Prompt:
Implement the Python class `Test028_Department_Modify_P1` described below.

Class description:
Implement the Test028_Department_Modify_P1 class.

Method signatures and docstrings:
- def test_modify_name(self): 修改部门名称
- def test_modify_number(self): 修改排序号
- def test_modify_dept(self): 修改上级部门
- def test_modify_back(self): 修改部门并返回

Implement the Python class `Test028_Department_Modify_P1` described below.

Class description:
Implement the Test028_Department_Modify_P1 class.

Method signatures and docstrings:
- def test_modify_name(self): 修改部门名称
- def test_modify_number(self): 修改排序号
- def test_modify_dept(self): 修改上级部门
- def test_modify_back(self): 修改部门并返回

<|skeleton|>
class Test028_Department_Modify_P1:

    def test_modify_name(self):
        """修改部门名称"""
        <|body_0|>

    def test_modify_number(self):
        """修改排序号"""
        <|body_1|>

    def test_modify_dept(self):
        """修改上级部门"""
        <|body_2|>

    def test_modify_back(self):
        """修改部门并返回"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.name_clear()
        d.add_department(Data.depname + 'Update', '')
        d.add_save()
        self.assertEqual(d.success(), True)
        function.screenshot(self.driver, 'modify_department_name.jpg')
<|end_body_0|>

<|body_start_1|>
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.order_clear()
        d.add_department('', '111')
        d.add_save()
        self.assertEqual(d.success(), True)
        function.screenshot(self.driver, 'modify_department_order.jpg')
<|end_body_1|>

<|body_start_2|>
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.change_dept()
        d.add_save()
        self.assertEqual(d.success(), True)
        function.screenshot(self.driver, 'modify_department_dept.jpg')
<|end_body_2|>

<|body_start_3|>
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.add_back()
        self.assertEqual(d.verify(), True)
        function.screenshot(self.driver, 'modify_department_back.jpg')
<|end_body_3|>

Solution:
class Test028_Department_Modify_P1:
    def test_modify_name(self):
        """修改部门名称"""
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.name_clear()
        d.add_department(Data.depname + 'Update', '')
        d.add_save()
        self.assertEqual(d.success(), True)
        function.screenshot(self.driver, 'modify_department_name.jpg')

    def test_modify_number(self):
        """修改排序号"""
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.order_clear()
        d.add_department('', '111')
        d.add_save()
        self.assertEqual(d.success(), True)
        function.screenshot(self.driver, 'modify_department_order.jpg')

    def test_modify_dept(self):
        """修改上级部门"""
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.change_dept()
        d.add_save()
        self.assertEqual(d.success(), True)
        function.screenshot(self.driver, 'modify_department_dept.jpg')

    def test_modify_back(self):
        """修改部门并返回"""
        d = departmentmanage(self.driver)
        d.open_departmentmanage()
        self.assertEqual(d.verify(), True)
        d.modify_obj()
        self.assertEqual(d.sub_tagname(), '部门管理-修改')
        d.add_back()
        self.assertEqual(d.verify(), True)
        function.screenshot(self.driver, 'modify_department_back.jpg')


forward

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

Class description:
Scale Invariant Signal to Noise Ratio with support for PIT Training Adapted from: - https://github.com/kaituoxu/Conv-TasNet Attributes: eps {float} -- epsilon to avoid 0 division pit {bool} -- use pit training https://arxiv.org/abs/1607.00325

Method signatures and docstrings:
- def __init__(self: 'SI_SNR', eps: float=1e-08, pit: bool=False) -> None: Initialization Keyword Arguments: eps {float} -- epsilon to avoid 0 division (default: {1e-8}) pit {bool} -- use pit training (default: {False})
- def forward(self: 'SI_SNR', Y_: torch.Tensor, Y: torch.Tensor) -> torch.Tensor: Forward Pass zero-mean prior to calculation: s_target = (<ŝ,s>.s) / ||s||² e_noise = ŝ - s_target si_snr = 10 torch.log10(||s_target||² / ||e_noise||²) Arguments: Y_ {torch.Tensor} -- estimated source separation input tensor Y {torch.Tensor} -- target source separation input tensor Returns: torch.Tensor -- si snr output loss tensor

Implement the Python class `SI_SNR` described below.

Class description:
Scale Invariant Signal to Noise Ratio with support for PIT Training Adapted from: - https://github.com/kaituoxu/Conv-TasNet Attributes: eps {float} -- epsilon to avoid 0 division pit {bool} -- use pit training https://arxiv.org/abs/1607.00325

Method signatures and docstrings:
- def __init__(self: 'SI_SNR', eps: float=1e-08, pit: bool=False) -> None: Initialization Keyword Arguments: eps {float} -- epsilon to avoid 0 division (default: {1e-8}) pit {bool} -- use pit training (default: {False})
- def forward(self: 'SI_SNR', Y_: torch.Tensor, Y: torch.Tensor) -> torch.Tensor: Forward Pass zero-mean prior to calculation: s_target = (<ŝ,s>.s) / ||s||² e_noise = ŝ - s_target si_snr = 10 torch.log10(||s_target||² / ||e_noise||²) Arguments: Y_ {torch.Tensor} -- estimated source separation input tensor Y {torch.Tensor} -- target source separation input tensor Returns: torch.Tensor -- si snr output loss tensor

