that |Memoize| decorator caches results.
- def testFunctionMemoizedBasedOnArgs(self): Tests that |Memoize| caches results based on args and kwargs.

<|skeleton|>
class MemoizeDecoratorTest:

    def testFunctionExceptionNotMemoized(self):
        """Tests that |Memoize| decorator does not cache exception results."""
        <|body_0|>

    def testFunctionResultMemoized(self):
        """Tests that |Memoize| decorator caches results."""
        <|body_1|>

    def testFunctionMemoizedBasedOnArgs(self):
        """Tests that |Memoize| caches results based on args and kwargs."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        class ExceptionType1(Exception):
            pass

        class ExceptionType2(Exception):
            pass

        @decorators.Memoize
        def raiseExceptions():
            if raiseExceptions.count == 0:
                raiseExceptions.count += 1
                raise ExceptionType1()
            if raiseExceptions.count == 1:
                raise ExceptionType2()
        raiseExceptions.count = 0
        with self.assertRaises(ExceptionType1):
            raiseExceptions()
        with self.assertRaises(ExceptionType2):
            raiseExceptions()
<|end_body_0|>

<|body_start_1|>
        @decorators.Memoize
        def memoized():
            memoized.count += 1
            return memoized.count
        memoized.count = 0

        def notMemoized():
            notMemoized.count += 1
            return notMemoized.count
        notMemoized.count = 0
        self.assertEquals(memoized(), 1)
        self.assertEquals(memoized(), 1)
        self.assertEquals(memoized(), 1)
        self.assertEquals(notMemoized(), 1)
        self.assertEquals(notMemoized(), 2)
        self.assertEquals(notMemoized(), 3)
<|end_body_1|>

<|body_start_2|>
        @decorators.Memoize
        def returnValueBasedOnArgsKwargs(a, k=0):
            return a + k
        self.assertEquals(returnValueBasedOnArgsKwargs(1, 1), 2)
        self.assertEquals(returnValueBasedOnArgsKwargs(1, 2), 3)
        self.assertEquals(returnValueBasedOnArgsKwargs(2, 1), 3)
        self.assertEquals(returnValueBasedOnArgsKwargs(3, 3), 6)
<|end_body_2|>

Solution:
class MemoizeDecoratorTest:
    def testFunctionExceptionNotMemoized(self):
        """Tests that |Memoize| decorator does not cache exception results."""
        class ExceptionType1(Exception):
            pass

        class ExceptionType2(Exception):
            pass

        @decorators.Memoize
        def raiseExceptions():
            if raiseExceptions.count == 0:
                raiseExceptions.count += 1
                raise ExceptionType1()
            if raiseExceptions.count == 1:
                raise ExceptionType2()
        raiseExceptions.count = 0
        with self.assertRaises(ExceptionType1):
            raiseExceptions()
        with self.assertRaises(ExceptionType2):
            raiseExceptions()

    def testFunctionResultMemoized(self):
        """Tests that |Memoize| decorator caches results."""
        @decorators.Memoize
        def memoized():
            memoized.count += 1
            return memoized.count
        memoized.count = 0

        def notMemoized():
            notMemoized.count += 1
            return notMemoized.count
        notMemoized.count = 0
        self.assertEquals(memoized(), 1)
        self.assertEquals(memoized(), 1)
        self.assertEquals(memoized(), 1)
        self.assertEquals(notMemoized(), 1)
        self.assertEquals(notMemoized(), 2)
        self.assertEquals(notMemoized(), 3)

    def testFunctionMemoizedBasedOnArgs(self):
        """Tests that |Memoize| caches results based on args and kwargs."""
        @decorators.Memoize
        def returnValueBasedOnArgsKwargs(a, k=0):
            return a + k
        self.assertEquals(returnValueBasedOnArgsKwargs(1, 1), 2)
        self.assertEquals(returnValueBasedOnArgsKwargs(1, 2), 3)
        self.assertEquals(returnValueBasedOnArgsKwargs(2, 1), 3)
        self.assertEquals(returnValueBasedOnArgsKwargs(3, 3), 6)


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

Class description:
Implement the EducationStateDetailsViewTest class.

Method signatures and docstrings:
- def test_no_data(self): Make sure each state page renders if there is no database data.
- def test_with_null_data(self): Make sure each state page renders if there is data in the database.
- def test_with_data(self): Make sure each page renders if there is non-null data in the databasese

Implement the Python class `EducationStateDetailsViewTest` described below.

Class description:
Implement the EducationStateDetailsViewTest class.

Method signatures and docstrings:
- def test_no_data(self): Make sure each state page renders if there is no database data.
- def test_with_null_data(self): Make sure each state page renders if there is data in the database.
- def test_with_data(self): Make sure each page renders if there is non-null data in the databasese

<|skeleton|>
class EducationStateDetailsViewTest:

    def test_no_data(self):
        """Make sure each state page renders if there is no database data."""
        <|body_0|>

    def test_with_null_data(self):
        """Make sure each state page renders if there is data in the database."""
        <|body_1|>

    def test_with_data(self):
        """Make sure each page renders if there is non-null data in the databasese"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        for s in STATES:
            response = self.client.get(reverse('education:state_detail', args=(s,)))
            self.assertEqual(response.status_code, 200)
            self.assertNotEqual(response.context.get('message'), None)
            self.assertContains(response, 'Error: No data for state {}'.format(s))
<|end_body_0|>

<|body_start_1|>
        create_null_states()
        for s in STATES:
            response = self.client.get(reverse('education:state_detail', args=(s,)))
            self.assertEqual(response.status_code, 200)
            self.assertNotEqual(response.context.get('data'), None)
            self.assertNotEqual(response.context.get('json_data'), None)
            self.assertContains(response, 'Students in 15-16 Cohort')
            self.assertNotContains(response, '>Native American	')
<|end_body_1|>

<|body_start_2|>
        create_states()
        for s in STATES:
            response = self.client.get(reverse('education:state_detail', args=(s,)))
            self.assertEqual(response.status_code, 200)
            self.assertNotEqual(response.context.get('data'), None)
            self.assertNotEqual(response.context.get('json_data'), None)
            self.assertContains(response, 'Students in 15-16 Cohort')
            self.assertContains(response, '>Native American	')
<|end_body_2|>

Solution:
class EducationStateDetailsViewTest:
    def test_no_data(self):
        """Make sure each state page renders if there is no database data."""
        for s in STATES:
            response = self.client.get(reverse('education:state_detail', args=(s,)))
            self.assertEqual(response.status_code, 200)
            self.assertNotEqual(response.context.get('message'), None)
            self.assertContains(response, 'Error: No data for state {}'.format(s))

    def test_with_null_data(self):
        """Make sure each state page renders if there is data in the database."""
        create_null_states()
        for s in STATES:
            response = self.client.get(reverse('education:state_detail', args=(s,)))
            self.assertEqual(response.status_code, 200)
            self.assertNotEqual(response.context.get('data'), None)
            self.assertNotEqual(response.context.get('json_data'), None)
            self.assertContains(response, 'Students in 15-16 Cohort')
            self.assertNotContains(response, '>Native American	')

    def test_with_data(self):
        """Make sure each page renders if there is non-null data in the databasese"""
        create_states()
        for s in STATES:
            response = self.client.get(reverse('education:state_detail', args=(s,)))
            self.assertEqual(response.status_code, 200)
            self.assertNotEqual(response.context.get('data'), None)
            self.assertNotEqual(response.context.get('json_data'), None)
            self.assertContains(response, 'Students in 15-16 Cohort')
            self.assertContains(response, '>Native American	')


Args

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

Class description:
Upload an RPM package to an artifact repository.