<|skeleton|>
class SI_SNR:
    """Scale Invariant Signal to Noise Ratio with support for PIT Training Adapted from: - https://github.com/kaituoxu/Conv-TasNet Attributes: eps {float} -- epsilon to avoid 0 division pit {bool} -- use pit training https://arxiv.org/abs/1607.00325"""

    def __init__(self: 'SI_SNR', eps: float=1e-08, pit: bool=False) -> None:
        """Initialization Keyword Arguments: eps {float} -- epsilon to avoid 0 division (default: {1e-8}) pit {bool} -- use pit training (default: {False})"""
        <|body_0|>

    def forward(self: 'SI_SNR', Y_: torch.Tensor, Y: torch.Tensor) -> torch.Tensor:
        """Forward Pass zero-mean prior to calculation: s_target = (<ŝ,s>.s) / ||s||² e_noise = ŝ - s_target si_snr = 10 torch.log10(||s_target||² / ||e_noise||²) Arguments: Y_ {torch.Tensor} -- estimated source separation input tensor Y {torch.Tensor} -- target source separation input tensor Returns: torch.Tensor -- si snr output loss tensor"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(SI_SNR, self).__init__()
        self.eps = eps
        self.pit = pit
<|end_body_0|>

<|body_start_1|>
        B, C, S = Y.size()
        zero_mean_target = Y - torch.mean(Y, dim=-1, keepdim=True)
        zero_mean_estimate = Y_ - torch.mean(Y_, dim=-1, keepdim=True)
        s_target = torch.unsqueeze(zero_mean_target, dim=1)
        s_estimate = torch.unsqueeze(zero_mean_estimate, dim=2)
        pair_wise_dot = torch.sum(s_estimate * s_target, dim=3, keepdim=True)
        s_target_energy = torch.sum(s_target ** 2, dim=3, keepdim=True) + self.eps
        pair_wise_proj = pair_wise_dot * s_target / s_target_energy
        e_noise = s_estimate - pair_wise_proj
        pair_wise_si_snr = torch.sum(pair_wise_proj ** 2, dim=3) / (torch.sum(e_noise ** 2, dim=3) + self.eps)
        pair_wise_si_snr = 10 * torch.log10(pair_wise_si_snr + self.eps)
        if self.pit:
            perms = Y.new_tensor(list(permutations(range(C)))).long()
            index = torch.unsqueeze(perms, 2)
            perms_one_hot = Y.new_zeros((*perms.size(), C)).scatter_(2, index, 1)
            snr_set = torch.einsum('bij,pij->bp', [pair_wise_si_snr, perms_one_hot])
            max_snr_idx = torch.argmax(snr_set, dim=1)
            max_snr, _ = torch.max(snr_set, dim=1)
            max_snr /= C
        si_snr = max_snr if self.pit else pair_wise_si_snr
        loss = 0 - torch.mean(si_snr)
        return loss
<|end_body_1|>

Solution:
class SI_SNR:
    """Scale Invariant Signal to Noise Ratio with support for PIT Training Adapted from: - https://github.com/kaituoxu/Conv-TasNet Attributes: eps {float} -- epsilon to avoid 0 division pit {bool} -- use pit training https://arxiv.org/abs/1607.00325"""

    def __init__(self: 'SI_SNR', eps: float=1e-08, pit: bool=False) -> None:
        """Initialization Keyword Arguments: eps {float} -- epsilon to avoid 0 division (default: {1e-8}) pit {bool} -- use pit training (default: {False})"""
        super(SI_SNR, self).__init__()
        self.eps = eps
        self.pit = pit

    def forward(self: 'SI_SNR', Y_: torch.Tensor, Y: torch.Tensor) -> torch.Tensor:
        """Forward Pass zero-mean prior to calculation: s_target = (<ŝ,s>.s) / ||s||² e_noise = ŝ - s_target si_snr = 10 torch.log10(||s_target||² / ||e_noise||²) Arguments: Y_ {torch.Tensor} -- estimated source separation input tensor Y {torch.Tensor} -- target source separation input tensor Returns: torch.Tensor -- si snr output loss tensor"""
        B, C, S = Y.size()
        zero_mean_target = Y - torch.mean(Y, dim=-1, keepdim=True)
        zero_mean_estimate = Y_ - torch.mean(Y_, dim=-1, keepdim=True)
        s_target = torch.unsqueeze(zero_mean_target, dim=1)
        s_estimate = torch.unsqueeze(zero_mean_estimate, dim=2)
        pair_wise_dot = torch.sum(s_estimate * s_target, dim=3, keepdim=True)
        s_target_energy = torch.sum(s_target ** 2, dim=3, keepdim=True) + self.eps
        pair_wise_proj = pair_wise_dot * s_target / s_target_energy
        e_noise = s_estimate - pair_wise_proj
        pair_wise_si_snr = torch.sum(pair_wise_proj ** 2, dim=3) / (torch.sum(e_noise ** 2, dim=3) + self.eps)
        pair_wise_si_snr = 10 * torch.log10(pair_wise_si_snr + self.eps)
        if self.pit:
            perms = Y.new_tensor(list(permutations(range(C)))).long()
            index = torch.unsqueeze(perms, 2)
            perms_one_hot = Y.new_zeros((*perms.size(), C)).scatter_(2, index, 1)
            snr_set = torch.einsum('bij,pij->bp', [pair_wise_si_snr, perms_one_hot])
            max_snr_idx = torch.argmax(snr_set, dim=1)
            max_snr, _ = torch.max(snr_set, dim=1)
            max_snr /= C
        si_snr = max_snr if self.pit else pair_wise_si_snr
        loss = 0 - torch.mean(si_snr)
        return loss


setUpClass

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

Class description:
Implement the DeactivateReactivateBlockStorage class.

Method signatures and docstrings:
- def setUpClass(cls): Perform actions that setup the necessary resources for testing The following resources are created during this setup: - A server with defaults defined in server behaviors - An image from the newly created server The following data is generated during this set up: - Get compute integration composite
- def test_create_volume_from_deactivated_image_invalid(self): Verify that a volume cannot be created from a deactivated image Attempt to create a volume using a deactivated image This test will be successful if: - The response code received for deactivate image is a 204 - The response code received for create volume is a 400
- def test_create_volume_from_reactivated_image(self): Verify that a volume can be created from a reactivated image Create a volume using a reactivated image This test will be successful if: - The response code received for reactivate image is a 204 - The volume status is available

Implement the Python class `DeactivateReactivateBlockStorage` described below.

Class description:
Implement the DeactivateReactivateBlockStorage class.

Method signatures and docstrings:
- def setUpClass(cls): Perform actions that setup the necessary resources for testing The following resources are created during this setup: - A server with defaults defined in server behaviors - An image from the newly created server The following data is generated during this set up: - Get compute integration composite
- def test_create_volume_from_deactivated_image_invalid(self): Verify that a volume cannot be created from a deactivated image Attempt to create a volume using a deactivated image This test will be successful if: - The response code received for deactivate image is a 204 - The response code received for create volume is a 400
- def test_create_volume_from_reactivated_image(self): Verify that a volume can be created from a reactivated image Create a volume using a reactivated image This test will be successful if: - The response code received for reactivate image is a 204 - The volume status is available

<|skeleton|>
class DeactivateReactivateBlockStorage:

    def setUpClass(cls):
        """Perform actions that setup the necessary resources for testing The following resources are created during this setup: - A server with defaults defined in server behaviors - An image from the newly created server The following data is generated during this set up: - Get compute integration composite"""
        <|body_0|>

    def test_create_volume_from_deactivated_image_invalid(self):
        """Verify that a volume cannot be created from a deactivated image Attempt to create a volume using a deactivated image This test will be successful if: - The response code received for deactivate image is a 204 - The response code received for create volume is a 400"""
        <|body_1|>

    def test_create_volume_from_reactivated_image(self):
        """Verify that a volume can be created from a reactivated image Create a volume using a reactivated image This test will be successful if: - The response code received for reactivate image is a 204 - The volume status is available"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super(DeactivateReactivateBlockStorage, cls).setUpClass()
        cls.server = cls.compute.servers.behaviors.create_active_server().entity
        cls.image = cls.compute.images.behaviors.create_active_image(cls.server.id).entity
        cls.resources.add(cls.server.id, cls.compute.servers.client.delete_server)
        cls.resources.add(cls.image.id, cls.images_client.delete_image)
<|end_body_0|>

<|body_start_1|>
        self.resp = self.images_admin.client.deactivate_image(self.image.id)
        self.assertEqual(204, self.resp.status_code)
        resp = self.volumes.client.create_volume(size=self.volumes.config.min_volume_from_image_size, volume_type=self.volumes.config.default_volume_type, image_ref=self.image.id)
        self.assertEqual(400, resp.status_code)
<|end_body_1|>

<|body_start_2|>
        resp = self.images_admin.client.reactivate_image(self.image.id)
        self.assertEqual(204, resp.status_code)
        created_volume = self.volumes.behaviors.create_available_volume(size=self.volumes.config.min_volume_from_image_size, volume_type=self.volumes.config.default_volume_type, image_ref=self.image.id)
        self.resources.add(created_volume.id_, self.volumes.client.delete_volume)
        self.assertImageToVolumeCopySucceeded(created_volume.id_, self.volumes.config.min_volume_from_image_size)
<|end_body_2|>

Solution:
class DeactivateReactivateBlockStorage:
    def setUpClass(cls):
        """Perform actions that setup the necessary resources for testing The following resources are created during this setup: - A server with defaults defined in server behaviors - An image from the newly created server The following data is generated during this set up: - Get compute integration composite"""
        super(DeactivateReactivateBlockStorage, cls).setUpClass()
        cls.server = cls.compute.servers.behaviors.create_active_server().entity
        cls.image = cls.compute.images.behaviors.create_active_image(cls.server.id).entity
        cls.resources.add(cls.server.id, cls.compute.servers.client.delete_server)
        cls.resources.add(cls.image.id, cls.images_client.delete_image)

    def test_create_volume_from_deactivated_image_invalid(self):
        """Verify that a volume cannot be created from a deactivated image Attempt to create a volume using a deactivated image This test will be successful if: - The response code received for deactivate image is a 204 - The response code received for create volume is a 400"""
        self.resp = self.images_admin.client.deactivate_image(self.image.id)
        self.assertEqual(204, self.resp.status_code)
        resp = self.volumes.client.create_volume(size=self.volumes.config.min_volume_from_image_size, volume_type=self.volumes.config.default_volume_type, image_ref=self.image.id)
        self.assertEqual(400, resp.status_code)