Method signatures and docstrings:
- def Args(parser): Set up arguements for this command. Args: parser: An argparse.ArgumentPaser.
- def Run(self, args): Run package import command.

Implement the Python class `Upload` described below.

Class description:
Upload an RPM package to an artifact repository.

Method signatures and docstrings:
- def Args(parser): Set up arguements for this command. Args: parser: An argparse.ArgumentPaser.
- def Run(self, args): Run package import command.

<|skeleton|>
class Upload:
    """Upload an RPM package to an artifact repository."""

    def Args(parser):
        """Set up arguements for this command. Args: parser: An argparse.ArgumentPaser."""
        <|body_0|>

    def Run(self, args):
        """Run package import command."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        flags.GetRepoArg().AddToParser(parser)
        base.ASYNC_FLAG.AddToParser(parser)
        parser.add_argument('--source', metavar='SOURCE', required=True, help='            The path of a package to upload.')
<|end_body_0|>

<|body_start_1|>
        client = apis.GetClientInstance('artifactregistry', self.api_version)
        messages = client.MESSAGES_MODULE
        client.additional_http_headers['X-Goog-Upload-Protocol'] = 'multipart'
        repo_ref = args.CONCEPTS.repository.Parse()
        upload_req = messages.UploadYumArtifactRequest
        upload_request = upload_req()
        request = messages.ArtifactregistryProjectsLocationsRepositoriesYumArtifactsUploadRequest(uploadYumArtifactRequest=upload_request, parent=repo_ref.RelativeName())
        upload = transfer.Upload.FromFile(args.source, mime_type='application/x-rpm')
        op_obj = client.projects_locations_repositories_yumArtifacts.Upload(request, upload=upload)
        op = op_obj.operation
        op_ref = resources.REGISTRY.ParseRelativeName(op.name, collection='artifactregistry.projects.locations.operations')
        if args.async_:
            return op_ref
        else:
            result = waiter.WaitFor(waiter.CloudOperationPollerNoResources(client.projects_locations_operations), op_ref, 'Uploading package')
            return result
<|end_body_1|>

Solution:
class Upload:
    """Upload an RPM package to an artifact repository."""

    def Args(parser):
        """Set up arguements for this command. Args: parser: An argparse.ArgumentPaser."""
        flags.GetRepoArg().AddToParser(parser)
        base.ASYNC_FLAG.AddToParser(parser)
        parser.add_argument('--source', metavar='SOURCE', required=True, help='            The path of a package to upload.')

    def Run(self, args):
        """Run package import command."""
        client = apis.GetClientInstance('artifactregistry', self.api_version)
        messages = client.MESSAGES_MODULE
        client.additional_http_headers['X-Goog-Upload-Protocol'] = 'multipart'
        repo_ref = args.CONCEPTS.repository.Parse()
        upload_req = messages.UploadYumArtifactRequest
        upload_request = upload_req()
        request = messages.ArtifactregistryProjectsLocationsRepositoriesYumArtifactsUploadRequest(uploadYumArtifactRequest=upload_request, parent=repo_ref.RelativeName())
        upload = transfer.Upload.FromFile(args.source, mime_type='application/x-rpm')
        op_obj = client.projects_locations_repositories_yumArtifacts.Upload(request, upload=upload)
        op = op_obj.operation
        op_ref = resources.REGISTRY.ParseRelativeName(op.name, collection='artifactregistry.projects.locations.operations')
        if args.async_:
            return op_ref
        else:
            result = waiter.WaitFor(waiter.CloudOperationPollerNoResources(client.projects_locations_operations), op_ref, 'Uploading package')
            return result


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

Class description:
Class for creating a measurement pattern. This class represents the measurement pattern in the MBQC model. Please refer to [The measurement calculus, arXiv: 0704.1263] for technical details. Attributes: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list

Method signatures and docstrings:
- def __init__(self, name: str, space: list, input_: list, output_: list, commands: list): Constructor for Pattern class. Args: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list
- def print(self) -> None: Print all commands in the command list.

Implement the Python class `Pattern` described below.

Class description:
Class for creating a measurement pattern. This class represents the measurement pattern in the MBQC model. Please refer to [The measurement calculus, arXiv: 0704.1263] for technical details. Attributes: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list

Method signatures and docstrings:
- def __init__(self, name: str, space: list, input_: list, output_: list, commands: list): Constructor for Pattern class. Args: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list
- def print(self) -> None: Print all commands in the command list.

<|skeleton|>
class Pattern:
    """Class for creating a measurement pattern. This class represents the measurement pattern in the MBQC model. Please refer to [The measurement calculus, arXiv: 0704.1263] for technical details. Attributes: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list"""

    def __init__(self, name: str, space: list, input_: list, output_: list, commands: list):
        """Constructor for Pattern class. Args: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list"""
        <|body_0|>

    def print(self) -> None:
        """Print all commands in the command list."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.name = name
        self.space = space
        self.input_ = input_
        self.output_ = output_
        self.commands = commands
<|end_body_0|>

<|body_start_1|>
        print('-----------------------------------------------------------')
        print('                    Current Command List                   ')
        print('-----------------------------------------------------------')
        for cmd in self.commands:
            print('\x1b[91m' + 'Command:'.ljust(16) + cmd.name + '\x1b[0m')
            if cmd.name == 'E':
                print('which_qubits:'.ljust(15), cmd.which_qubits)
            elif cmd.name == 'M':
                print('which_qubit:'.ljust(15), cmd.which_qubit)
                print('plane:'.ljust(15), cmd.plane)
                print('angle:'.ljust(15), cmd.angle)
                print('domain_s:'.ljust(15), cmd.domain_s)
                print('domain_t:'.ljust(15), cmd.domain_t)
            else:
                print('which_qubit:'.ljust(15), cmd.which_qubit)
                print('domain:'.ljust(15), cmd.domain)
            print('-----------------------------------------------------------')
<|end_body_1|>

Solution:
class Pattern:
    """Class for creating a measurement pattern. This class represents the measurement pattern in the MBQC model. Please refer to [The measurement calculus, arXiv: 0704.1263] for technical details. Attributes: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list"""

    def __init__(self, name: str, space: list, input_: list, output_: list, commands: list):
        """Constructor for Pattern class. Args: name (str): pattern name space (list): space vertices input_ (list): input vertices output_ (list): output vertices commands (list): command list"""
        self.name = name
        self.space = space
        self.input_ = input_
        self.output_ = output_
        self.commands = commands

    def print(self) -> None:
        """Print all commands in the command list."""
        print('-----------------------------------------------------------')
        print('                    Current Command List                   ')
        print('-----------------------------------------------------------')
        for cmd in self.commands:
            print('\x1b[91m' + 'Command:'.ljust(16) + cmd.name + '\x1b[0m')
            if cmd.name == 'E':
                print('which_qubits:'.ljust(15), cmd.which_qubits)
            elif cmd.name == 'M':
                print('which_qubit:'.ljust(15), cmd.which_qubit)
                print('plane:'.ljust(15), cmd.plane)
                print('angle:'.ljust(15), cmd.angle)
                print('domain_s:'.ljust(15), cmd.domain_s)
                print('domain_t:'.ljust(15), cmd.domain_t)
            else:
                print('which_qubit:'.ljust(15), cmd.which_qubit)
                print('domain:'.ljust(15), cmd.domain)
            print('-----------------------------------------------------------')


lowestCommonAncestor

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def lowestCommonAncestor(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode
- def ancestor(self, root, dis): 根据祖先找到目标节点