    def test_create_volume_from_reactivated_image(self):
        """Verify that a volume can be created from a reactivated image Create a volume using a reactivated image This test will be successful if: - The response code received for reactivate image is a 204 - The volume status is available"""
        resp = self.images_admin.client.reactivate_image(self.image.id)
        self.assertEqual(204, resp.status_code)
        created_volume = self.volumes.behaviors.create_available_volume(size=self.volumes.config.min_volume_from_image_size, volume_type=self.volumes.config.default_volume_type, image_ref=self.image.id)
        self.resources.add(created_volume.id_, self.volumes.client.delete_volume)
        self.assertImageToVolumeCopySucceeded(created_volume.id_, self.volumes.config.min_volume_from_image_size)


getResidual

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

Class description:
Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp

Method signatures and docstrings:
- def getInputSpecification(cls): Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying input of cls.
- def getResidual(self, initial, params, pivot, settings): Removes trained signal from data and find residual @ In, initial, np.array, original signal shaped [pivotValues, targets], targets MUST be in same order as self.target @ In, params, dict, training parameters as from self.characterize @ In, pivot, np.array, time-like array values @ In, settings, dict, additional settings specific to algorithm @ Out, residual, np.array, reduced signal shaped [pivotValues, targets]

Implement the Python class `LogTransformer` described below.

Class description:
Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp

Method signatures and docstrings:
- def getInputSpecification(cls): Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying input of cls.
- def getResidual(self, initial, params, pivot, settings): Removes trained signal from data and find residual @ In, initial, np.array, original signal shaped [pivotValues, targets], targets MUST be in same order as self.target @ In, params, dict, training parameters as from self.characterize @ In, pivot, np.array, time-like array values @ In, settings, dict, additional settings specific to algorithm @ Out, residual, np.array, reduced signal shaped [pivotValues, targets]

<|skeleton|>
class LogTransformer:
    """Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp"""

    def getInputSpecification(cls):
        """Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying input of cls."""
        <|body_0|>

    def getResidual(self, initial, params, pivot, settings):
        """Removes trained signal from data and find residual @ In, initial, np.array, original signal shaped [pivotValues, targets], targets MUST be in same order as self.target @ In, params, dict, training parameters as from self.characterize @ In, pivot, np.array, time-like array values @ In, settings, dict, additional settings specific to algorithm @ Out, residual, np.array, reduced signal shaped [pivotValues, targets]"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        specs = super().getInputSpecification()
        specs.name = 'logtransformer'
        specs.description = 'applies the natural logarithm to the data and inverts by applying the\n                        exponential function.'
        return specs
<|end_body_0|>

<|body_start_1|>
        for t, (target, data) in enumerate(params.items()):
            if np.any(initial[:, t] <= 0):
                raise ValueError(f'Log transformation requires strictly positive values, and negative values were found in target "{target}"! If negative values were expected, perhaps an ArcsinhTransformer would be more appropriate?')
        return super().getResidual(initial, params, pivot, settings)
<|end_body_1|>

Solution:
class LogTransformer:
    """Wrapper of scikit-learn's FunctionTransformer for np.log/np.exp"""

    def getInputSpecification(cls):
        """Method to get a reference to a class that specifies the input data for class cls. @ In, None @ Out, specs, InputData.ParameterInput, class to use for specifying input of cls."""
        specs = super().getInputSpecification()
        specs.name = 'logtransformer'
        specs.description = 'applies the natural logarithm to the data and inverts by applying the\n                        exponential function.'
        return specs

    def getResidual(self, initial, params, pivot, settings):
        """Removes trained signal from data and find residual @ In, initial, np.array, original signal shaped [pivotValues, targets], targets MUST be in same order as self.target @ In, params, dict, training parameters as from self.characterize @ In, pivot, np.array, time-like array values @ In, settings, dict, additional settings specific to algorithm @ Out, residual, np.array, reduced signal shaped [pivotValues, targets]"""
        for t, (target, data) in enumerate(params.items()):
            if np.any(initial[:, t] <= 0):
                raise ValueError(f'Log transformation requires strictly positive values, and negative values were found in target "{target}"! If negative values were expected, perhaps an ArcsinhTransformer would be more appropriate?')
        return super().getResidual(initial, params, pivot, settings)


Prompt:
Implement the Python class `Lab4Test` described below.

Class description:
Implement the Lab4Test class.

Method signatures and docstrings:
- def test_squared(self): testing squared function
- def test_check_title(self): testing check_title function
- def test_restock_inventory(self): testing restock_inventory function
- def test_filter_0_items(self): testing filter_0_items function
- def test_average_grades(self): testing average_grades function

Implement the Python class `Lab4Test` described below.

Class description:
Implement the Lab4Test class.

Method signatures and docstrings:
- def test_squared(self): testing squared function
- def test_check_title(self): testing check_title function
- def test_restock_inventory(self): testing restock_inventory function
- def test_filter_0_items(self): testing filter_0_items function
- def test_average_grades(self): testing average_grades function

<|skeleton|>
class Lab4Test:

    def test_squared(self):
        """testing squared function"""
        <|body_0|>

    def test_check_title(self):
        """testing check_title function"""
        <|body_1|>

    def test_restock_inventory(self):
        """testing restock_inventory function"""
        <|body_2|>

    def test_filter_0_items(self):
        """testing filter_0_items function"""
        <|body_3|>

    def test_average_grades(self):
        """testing average_grades function"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        func = lab4.squared
        case1 = [1, 2, 3]
        expected1 = [1, 4, 9]
        self.assertEqual(func(case1), expected1)
<|end_body_0|>

<|body_start_1|>
        func = lab4.check_title
        case2 = ['Hello World', 'hi', 'Hello']
        expected2 = ['Hello World', 'Hello']
        self.assertEqual(func(case2), expected2)
<|end_body_1|>

<|body_start_2|>
        func = lab4.restock_inventory
        case3 = {'a': 10, 'b': 5, 'c': 7}
        expected3 = {'a': 20, 'b': 15, 'c': 17}
        self.assertEqual(func(case3), expected3)
<|end_body_2|>

<|body_start_3|>
        func = lab4.filter_0_items
        case4 = {'a': 10, 'b': 0, 'c': 7}
        expected4 = {'a': 10, 'c': 7}
        self.assertEqual(func(case4), expected4)
<|end_body_3|>

<|body_start_4|>
        func = lab4.average_grades
        case5 = {'Michael': [100, 78, 88, 900 / 10], 'Daniel': [80, 95, 77, 64.0], 'Josh': [99, 89, 94, 66]}
        expected5 = {'Michael': 89.0, 'Daniel': 79.0, 'Josh': 87.0}
        self.assertEqual(func(case5), expected5)
<|end_body_4|>

Solution:
class Lab4Test:
    def test_squared(self):
        """testing squared function"""
        func = lab4.squared
        case1 = [1, 2, 3]
        expected1 = [1, 4, 9]
        self.assertEqual(func(case1), expected1)

    def test_check_title(self):
        """testing check_title function"""
        func = lab4.check_title
        case2 = ['Hello World', 'hi', 'Hello']
        expected2 = ['Hello World', 'Hello']
        self.assertEqual(func(case2), expected2)

    def test_restock_inventory(self):
        """testing restock_inventory function"""
        func = lab4.restock_inventory
        case3 = {'a': 10, 'b': 5, 'c': 7}
        expected3 = {'a': 20, 'b': 15, 'c': 17}
        self.assertEqual(func(case3), expected3)

    def test_filter_0_items(self):
        """testing filter_0_items function"""
        func = lab4.filter_0_items
        case4 = {'a': 10, 'b': 0, 'c': 7}
        expected4 = {'a': 10, 'c': 7}
        self.assertEqual(func(case4), expected4)

    def test_average_grades(self):
        """testing average_grades function"""
        func = lab4.average_grades
        case5 = {'Michael': [100, 78, 88, 900 / 10], 'Daniel': [80, 95, 77, 64.0], 'Josh': [99, 89, 94, 66]}
        expected5 = {'Michael': 89.0, 'Daniel': 79.0, 'Josh': 87.0}
        self.assertEqual(func(case5), expected5)


water

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

Class description:
Finds cloud, water and valid TIR pixels

Method signatures and docstrings:
- def cloud(color, BT): Cloud pixels are grey, bright and cold - grey and bright: 2D Saturation and Value - cold: Brightness Temperature More detail on grey and bright threshold: - Saturation always < 0.1 - if Value between 0.1 and 0.2 then Saturation must be 0.1 less than Value - Value always > 0.1 (i.e. negative Saturation not possible)
- def water(toa): Water pixels have an NDWI > 0.1 (Normalized Difference Water Index)

Implement the Python class `Mask` described below.

Class description:
Finds cloud, water and valid TIR pixels

Method signatures and docstrings:
- def cloud(color, BT): Cloud pixels are grey, bright and cold - grey and bright: 2D Saturation and Value - cold: Brightness Temperature More detail on grey and bright threshold: - Saturation always < 0.1 - if Value between 0.1 and 0.2 then Saturation must be 0.1 less than Value - Value always > 0.1 (i.e. negative Saturation not possible)
- def water(toa): Water pixels have an NDWI > 0.1 (Normalized Difference Water Index)

<|skeleton|>
class Mask:
    """Finds cloud, water and valid TIR pixels"""

    def cloud(color, BT):
        """Cloud pixels are grey, bright and cold - grey and bright: 2D Saturation and Value - cold: Brightness Temperature More detail on grey and bright threshold: - Saturation always < 0.1 - if Value between 0.1 and 0.2 then Saturation must be 0.1 less than Value - Value always > 0.1 (i.e. negative Saturation not possible)"""
        <|body_0|>

    def water(toa):
        """Water pixels have an NDWI > 0.1 (Normalized Difference Water Index)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        color2D = ee.Dictionary(ee.Dictionary(color).get('2D'))
        saturation = ee.Image(color2D.get('saturation'))
        value = ee.Image(color2D.get('value'))
        threshold = value.subtract(0.15).updateMask(value.lt(0.3)).unmask(0.15, False)
        grey_and_bright = saturation.lt(ee.Image(threshold))
        cold = ee.Image(BT).lt(20)
        cloud = grey_and_bright.multiply(cold)
        cloudy = cloud.distance(ee.Kernel.euclidean(100, 'meters')).gte(0).unmask(0, False)
        return cloudy.rename(['cloud'])
<|end_body_0|>