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def lowestCommonAncestor(self, root, p, q): :type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode
- def ancestor(self, root, dis): 根据祖先找到目标节点

<|skeleton|>
class Solution:

    def lowestCommonAncestor(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        <|body_0|>

    def ancestor(self, root, dis):
        """根据祖先找到目标节点"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if not root:
            return None
        P = self.ancestor(root, p)
        Q = self.ancestor(root, q)
        h = min(len(P), len(Q))
        for i in xrange(h):
            if P[i] is Q[i]:
                result = P[i]
            else:
                break
        return result
<|end_body_0|>

<|body_start_1|>
        if not root:
            return []
        if root is dis:
            return [dis]
        l = self.ancestor(root.left, dis)
        r = self.ancestor(root.right, dis)
        if not l + r:
            return []
        return [root] + l + r
<|end_body_1|>

Solution:
class Solution:
    def lowestCommonAncestor(self, root, p, q):
        """:type root: TreeNode :type p: TreeNode :type q: TreeNode :rtype: TreeNode"""
        if not root:
            return None
        P = self.ancestor(root, p)
        Q = self.ancestor(root, q)
        h = min(len(P), len(Q))
        for i in xrange(h):
            if P[i] is Q[i]:
                result = P[i]
            else:
                break
        return result

    def ancestor(self, root, dis):
        """根据祖先找到目标节点"""
        if not root:
            return []
        if root is dis:
            return [dis]
        l = self.ancestor(root.left, dis)
        r = self.ancestor(root.right, dis)
        if not l + r:
            return []
        return [root] + l + r


cast

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

Class description:
Implement the Input class.

Method signatures and docstrings:
- def stdin(self, sys_stdin): Imports standard input. :param _io.TextIOWrapper sys_stdin: standard input :return: root node of binary tree :rtype: TreeNode object
- def cast(self, x): Converts string values to integer or None values. :param str x: string input parameter :return: converted integer or None value :rtype: int or None

Implement the Python class `Input` described below.

Class description:
Implement the Input class.

Method signatures and docstrings:
- def stdin(self, sys_stdin): Imports standard input. :param _io.TextIOWrapper sys_stdin: standard input :return: root node of binary tree :rtype: TreeNode object
- def cast(self, x): Converts string values to integer or None values. :param str x: string input parameter :return: converted integer or None value :rtype: int or None

<|skeleton|>
class Input:

    def stdin(self, sys_stdin):
        """Imports standard input. :param _io.TextIOWrapper sys_stdin: standard input :return: root node of binary tree :rtype: TreeNode object"""
        <|body_0|>

    def cast(self, x):
        """Converts string values to integer or None values. :param str x: string input parameter :return: converted integer or None value :rtype: int or None"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        inputs = [x.strip('[]"\n') for x in sys_stdin]
        a = [self.cast(x) for x in inputs[0].split(',')]
        o = TreeNode().convert(a)
        x = int(inputs[1])
        return (o, x)
<|end_body_0|>

<|body_start_1|>
        if x.lower() == 'null':
            return None
        else:
            return int(x)
<|end_body_1|>

Solution:
class Input:
    def stdin(self, sys_stdin):
        """Imports standard input. :param _io.TextIOWrapper sys_stdin: standard input :return: root node of binary tree :rtype: TreeNode object"""
        inputs = [x.strip('[]"\n') for x in sys_stdin]
        a = [self.cast(x) for x in inputs[0].split(',')]
        o = TreeNode().convert(a)
        x = int(inputs[1])
        return (o, x)

    def cast(self, x):
        """Converts string values to integer or None values. :param str x: string input parameter :return: converted integer or None value :rtype: int or None"""
        if x.lower() == 'null':
            return None
        else:
            return int(x)


mergeTwoLists

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode
- def mergeTwoLists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode
- def mergeTwoLists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode

<|skeleton|>
class Solution:

    def mergeKLists(self, lists):
        """:type lists: List[ListNode] :rtype: ListNode"""
        <|body_0|>

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

<|end_skeleton|>

<|body_start_0|>
        if not lists:
            return None
        if len(lists) == 1:
            return lists[0]
        if len(lists) == 2:
            return self.mergeTwoLists(lists[0], lists[1])
        return self.mergeTwoLists(self.mergeKLists(lists[:len(lists) / 2]), self.mergeKLists(lists[len(lists) / 2:]))
<|end_body_0|>

<|body_start_1|>
        res = ListNode(0)
        last = res
        while l1 or l2:
            if l1 == None:
                last.next = l2
                return res.next
            if l2 == None:
                last.next = l1
                return res.next
            if l1.val < l2.val:
                last.next = l1
                last = last.next
                l1 = l1.next
            else:
                last.next = l2
                last = last.next
                l2 = l2.next
        return res.next
<|end_body_1|>

Solution:
class Solution:
    def mergeKLists(self, lists):
        """:type lists: List[ListNode] :rtype: ListNode"""
        if not lists:
            return None
        if len(lists) == 1:
            return lists[0]
        if len(lists) == 2:
            return self.mergeTwoLists(lists[0], lists[1])
        return self.mergeTwoLists(self.mergeKLists(lists[:len(lists) / 2]), self.mergeKLists(lists[len(lists) / 2:]))

    def mergeTwoLists(self, l1, l2):
        """:type l1: ListNode :type l2: ListNode :rtype: ListNode"""
        res = ListNode(0)
        last = res
        while l1 or l2:
            if l1 == None:
                last.next = l2
                return res.next
            if l2 == None:
                last.next = l1
                return res.next
            if l1.val < l2.val:
                last.next = l1
                last = last.next
                l1 = l1.next
            else:
                last.next = l2
                last = last.next
                l2 = l2.next
        return res.next


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

Class description:
Implement the employee_away_history class.

Method signatures and docstrings:
- def set_date_from(self): This method is used to set the Date From field based on Leave Button ------------------------------------------------
- def set_date_to(self): This method is used to set the Date To field based on Return Button ------------------------------------------------

Implement the Python class `employee_away_history` described below.

Class description:
Implement the employee_away_history class.

Method signatures and docstrings:
- def set_date_from(self): This method is used to set the Date From field based on Leave Button ------------------------------------------------
- def set_date_to(self): This method is used to set the Date To field based on Return Button ------------------------------------------------

<|skeleton|>
class employee_away_history:

    def set_date_from(self):
        """This method is used to set the Date From field based on Leave Button ------------------------------------------------"""
        <|body_0|>

    def set_date_to(self):
        """This method is used to set the Date To field based on Return Button ------------------------------------------------"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        cr, uid, context = self.env.args
        context = dict(context)
        for rec in self:
            rec.write({'date_from': time.strftime(DEFAULT_SERVER_DATE_FORMAT)})
            emp_rec = self.env['hr.employee'].search([('id', '=', rec.emp_id.id)])
            if emp_rec:
                emp_rec.write({'away': True})
        return True
<|end_body_0|>

<|body_start_1|>
        cr, uid, context = self.env.args
        context = dict(context)
        for rec in self:
            rec.write({'date_to': time.strftime(DEFAULT_SERVER_DATE_FORMAT)})
            emp_rec = self.env['hr.employee'].search([('id', '=', rec.emp_id.id)])
            if emp_rec:
                emp_rec.write({'away': False})
        return True
<|end_body_1|>

Solution:
class employee_away_history:
    def set_date_from(self):
        """This method is used to set the Date From field based on Leave Button ------------------------------------------------"""
        cr, uid, context = self.env.args
        context = dict(context)
        for rec in self:
            rec.write({'date_from': time.strftime(DEFAULT_SERVER_DATE_FORMAT)})
            emp_rec = self.env['hr.employee'].search([('id', '=', rec.emp_id.id)])
            if emp_rec:
                emp_rec.write({'away': True})
        return True

    def set_date_to(self):
        """This method is used to set the Date To field based on Return Button ------------------------------------------------"""
        cr, uid, context = self.env.args
        context = dict(context)
        for rec in self:
            rec.write({'date_to': time.strftime(DEFAULT_SERVER_DATE_FORMAT)})
            emp_rec = self.env['hr.employee'].search([('id', '=', rec.emp_id.id)])
            if emp_rec:
                emp_rec.write({'away': False})
        return True


predict

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

Class description:
Implement the KNN class.