<|body_start_1|>
        ndwi = ee.Image(toa).normalizedDifference(['green', 'nir'])
        water = ndwi.gte(0.1)
        return water.rename(['water'])
<|end_body_1|>

Solution:
class Mask:
    """Finds cloud, water and valid TIR pixels"""

    def cloud(color, BT):
        """Cloud pixels are grey, bright and cold - grey and bright: 2D Saturation and Value - cold: Brightness Temperature More detail on grey and bright threshold: - Saturation always < 0.1 - if Value between 0.1 and 0.2 then Saturation must be 0.1 less than Value - Value always > 0.1 (i.e. negative Saturation not possible)"""
        color2D = ee.Dictionary(ee.Dictionary(color).get('2D'))
        saturation = ee.Image(color2D.get('saturation'))
        value = ee.Image(color2D.get('value'))
        threshold = value.subtract(0.15).updateMask(value.lt(0.3)).unmask(0.15, False)
        grey_and_bright = saturation.lt(ee.Image(threshold))
        cold = ee.Image(BT).lt(20)
        cloud = grey_and_bright.multiply(cold)
        cloudy = cloud.distance(ee.Kernel.euclidean(100, 'meters')).gte(0).unmask(0, False)
        return cloudy.rename(['cloud'])

    def water(toa):
        """Water pixels have an NDWI > 0.1 (Normalized Difference Water Index)"""
        ndwi = ee.Image(toa).normalizedDifference(['green', 'nir'])
        water = ndwi.gte(0.1)
        return water.rename(['water'])


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

Class description:
https://leetcode.com/problems/two-sum/ 给一个数组nums，和一个目标数target，求两个数之和为target的下标

Method signatures and docstrings:
- def two_sum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def two_sum2(self, nums, target): 第二种写法

Implement the Python class `Solution` described below.

Class description:
https://leetcode.com/problems/two-sum/ 给一个数组nums，和一个目标数target，求两个数之和为target的下标

Method signatures and docstrings:
- def two_sum(self, nums, target): :type nums: List[int] :type target: int :rtype: List[int]
- def two_sum2(self, nums, target): 第二种写法

<|skeleton|>
class Solution:
    """https://leetcode.com/problems/two-sum/ 给一个数组nums，和一个目标数target，求两个数之和为target的下标"""

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

    def two_sum2(self, nums, target):
        """第二种写法"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if nums is None or len(nums) <= 0:
            return
        my_map = {}
        for i in range(len(nums)):
            if target - nums[i] in my_map:
                return list((my_map[target - nums[i]], i))
            else:
                my_map[nums[i]] = i
        return
<|end_body_0|>

<|body_start_1|>
        new_nums = nums[:]
        for num in nums:
            new_nums.remove(num)
            if target - num in new_nums:
                if target - num == num:
                    return [i for i, x in enumerate(nums) if x == num]
                else:
                    return sorted([nums.index(num), nums.index(target - num)])
            new_nums = nums[:]
<|end_body_1|>

Solution:
class Solution:
    """https://leetcode.com/problems/two-sum/ 给一个数组nums，和一个目标数target，求两个数之和为target的下标"""

    def two_sum(self, nums, target):
        """:type nums: List[int] :type target: int :rtype: List[int]"""
        if nums is None or len(nums) <= 0:
            return
        my_map = {}
        for i in range(len(nums)):
            if target - nums[i] in my_map:
                return list((my_map[target - nums[i]], i))
            else:
                my_map[nums[i]] = i
        return

    def two_sum2(self, nums, target):
        """第二种写法"""
        new_nums = nums[:]
        for num in nums:
            new_nums.remove(num)
            if target - num in new_nums:
                if target - num == num:
                    return [i for i, x in enumerate(nums) if x == num]
                else:
                    return sorted([nums.index(num), nums.index(target - num)])
            new_nums = nums[:]


loss

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

Class description:
A two-layer fully-connected neural network with ReLU non-linearity and softmax loss that uses a modular layer design. We assume an input dimension of D, a hidden dimension of H, and perform classification over C classes. The architecture should be affine - relu - affine - softmax. Note that this class does not implement gradient descent; instead, it will interact with a separate Solver object that is responsible for running optimization. The learnable parameters of the model are stored in the dictionary self.params that maps parameter names to numpy arrays.

Method signatures and docstrings:
- def __init__(self, input_dim=3 * 32 * 32, hidden_dim=100, num_classes=10, weight_scale=0.001, reg=0.0): Initialize a new network. :param input_dim: An integer giving the size of the input :param hidden_dim: An integer giving the size of the hidden layer :param num_classes: An integer giving the number of classes to classify :param dropout: Scalar between 0 and 1 giving dropout strength. :param weight_scale: Scalar giving the standard deviation for random initialization of the weights. :param reg: Scalar giving L2 regularization strength.
- def loss(self, X, y=None): Compute loss and gradient for a mini-batch of data. :param X: Array of input data of shape (N, d_1, ..., d_k) :param y: Array of labels, of shape (N,). y[i] gives the label for X[i]. :return y: If y is None, then run a test-time forward pass of the model and return: - scores: Array of shape (N, C) giving classification scores, where scores[i, c] is the classification score for X[i] and class c. If y is not None, then run a training-time forward and backward pass and return a tuple of: - loss: Scalar value giving the loss - grads: Dictionary with the same keys as self.params, mapping parameter names to gradients of the loss with respect to those parameters.

Implement the Python class `TwoLayerNet` described below.

Class description:
A two-layer fully-connected neural network with ReLU non-linearity and softmax loss that uses a modular layer design. We assume an input dimension of D, a hidden dimension of H, and perform classification over C classes. The architecture should be affine - relu - affine - softmax. Note that this class does not implement gradient descent; instead, it will interact with a separate Solver object that is responsible for running optimization. The learnable parameters of the model are stored in the dictionary self.params that maps parameter names to numpy arrays.

Method signatures and docstrings:
- def __init__(self, input_dim=3 * 32 * 32, hidden_dim=100, num_classes=10, weight_scale=0.001, reg=0.0): Initialize a new network. :param input_dim: An integer giving the size of the input :param hidden_dim: An integer giving the size of the hidden layer :param num_classes: An integer giving the number of classes to classify :param dropout: Scalar between 0 and 1 giving dropout strength. :param weight_scale: Scalar giving the standard deviation for random initialization of the weights. :param reg: Scalar giving L2 regularization strength.
- def loss(self, X, y=None): Compute loss and gradient for a mini-batch of data. :param X: Array of input data of shape (N, d_1, ..., d_k) :param y: Array of labels, of shape (N,). y[i] gives the label for X[i]. :return y: If y is None, then run a test-time forward pass of the model and return: - scores: Array of shape (N, C) giving classification scores, where scores[i, c] is the classification score for X[i] and class c. If y is not None, then run a training-time forward and backward pass and return a tuple of: - loss: Scalar value giving the loss - grads: Dictionary with the same keys as self.params, mapping parameter names to gradients of the loss with respect to those parameters.

<|skeleton|>
class TwoLayerNet:
    """A two-layer fully-connected neural network with ReLU non-linearity and softmax loss that uses a modular layer design. We assume an input dimension of D, a hidden dimension of H, and perform classification over C classes. The architecture should be affine - relu - affine - softmax. Note that this class does not implement gradient descent; instead, it will interact with a separate Solver object that is responsible for running optimization. The learnable parameters of the model are stored in the dictionary self.params that maps parameter names to numpy arrays."""

    def __init__(self, input_dim=3 * 32 * 32, hidden_dim=100, num_classes=10, weight_scale=0.001, reg=0.0):
        """Initialize a new network. :param input_dim: An integer giving the size of the input :param hidden_dim: An integer giving the size of the hidden layer :param num_classes: An integer giving the number of classes to classify :param dropout: Scalar between 0 and 1 giving dropout strength. :param weight_scale: Scalar giving the standard deviation for random initialization of the weights. :param reg: Scalar giving L2 regularization strength."""
        <|body_0|>

    def loss(self, X, y=None):
        """Compute loss and gradient for a mini-batch of data. :param X: Array of input data of shape (N, d_1, ..., d_k) :param y: Array of labels, of shape (N,). y[i] gives the label for X[i]. :return y: If y is None, then run a test-time forward pass of the model and return: - scores: Array of shape (N, C) giving classification scores, where scores[i, c] is the classification score for X[i] and class c. If y is not None, then run a training-time forward and backward pass and return a tuple of: - loss: Scalar value giving the loss - grads: Dictionary with the same keys as self.params, mapping parameter names to gradients of the loss with respect to those parameters."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.params = {}
        self.reg = reg
        self.params['W1'] = np.random.normal(loc=0, scale=weight_scale, size=(input_dim, hidden_dim))
        self.params['b1'] = np.zeros(hidden_dim)
        self.params['W2'] = np.random.normal(loc=0, scale=weight_scale, size=(hidden_dim, num_classes))
        self.params['b2'] = np.zeros(num_classes)
<|end_body_0|>

<|body_start_1|>
        out_1, cache_1 = affine_relu_forward(X, self.params['W1'], self.params['b1'])
        out_2, cache_2 = affine_forward(out_1, self.params['W2'], self.params['b2'])
        scores = out_2
        if y is None:
            return scores
        loss, grads = (0, {})
        loss, d_scores = softmax_loss(scores, y)
        dout_1, grads['W2'], grads['b2'] = affine_backward(d_scores, cache_2)
        dx, grads['W1'], grads['b1'] = affine_relu_backward(dout_1, cache_1)
        w2_reg = 0.5 * self.reg * float(np.tensordot(self.params['W2'], self.params['W2'], axes=((0, 1), (0, 1))))
        w1_reg = 0.5 * self.reg * float(np.tensordot(self.params['W1'], self.params['W1'], axes=((0, 1), (0, 1))))
        dw2_reg = self.reg * self.params['W2']
        dw1_reg = self.reg * self.params['W1']
        loss += w2_reg + w1_reg
        grads['W2'] += dw2_reg
        grads['W1'] += dw1_reg
        return (loss, grads)
<|end_body_1|>

Solution:
class TwoLayerNet:
    """A two-layer fully-connected neural network with ReLU non-linearity and softmax loss that uses a modular layer design. We assume an input dimension of D, a hidden dimension of H, and perform classification over C classes. The architecture should be affine - relu - affine - softmax. Note that this class does not implement gradient descent; instead, it will interact with a separate Solver object that is responsible for running optimization. The learnable parameters of the model are stored in the dictionary self.params that maps parameter names to numpy arrays."""