Method signatures and docstrings:
- def __init__(self, train_x, train_class): Initialize the data set that classification will be based on.
- def predict(self, x_data, k_neighbors): Find the nearest k-neighbors and classify the new set of data points.

Implement the Python class `KNN` described below.

Class description:
Implement the KNN class.

Method signatures and docstrings:
- def __init__(self, train_x, train_class): Initialize the data set that classification will be based on.
- def predict(self, x_data, k_neighbors): Find the nearest k-neighbors and classify the new set of data points.

<|skeleton|>
class KNN:

    def __init__(self, train_x, train_class):
        """Initialize the data set that classification will be based on."""
        <|body_0|>

    def predict(self, x_data, k_neighbors):
        """Find the nearest k-neighbors and classify the new set of data points."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.train_x = train_x
        self.train_class = train_class
        return
<|end_body_0|>

<|body_start_1|>
        pred_class = np.zeros(x_data.shape[0])
        for i, x in enumerate(x_data):
            distancesq = ((x - self.train_x) ** 2).sum(axis=1)
            nn_ind = np.argsort(distancesq)[:k_neighbors]
            nn_class, counts = np.unique(self.train_class[nn_ind], return_counts=True)
            pred_class[i] += nn_class[np.argmax(counts)]
        return pred_class
<|end_body_1|>

Solution:
class KNN:
    def __init__(self, train_x, train_class):
        """Initialize the data set that classification will be based on."""
        self.train_x = train_x
        self.train_class = train_class
        return

    def predict(self, x_data, k_neighbors):
        """Find the nearest k-neighbors and classify the new set of data points."""
        pred_class = np.zeros(x_data.shape[0])
        for i, x in enumerate(x_data):
            distancesq = ((x - self.train_x) ** 2).sum(axis=1)
            nn_ind = np.argsort(distancesq)[:k_neighbors]
            nn_class, counts = np.unique(self.train_class[nn_ind], return_counts=True)
            pred_class[i] += nn_class[np.argmax(counts)]
        return pred_class


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

Class description:
Implement the IndexViewTests class.

Method signatures and docstrings:
- def test_no_listings(self): If no listings exist, none are displayed on the index page.
- def test_display_listings(self): Index view displays all created listings.

Implement the Python class `IndexViewTests` described below.

Class description:
Implement the IndexViewTests class.

Method signatures and docstrings:
- def test_no_listings(self): If no listings exist, none are displayed on the index page.
- def test_display_listings(self): Index view displays all created listings.

<|skeleton|>
class IndexViewTests:

    def test_no_listings(self):
        """If no listings exist, none are displayed on the index page."""
        <|body_0|>

    def test_display_listings(self):
        """Index view displays all created listings."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        response = self.client.get(reverse('index'))
        self.assertEqual(response.status_code, 200)
        self.assertQuerysetEqual(response.context['listings'], [])
<|end_body_0|>

<|body_start_1|>
        listing = Listing.objects.create()
        response = self.client.get(reverse('index'))
        self.assertQuerysetEqual(response.context['listings'], [listing])
<|end_body_1|>

Solution:
class IndexViewTests:
    def test_no_listings(self):
        """If no listings exist, none are displayed on the index page."""
        response = self.client.get(reverse('index'))
        self.assertEqual(response.status_code, 200)
        self.assertQuerysetEqual(response.context['listings'], [])

    def test_display_listings(self):
        """Index view displays all created listings."""
        listing = Listing.objects.create()
        response = self.client.get(reverse('index'))
        self.assertQuerysetEqual(response.context['listings'], [listing])


predict

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

Class description:
Classifier can be single-task, or multi-task.

Method signatures and docstrings:
- def __init__(self, params, task_labels='majority'): Creates a Classifier instance for predicting task_labels. Args: params: a Params instance which includes the hyperparameters of the model task_labels: list of label names to be predicted from text.
- def create(self, labels=1): Creates a single-task, multi-task or multi-label classifier. Args: labels: shows the number of labels for the output. If more than 1, the model is multi-label. The length of self.task_labels shows whether the model is a single-task or multi-task.
- def train_model(self, train_batches, val_batches, loss_function, weights): Trains and validates a classifier on the input batches. Args: train_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() val_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() loss_function: a function for calculating the loss value during training weights: a dictionary of weights for each task. Returns: the number of training epochs before early stopping.
- def predict(self, batches): Predicts the outputs for each task_label. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label
- def mc_predict(self, batches): Uses the trained models for mc_pass iterations to calculate uncertainty. Each iteration is performed with dropouts, so the predictions vary. Based on Gal and Ghahramani, 2016. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label

Implement the Python class `Classifier` described below.

Class description:
Classifier can be single-task, or multi-task.

Method signatures and docstrings:
- def __init__(self, params, task_labels='majority'): Creates a Classifier instance for predicting task_labels. Args: params: a Params instance which includes the hyperparameters of the model task_labels: list of label names to be predicted from text.
- def create(self, labels=1): Creates a single-task, multi-task or multi-label classifier. Args: labels: shows the number of labels for the output. If more than 1, the model is multi-label. The length of self.task_labels shows whether the model is a single-task or multi-task.
- def train_model(self, train_batches, val_batches, loss_function, weights): Trains and validates a classifier on the input batches. Args: train_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() val_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() loss_function: a function for calculating the loss value during training weights: a dictionary of weights for each task. Returns: the number of training epochs before early stopping.
- def predict(self, batches): Predicts the outputs for each task_label. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label
- def mc_predict(self, batches): Uses the trained models for mc_pass iterations to calculate uncertainty. Each iteration is performed with dropouts, so the predictions vary. Based on Gal and Ghahramani, 2016. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label

<|skeleton|>
class Classifier:
    """Classifier can be single-task, or multi-task."""

    def __init__(self, params, task_labels='majority'):
        """Creates a Classifier instance for predicting task_labels. Args: params: a Params instance which includes the hyperparameters of the model task_labels: list of label names to be predicted from text."""
        <|body_0|>

    def create(self, labels=1):
        """Creates a single-task, multi-task or multi-label classifier. Args: labels: shows the number of labels for the output. If more than 1, the model is multi-label. The length of self.task_labels shows whether the model is a single-task or multi-task."""
        <|body_1|>

    def train_model(self, train_batches, val_batches, loss_function, weights):
        """Trains and validates a classifier on the input batches. Args: train_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() val_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() loss_function: a function for calculating the loss value during training weights: a dictionary of weights for each task. Returns: the number of training epochs before early stopping."""
        <|body_2|>

    def predict(self, batches):
        """Predicts the outputs for each task_label. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label"""
        <|body_3|>

    def mc_predict(self, batches):
        """Uses the trained models for mc_pass iterations to calculate uncertainty. Each iteration is performed with dropouts, so the predictions vary. Based on Gal and Ghahramani, 2016. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.params = params
        self.task_labels = task_labels
        self.cache_dir = tempfile.gettempdir()
<|end_body_0|>

<|body_start_1|>
        tf.compat.v1.reset_default_graph()
        config = transformers.BertConfig.from_pretrained(os.path.join(self.params.bert_path, 'config.json'), cache_dir=self.cache_dir)
        model = transformers.TFBertModel.from_pretrained(os.path.join(self.params.bert_path, 'tf_model.h5'), config=config, cache_dir=self.cache_dir)
        inputs = tf.keras.layers.Input(shape=(int(self.params.max_l),), dtype=tf.int64, name='inputs')
        atten = tf.keras.layers.Input(shape=(int(self.params.max_l),), dtype=tf.int64, name='atten')
        hidden = model([inputs, atten])[1]
        drop_hidden = tf.keras.layers.Dropout(0.1)(hidden, training=self.params.mc_dropout)
        logits = dict()
        for task_label in self.task_labels:
            logits[task_label] = tf.keras.layers.Dense(labels, activation='sigmoid', name=task_label)(drop_hidden)
        self.tf_model = tf.keras.Model({'inputs': inputs, 'atten': atten}, logits)
<|end_body_1|>

<|body_start_2|>
        cb_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=self.params.early_stopping_check, min_delta=self.params.min_epoch_change)
        cb_scheduler = tf.keras.callbacks.LearningRateScheduler(utils.reduce_learning_rate)
        self.tf_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=self.params.lr), loss=loss_function)
        y = {task_label: train_batches[task_label].replace(np.nan, -1) for task_label in self.task_labels}
        val_y = {task_label: val_batches[task_label].replace(np.nan, -1) for task_label in self.task_labels}
        history = self.tf_model.fit(x={'inputs': train_batches['inputs'], 'atten': train_batches['attentions']}, y=y, epochs=self.params.n_epoch, callbacks=[cb_early, cb_scheduler], validation_data=({'inputs': val_batches['inputs'], 'atten': val_batches['attentions']}, val_y), class_weight=weights)
        return len(history.history['loss'])
<|end_body_2|>

<|body_start_3|>
        results = collections.defaultdict(list)
        if 'majority' in batches.keys():
            results['majority'] = batches['majority']
        if 'text_id' in batches.keys():
            results['text_id'] = batches['text_id']
        logits = self.tf_model.predict(x={'inputs': batches['inputs'], 'atten': batches['attentions']})
        for i, task_label in enumerate(self.task_labels):
            predictions = utils.to_binary(logits[i] if len(self.task_labels) > 1 else logits)
            results[task_label + '_pred'] = predictions
            if task_label in batches.keys():
                results[task_label + '_label'] = batches[task_label]
            if len(self.task_labels) == 1:
                results[task_label + '_logit'] = logits.flatten()
        return pd.DataFrame(results)
<|end_body_3|>

<|body_start_4|>
        results = collections.defaultdict(list)
        dropout_predictions = np.empty((0, batches['inputs'].shape[0], 1))
        for i, task_label in enumerate(self.task_labels):
            for _ in range(self.params.mc_passes):
                logits = self.tf_model.predict(x={'inputs': batches['inputs'], 'atten': batches['attentions']})
                mc_predictions = utils.to_binary(logits[i] if len(self.task_labels) > 1 else logits)
                dropout_predictions = np.vstack((dropout_predictions, mc_predictions[np.newaxis, :, np.newaxis]))
            results[task_label + '_mean'] = list(np.squeeze(np.mean(dropout_predictions, axis=0)))
            results[task_label + '_variance'] = list(np.squeeze(np.var(dropout_predictions, axis=0)))
        return pd.DataFrame(results)
<|end_body_4|>

Solution:
class Classifier:
    """Classifier can be single-task, or multi-task."""

    def __init__(self, params, task_labels='majority'):
        """Creates a Classifier instance for predicting task_labels. Args: params: a Params instance which includes the hyperparameters of the model task_labels: list of label names to be predicted from text."""
        self.params = params
        self.task_labels = task_labels
        self.cache_dir = tempfile.gettempdir()

    def create(self, labels=1):
        """Creates a single-task, multi-task or multi-label classifier. Args: labels: shows the number of labels for the output. If more than 1, the model is multi-label. The length of self.task_labels shows whether the model is a single-task or multi-task."""
        tf.compat.v1.reset_default_graph()
        config = transformers.BertConfig.from_pretrained(os.path.join(self.params.bert_path, 'config.json'), cache_dir=self.cache_dir)
        model = transformers.TFBertModel.from_pretrained(os.path.join(self.params.bert_path, 'tf_model.h5'), config=config, cache_dir=self.cache_dir)
        inputs = tf.keras.layers.Input(shape=(int(self.params.max_l),), dtype=tf.int64, name='inputs')
        atten = tf.keras.layers.Input(shape=(int(self.params.max_l),), dtype=tf.int64, name='atten')
        hidden = model([inputs, atten])[1]
        drop_hidden = tf.keras.layers.Dropout(0.1)(hidden, training=self.params.mc_dropout)
        logits = dict()
        for task_label in self.task_labels:
            logits[task_label] = tf.keras.layers.Dense(labels, activation='sigmoid', name=task_label)(drop_hidden)
        self.tf_model = tf.keras.Model({'inputs': inputs, 'atten': atten}, logits)

    def train_model(self, train_batches, val_batches, loss_function, weights):
        """Trains and validates a classifier on the input batches. Args: train_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() val_batches: a dictionary of inputs, attnetions and labels created by self.get_batches() loss_function: a function for calculating the loss value during training weights: a dictionary of weights for each task. Returns: the number of training epochs before early stopping."""
        cb_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=self.params.early_stopping_check, min_delta=self.params.min_epoch_change)
        cb_scheduler = tf.keras.callbacks.LearningRateScheduler(utils.reduce_learning_rate)
        self.tf_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=self.params.lr), loss=loss_function)
        y = {task_label: train_batches[task_label].replace(np.nan, -1) for task_label in self.task_labels}
        val_y = {task_label: val_batches[task_label].replace(np.nan, -1) for task_label in self.task_labels}
        history = self.tf_model.fit(x={'inputs': train_batches['inputs'], 'atten': train_batches['attentions']}, y=y, epochs=self.params.n_epoch, callbacks=[cb_early, cb_scheduler], validation_data=({'inputs': val_batches['inputs'], 'atten': val_batches['attentions']}, val_y), class_weight=weights)
        return len(history.history['loss'])

    def predict(self, batches):
        """Predicts the outputs for each task_label. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label"""
        results = collections.defaultdict(list)
        if 'majority' in batches.keys():
            results['majority'] = batches['majority']
        if 'text_id' in batches.keys():
            results['text_id'] = batches['text_id']
        logits = self.tf_model.predict(x={'inputs': batches['inputs'], 'atten': batches['attentions']})
        for i, task_label in enumerate(self.task_labels):
            predictions = utils.to_binary(logits[i] if len(self.task_labels) > 1 else logits)
            results[task_label + '_pred'] = predictions
            if task_label in batches.keys():
                results[task_label + '_label'] = batches[task_label]
            if len(self.task_labels) == 1:
                results[task_label + '_logit'] = logits.flatten()
        return pd.DataFrame(results)

    def mc_predict(self, batches):
        """Uses the trained models for mc_pass iterations to calculate uncertainty. Each iteration is performed with dropouts, so the predictions vary. Based on Gal and Ghahramani, 2016. Args: batches: the input batches created through self.get_batches() Returns: A dataframe that includes predictions and labels for each task_label"""
        results = collections.defaultdict(list)
        dropout_predictions = np.empty((0, batches['inputs'].shape[0], 1))
        for i, task_label in enumerate(self.task_labels):
            for _ in range(self.params.mc_passes):
                logits = self.tf_model.predict(x={'inputs': batches['inputs'], 'atten': batches['attentions']})
                mc_predictions = utils.to_binary(logits[i] if len(self.task_labels) > 1 else logits)
                dropout_predictions = np.vstack((dropout_predictions, mc_predictions[np.newaxis, :, np.newaxis]))
            results[task_label + '_mean'] = list(np.squeeze(np.mean(dropout_predictions, axis=0)))
            results[task_label + '_variance'] = list(np.squeeze(np.var(dropout_predictions, axis=0)))
        return pd.DataFrame(results)


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

Class description:
Implement the TenderUaAwardComplaintDocumentResource class.

Method signatures and docstrings:
- def validate_complaint_document(self, operation): TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to use different validators on methods according to procedure type.
- def collection_post(self): Tender Award Complaint Document Upload
- def put(self): Tender Award Complaint Document Update
- def patch(self): Tender Award Complaint Document Update

Implement the Python class `TenderUaAwardComplaintDocumentResource` described below.

Class description:
Implement the TenderUaAwardComplaintDocumentResource class.

Method signatures and docstrings:
- def validate_complaint_document(self, operation): TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to use different validators on methods according to procedure type.
- def collection_post(self): Tender Award Complaint Document Upload
- def put(self): Tender Award Complaint Document Update
- def patch(self): Tender Award Complaint Document Update

<|skeleton|>
class TenderUaAwardComplaintDocumentResource:

    def validate_complaint_document(self, operation):
        """TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to use different validators on methods according to procedure type."""
        <|body_0|>

    def collection_post(self):
        """Tender Award Complaint Document Upload"""
        <|body_1|>

    def put(self):
        """Tender Award Complaint Document Update"""
        <|body_2|>

    def patch(self):
        """Tender Award Complaint Document Update"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        if operation == 'update' and self.request.authenticated_role != self.context.author:
            self.request.errors.add('url', 'role', 'Can update document only author')
            self.request.errors.status = 403
            raise error_handler(self.request.errors)
        if self.request.validated['tender_status'] not in ['active.qualification', 'active.awarded']:
            raise_operation_error(self.request, "Can't {} document in current ({}) tender status".format(operation, self.request.validated['tender_status']))
        if any([i.status != 'active' for i in self.request.validated['tender'].lots if i.id == self.request.validated['award'].lotID]):
            raise_operation_error(self.request, 'Can {} document only in active lot status'.format(operation))
        if self.request.validated['complaint'].status not in STATUS4ROLE.get(self.request.authenticated_role, []):
            raise_operation_error(self.request, "Can't {} document in current ({}) complaint status".format(operation, self.request.validated['complaint'].status))
        return True
<|end_body_0|>

<|body_start_1|>
        if not self.validate_complaint_document('add'):
            return
        document = upload_file(self.request)
        document.author = self.request.authenticated_role
        self.context.documents.append(document)
        if save_tender(self.request):
            self.LOGGER.info('Created tender award complaint document {}'.format(document.id), extra=context_unpack(self.request, {'MESSAGE_ID': 'tender_award_complaint_document_create'}, {'document_id': document.id}))
            self.request.response.status = 201
            document_route = self.request.matched_route.name.replace('collection_', '')
            self.request.response.headers['Location'] = self.request.current_route_url(_route_name=document_route, document_id=document.id, _query={})
            return {'data': document.serialize('view')}
<|end_body_1|>

<|body_start_2|>
        if not self.validate_complaint_document('update'):
            return
        document = upload_file(self.request)
        document.author = self.request.authenticated_role
        self.request.validated['complaint'].documents.append(document)
        if save_tender(self.request):
            self.LOGGER.info('Updated tender award complaint document {}'.format(self.request.context.id), extra=context_unpack(self.request, {'MESSAGE_ID': 'tender_award_complaint_document_put'}))
            return {'data': document.serialize('view')}
<|end_body_2|>

<|body_start_3|>
        if not self.validate_complaint_document('update'):
            return
        if apply_patch(self.request, src=self.request.context.serialize()):
            update_file_content_type(self.request)
            self.LOGGER.info('Updated tender award complaint document {}'.format(self.request.context.id), extra=context_unpack(self.request, {'MESSAGE_ID': 'tender_award_complaint_document_patch'}))
            return {'data': self.request.context.serialize('view')}
<|end_body_3|>

Solution:
class TenderUaAwardComplaintDocumentResource:
    def validate_complaint_document(self, operation):
        """TODO move validators This class is inherited in limited and openeu (qualification complaint) package, but validate_complaint_document function has different validators. For now, we have no way to use different validators on methods according to procedure type."""
        if operation == 'update' and self.request.authenticated_role != self.context.author:
            self.request.errors.add('url', 'role', 'Can update document only author')
            self.request.errors.status = 403
            raise error_handler(self.request.errors)
        if self.request.validated['tender_status'] not in ['active.qualification', 'active.awarded']:
            raise_operation_error(self.request, "Can't {} document in current ({}) tender status".format(operation, self.request.validated['tender_status']))
        if any([i.status != 'active' for i in self.request.validated['tender'].lots if i.id == self.request.validated['award'].lotID]):
            raise_operation_error(self.request, 'Can {} document only in active lot status'.format(operation))
        if self.request.validated['complaint'].status not in STATUS4ROLE.get(self.request.authenticated_role, []):
            raise_operation_error(self.request, "Can't {} document in current ({}) complaint status".format(operation, self.request.validated['complaint'].status))
        return True

    def collection_post(self):
        """Tender Award Complaint Document Upload"""
        if not self.validate_complaint_document('add'):
            return
        document = upload_file(self.request)
        document.author = self.request.authenticated_role
        self.context.documents.append(document)
        if save_tender(self.request):
            self.LOGGER.info('Created tender award complaint document {}'.format(document.id), extra=context_unpack(self.request, {'MESSAGE_ID': 'tender_award_complaint_document_create'}, {'document_id': document.id}))
            self.request.response.status = 201
            document_route = self.request.matched_route.name.replace('collection_', '')
            self.request.response.headers['Location'] = self.request.current_route_url(_route_name=document_route, document_id=document.id, _query={})
            return {'data': document.serialize('view')}

    def put(self):
        """Tender Award Complaint Document Update"""
        if not self.validate_complaint_document('update'):
            return
        document = upload_file(self.request)
        document.author = self.request.authenticated_role
        self.request.validated['complaint'].documents.append(document)
        if save_tender(self.request):
            self.LOGGER.info('Updated tender award complaint document {}'.format(self.request.context.id), extra=context_unpack(self.request, {'MESSAGE_ID': 'tender_award_complaint_document_put'}))
            return {'data': document.serialize('view')}

    def patch(self):
        """Tender Award Complaint Document Update"""
        if not self.validate_complaint_document('update'):
            return
        if apply_patch(self.request, src=self.request.context.serialize()):
            update_file_content_type(self.request)
            self.LOGGER.info('Updated tender award complaint document {}'.format(self.request.context.id), extra=context_unpack(self.request, {'MESSAGE_ID': 'tender_award_complaint_document_patch'}))
            return {'data': self.request.context.serialize('view')}


load

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

Class description:
Implement the CloudPortal class.

Method signatures and docstrings:
- def __init__(self, container): Args: container(shelf.metadata.container.Container)
- def update(self, cloud_identifier, metadata): Updates the metadata in the cloud which is the source of truth. Args: cloud_identifier(basestring): Something that can identify the file in the cloud. Right now that will be a path to the file in S3 that stores the metadata metadata(schemas/metadata.json)
- def load(self, cloud_identifier): Loads metadata from the cloud. Returns dict

Implement the Python class `CloudPortal` described below.

Class description:
Implement the CloudPortal class.

Method signatures and docstrings:
- def __init__(self, container): Args: container(shelf.metadata.container.Container)
- def update(self, cloud_identifier, metadata): Updates the metadata in the cloud which is the source of truth. Args: cloud_identifier(basestring): Something that can identify the file in the cloud. Right now that will be a path to the file in S3 that stores the metadata metadata(schemas/metadata.json)
- def load(self, cloud_identifier): Loads metadata from the cloud. Returns dict

<|skeleton|>
class CloudPortal:

    def __init__(self, container):
        """Args: container(shelf.metadata.container.Container)"""
        <|body_0|>

    def update(self, cloud_identifier, metadata):
        """Updates the metadata in the cloud which is the source of truth. Args: cloud_identifier(basestring): Something that can identify the file in the cloud. Right now that will be a path to the file in S3 that stores the metadata metadata(schemas/metadata.json)"""
        <|body_1|>

    def load(self, cloud_identifier):
        """Loads metadata from the cloud. Returns dict"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.codec = container.yaml_codec
        self.mapper = container.mapper
        self.container = container
<|end_body_0|>

<|body_start_1|>
        contents = copy.deepcopy(metadata)
        contents = dict(contents)
        contents = self.mapper.to_cloud(contents)
        contents = self.codec.serialize(contents)
        with self.container.create_cloud_storage() as storage:
            storage.set_artifact_from_string(cloud_identifier, contents)
<|end_body_1|>

<|body_start_2|>
        with self.container.create_cloud_storage() as storage:
            meta = None
            try:
                raw_meta = storage.get_artifact_as_string(cloud_identifier)
                meta = self.codec.deserialize(raw_meta)
            except ArtifactNotFoundError:
                pass
            if not meta:
                meta = {}
            meta = self.mapper.to_response(meta)
        return meta
<|end_body_2|>

Solution:
class CloudPortal:
    def __init__(self, container):
        """Args: container(shelf.metadata.container.Container)"""
        self.codec = container.yaml_codec
        self.mapper = container.mapper
        self.container = container

    def update(self, cloud_identifier, metadata):
        """Updates the metadata in the cloud which is the source of truth. Args: cloud_identifier(basestring): Something that can identify the file in the cloud. Right now that will be a path to the file in S3 that stores the metadata metadata(schemas/metadata.json)"""
        contents = copy.deepcopy(metadata)
        contents = dict(contents)
        contents = self.mapper.to_cloud(contents)
        contents = self.codec.serialize(contents)
        with self.container.create_cloud_storage() as storage:
            storage.set_artifact_from_string(cloud_identifier, contents)

    def load(self, cloud_identifier):
        """Loads metadata from the cloud. Returns dict"""
        with self.container.create_cloud_storage() as storage:
            meta = None
            try:
                raw_meta = storage.get_artifact_as_string(cloud_identifier)
                meta = self.codec.deserialize(raw_meta)
            except ArtifactNotFoundError:
                pass
            if not meta:
                meta = {}
            meta = self.mapper.to_response(meta)
        return meta


minWindow

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

Class description:
Implement the BruteForceSolution class.

Method signatures and docstrings:
- def minWindow(self, searchString, t): Interface ---- :type searchString: str :type t: str :rtype: str Approach ---- 1. Plant the left pointer and scan the window 2. Find all substrings that contain all of the target 3. Take the smallest Complexity ---- M = target string N = length of query string Time : O(N^2) Space : O(1)
- def contains_all(self, searchString, t): This is a really convoluted way of doing this 1. You could use better syntax 2. Or make use of Collections.Counter()

Implement the Python class `BruteForceSolution` described below.

Class description:
Implement the BruteForceSolution class.

Method signatures and docstrings:
- def minWindow(self, searchString, t): Interface ---- :type searchString: str :type t: str :rtype: str Approach ---- 1. Plant the left pointer and scan the window 2. Find all substrings that contain all of the target 3. Take the smallest Complexity ---- M = target string N = length of query string Time : O(N^2) Space : O(1)
- def contains_all(self, searchString, t): This is a really convoluted way of doing this 1. You could use better syntax 2. Or make use of Collections.Counter()

<|skeleton|>
class BruteForceSolution:

    def minWindow(self, searchString, t):
        """Interface ---- :type searchString: str :type t: str :rtype: str Approach ---- 1. Plant the left pointer and scan the window 2. Find all substrings that contain all of the target 3. Take the smallest Complexity ---- M = target string N = length of query string Time : O(N^2) Space : O(1)"""
        <|body_0|>

    def contains_all(self, searchString, t):
        """This is a really convoluted way of doing this 1. You could use better syntax 2. Or make use of Collections.Counter()"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        n = len(searchString)
        min_window_size = sys.maxsize
        min_window = ''
        for left in range(0, n):
            for right in range(left, n):
                window_snippet = searchString[left:right + 1]
                window_contains_all = self.contains_all(window_snippet, t)
                if window_contains_all and len(window_snippet) < min_window_size:
                    min_window_size = len(window_snippet)
                    min_window = window_snippet
        return min_window
<|end_body_0|>

<|body_start_1|>
        required_characters = {}
        for i in range(0, len(t)):
            occurrences = 0
            if t[i] in required_characters:
                occurrences = required_characters[t[i]]
            required_characters[t[i]] = occurrences + 1
        for i in range(0, len(searchString)):
            curr = searchString[i]
            if curr in required_characters:
                new_occurrences = required_characters[curr] - 1
                '\n                If we have satisfied all of the characters for this character, remove the key\n                from the hashtable.\n                 \n                Otherwise, just update the mapping with 1 less occurrence to satisfy for\n                '
                if new_occurrences == 0:
                    del required_characters[curr]
                else:
                    required_characters[curr] = new_occurrences
        '\n        If we satisfied all characters the the required characters hashtable will be\n        empty\n        '
        return not required_characters
<|end_body_1|>

Solution:
class BruteForceSolution:
    def minWindow(self, searchString, t):
        """Interface ---- :type searchString: str :type t: str :rtype: str Approach ---- 1. Plant the left pointer and scan the window 2. Find all substrings that contain all of the target 3. Take the smallest Complexity ---- M = target string N = length of query string Time : O(N^2) Space : O(1)"""
        n = len(searchString)
        min_window_size = sys.maxsize
        min_window = ''
        for left in range(0, n):
            for right in range(left, n):
                window_snippet = searchString[left:right + 1]
                window_contains_all = self.contains_all(window_snippet, t)
                if window_contains_all and len(window_snippet) < min_window_size:
                    min_window_size = len(window_snippet)
                    min_window = window_snippet
        return min_window

    def contains_all(self, searchString, t):
        """This is a really convoluted way of doing this 1. You could use better syntax 2. Or make use of Collections.Counter()"""
        required_characters = {}
        for i in range(0, len(t)):
            occurrences = 0
            if t[i] in required_characters:
                occurrences = required_characters[t[i]]
            required_characters[t[i]] = occurrences + 1
        for i in range(0, len(searchString)):
            curr = searchString[i]
            if curr in required_characters:
                new_occurrences = required_characters[curr] - 1
                '\n                If we have satisfied all of the characters for this character, remove the key\n                from the hashtable.\n                 \n                Otherwise, just update the mapping with 1 less occurrence to satisfy for\n                '
                if new_occurrences == 0:
                    del required_characters[curr]
                else:
                    required_characters[curr] = new_occurrences
        '\n        If we satisfied all characters the the required characters hashtable will be\n        empty\n        '
        return not required_characters


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

Class description:
Add a random partial square pulse to a signal

Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.01, 0.2), frequencies: Sequence[float]=(0.001, 0.02), fraction: Sequence[float]=(0.01, 0.2)) -> None: Args: boundaries: list defining lower and upper boundaries for the square pulse magnitude, lower and upper values need to be positive , default : ``[0.01, 0.2]`` frequencies: list defining lower and upper frequencies for square pulse signal generation example : ``[0.001, 0.02]`` fraction: list defining lower and upper boundaries for partial square pulse generation default: ``[0.01, 0.2]``
- def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor: Args: signal: input 1 dimension signal to which a partial square pulse will be added

Implement the Python class `SignalRandAddSquarePulsePartial` described below.

Class description:
Add a random partial square pulse to a signal

Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.01, 0.2), frequencies: Sequence[float]=(0.001, 0.02), fraction: Sequence[float]=(0.01, 0.2)) -> None: Args: boundaries: list defining lower and upper boundaries for the square pulse magnitude, lower and upper values need to be positive , default : ``[0.01, 0.2]`` frequencies: list defining lower and upper frequencies for square pulse signal generation example : ``[0.001, 0.02]`` fraction: list defining lower and upper boundaries for partial square pulse generation default: ``[0.01, 0.2]``
- def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor: Args: signal: input 1 dimension signal to which a partial square pulse will be added

<|skeleton|>
class SignalRandAddSquarePulsePartial:
    """Add a random partial square pulse to a signal"""

    def __init__(self, boundaries: Sequence[float]=(0.01, 0.2), frequencies: Sequence[float]=(0.001, 0.02), fraction: Sequence[float]=(0.01, 0.2)) -> None:
        """Args: boundaries: list defining lower and upper boundaries for the square pulse magnitude, lower and upper values need to be positive , default : ``[0.01, 0.2]`` frequencies: list defining lower and upper frequencies for square pulse signal generation example : ``[0.001, 0.02]`` fraction: list defining lower and upper boundaries for partial square pulse generation default: ``[0.01, 0.2]``"""
        <|body_0|>

    def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor:
        """Args: signal: input 1 dimension signal to which a partial square pulse will be added"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__()
        check_boundaries(boundaries)
        self.boundaries = boundaries
        self.frequencies = frequencies
        self.fraction = fraction
<|end_body_0|>

<|body_start_1|>
        self.randomize(None)
        self.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])
        self.fracs = self.R.uniform(low=self.fraction[0], high=self.fraction[1])
        self.freqs = self.R.uniform(low=self.frequencies[0], high=self.frequencies[1])
        length = signal.shape[-1]
        time_partial = np.arange(0, round(self.fracs * length), 1)
        squaredpulse_partial = self.magnitude * squarepulse(self.freqs * time_partial)
        loc = np.random.choice(range(length))
        signal = paste(signal, squaredpulse_partial, (loc,))
        return signal
<|end_body_1|>

Solution:
class SignalRandAddSquarePulsePartial:
    """Add a random partial square pulse to a signal"""

    def __init__(self, boundaries: Sequence[float]=(0.01, 0.2), frequencies: Sequence[float]=(0.001, 0.02), fraction: Sequence[float]=(0.01, 0.2)) -> None:
        """Args: boundaries: list defining lower and upper boundaries for the square pulse magnitude, lower and upper values need to be positive , default : ``[0.01, 0.2]`` frequencies: list defining lower and upper frequencies for square pulse signal generation example : ``[0.001, 0.02]`` fraction: list defining lower and upper boundaries for partial square pulse generation default: ``[0.01, 0.2]``"""
        super().__init__()
        check_boundaries(boundaries)
        self.boundaries = boundaries
        self.frequencies = frequencies
        self.fraction = fraction

    def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor:
        """Args: signal: input 1 dimension signal to which a partial square pulse will be added"""
        self.randomize(None)
        self.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])
        self.fracs = self.R.uniform(low=self.fraction[0], high=self.fraction[1])
        self.freqs = self.R.uniform(low=self.frequencies[0], high=self.frequencies[1])
        length = signal.shape[-1]
        time_partial = np.arange(0, round(self.fracs * length), 1)
        squaredpulse_partial = self.magnitude * squarepulse(self.freqs * time_partial)
        loc = np.random.choice(range(length))
        signal = paste(signal, squaredpulse_partial, (loc,))
        return signal


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

Class description:
Serializer for Parameter model.

Method signatures and docstrings:
- def get_value(self, instance): Get the value of the ParameterSetting belonging to the Parameter.
- def get_choices(self, instance): Get choices corresponding to Choice parameter.
- def get_type(self, instance): Get the type of a Parameter.

Implement the Python class `ParameterSerializer` described below.

Class description:
Serializer for Parameter model.

Method signatures and docstrings:
- def get_value(self, instance): Get the value of the ParameterSetting belonging to the Parameter.
- def get_choices(self, instance): Get choices corresponding to Choice parameter.
- def get_type(self, instance): Get the type of a Parameter.

<|skeleton|>
class ParameterSerializer:
    """Serializer for Parameter model."""

    def get_value(self, instance):
        """Get the value of the ParameterSetting belonging to the Parameter."""
        <|body_0|>

    def get_choices(self, instance):
        """Get choices corresponding to Choice parameter."""
        <|body_1|>

    def get_type(self, instance):
        """Get the type of a Parameter."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        try:
            return ParameterSetting.objects.get(base_parameter=instance, project=self.context.get('view').kwargs.get('project')).raw_value
        except ParameterSetting.DoesNotExist:
            return None
<|end_body_0|>

<|body_start_1|>
        if instance.type == BaseParameter.CHOICE_TYPE:
            return [x.value for x in instance.get_typed_parameter().get_available_choices()]
        else:
            return None
<|end_body_1|>

<|body_start_2|>
        if instance.type == BaseParameter.BOOLEAN_TYPE:
            return 'Boolean'
        elif instance.type == BaseParameter.STATIC_TYPE:
            return 'Static'
        elif instance.type == BaseParameter.STRING_TYPE:
            return 'String'
        elif instance.type == BaseParameter.CHOICE_TYPE:
            return 'Choice'
        elif instance.type == BaseParameter.TEXT_TYPE:
            return 'Text'
        elif instance.type == BaseParameter.INTEGER_TYPE:
            return 'Integer'
        else:
            return 'Float'
<|end_body_2|>

Solution:
class ParameterSerializer:
    """Serializer for Parameter model."""

    def get_value(self, instance):
        """Get the value of the ParameterSetting belonging to the Parameter."""
        try:
            return ParameterSetting.objects.get(base_parameter=instance, project=self.context.get('view').kwargs.get('project')).raw_value
        except ParameterSetting.DoesNotExist:
            return None

    def get_choices(self, instance):
        """Get choices corresponding to Choice parameter."""
        if instance.type == BaseParameter.CHOICE_TYPE:
            return [x.value for x in instance.get_typed_parameter().get_available_choices()]
        else:
            return None

    def get_type(self, instance):
        """Get the type of a Parameter."""
        if instance.type == BaseParameter.BOOLEAN_TYPE:
            return 'Boolean'
        elif instance.type == BaseParameter.STATIC_TYPE:
            return 'Static'
        elif instance.type == BaseParameter.STRING_TYPE:
            return 'String'
        elif instance.type == BaseParameter.CHOICE_TYPE:
            return 'Choice'
        elif instance.type == BaseParameter.TEXT_TYPE:
            return 'Text'
        elif instance.type == BaseParameter.INTEGER_TYPE:
            return 'Integer'
        else:
            return 'Float'