    def __init__(self, input_dim=3 * 32 * 32, hidden_dim=100, num_classes=10, weight_scale=0.001, reg=0.0):
        """Initialize a new network. :param input_dim: An integer giving the size of the input :param hidden_dim: An integer giving the size of the hidden layer :param num_classes: An integer giving the number of classes to classify :param dropout: Scalar between 0 and 1 giving dropout strength. :param weight_scale: Scalar giving the standard deviation for random initialization of the weights. :param reg: Scalar giving L2 regularization strength."""
        self.params = {}
        self.reg = reg
        self.params['W1'] = np.random.normal(loc=0, scale=weight_scale, size=(input_dim, hidden_dim))
        self.params['b1'] = np.zeros(hidden_dim)
        self.params['W2'] = np.random.normal(loc=0, scale=weight_scale, size=(hidden_dim, num_classes))
        self.params['b2'] = np.zeros(num_classes)

    def loss(self, X, y=None):
        """Compute loss and gradient for a mini-batch of data. :param X: Array of input data of shape (N, d_1, ..., d_k) :param y: Array of labels, of shape (N,). y[i] gives the label for X[i]. :return y: If y is None, then run a test-time forward pass of the model and return: - scores: Array of shape (N, C) giving classification scores, where scores[i, c] is the classification score for X[i] and class c. If y is not None, then run a training-time forward and backward pass and return a tuple of: - loss: Scalar value giving the loss - grads: Dictionary with the same keys as self.params, mapping parameter names to gradients of the loss with respect to those parameters."""
        out_1, cache_1 = affine_relu_forward(X, self.params['W1'], self.params['b1'])
        out_2, cache_2 = affine_forward(out_1, self.params['W2'], self.params['b2'])
        scores = out_2
        if y is None:
            return scores
        loss, grads = (0, {})
        loss, d_scores = softmax_loss(scores, y)
        dout_1, grads['W2'], grads['b2'] = affine_backward(d_scores, cache_2)
        dx, grads['W1'], grads['b1'] = affine_relu_backward(dout_1, cache_1)
        w2_reg = 0.5 * self.reg * float(np.tensordot(self.params['W2'], self.params['W2'], axes=((0, 1), (0, 1))))
        w1_reg = 0.5 * self.reg * float(np.tensordot(self.params['W1'], self.params['W1'], axes=((0, 1), (0, 1))))
        dw2_reg = self.reg * self.params['W2']
        dw1_reg = self.reg * self.params['W1']
        loss += w2_reg + w1_reg
        grads['W2'] += dw2_reg
        grads['W1'] += dw1_reg
        return (loss, grads)


reverse

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

Class description:
Discrete shift-only flow acting on subset of categories.

Method signatures and docstrings:
- def __init__(self, N, K, categories, **kwargs): Args: categories A list of category numbers to be transformed.
- def call(self, x): Applies flow to first self.subset_K shuffled categories.
- def reverse(self, x): Applies reverse flow to first self.subset_K shuffled categories.

Implement the Python class `DiscreteFactorizedFlowPartial` described below.

Class description:
Discrete shift-only flow acting on subset of categories.

Method signatures and docstrings:
- def __init__(self, N, K, categories, **kwargs): Args: categories A list of category numbers to be transformed.
- def call(self, x): Applies flow to first self.subset_K shuffled categories.
- def reverse(self, x): Applies reverse flow to first self.subset_K shuffled categories.

<|skeleton|>
class DiscreteFactorizedFlowPartial:
    """Discrete shift-only flow acting on subset of categories."""

    def __init__(self, N, K, categories, **kwargs):
        """Args: categories A list of category numbers to be transformed."""
        <|body_0|>

    def call(self, x):
        """Applies flow to first self.subset_K shuffled categories."""
        <|body_1|>

    def reverse(self, x):
        """Applies reverse flow to first self.subset_K shuffled categories."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        subset_K = len(categories)
        assert subset_K <= K
        super().__init__(N, subset_K, **kwargs)
        shuffling = np.arange(K)
        np.random.shuffle(shuffling)
        shuffling = np.array(list(categories) + [v for v in shuffling if v not in categories])
        inverted_shuffling = np.arange(K)
        inverted_shuffling[shuffling] = np.arange(K)
        self.shuffling = shuffling
        self.inverted_shuffling = inverted_shuffling
        self.subset_K = subset_K
<|end_body_0|>

<|body_start_1|>
        x = tf.gather(x, self.shuffling, axis=-1)
        x1 = x[..., :self.subset_K]
        x1 = super().call(x1)
        x2 = x[..., self.subset_K:]
        x = tf.concat([x1, x2], axis=-1)
        x = tf.gather(x, self.inverted_shuffling, axis=-1)
        return x
<|end_body_1|>

<|body_start_2|>
        x = tf.gather(x, self.shuffling, axis=-1)
        x1 = x[..., :self.subset_K]
        x1 = super().reverse(x1)
        x2 = x[..., self.subset_K:]
        x = tf.concat([x1, x2], axis=-1)
        x = tf.gather(x, self.inverted_shuffling, axis=-1)
        return x
<|end_body_2|>

Solution:
class DiscreteFactorizedFlowPartial:
    """Discrete shift-only flow acting on subset of categories."""

    def __init__(self, N, K, categories, **kwargs):
        """Args: categories A list of category numbers to be transformed."""
        subset_K = len(categories)
        assert subset_K <= K
        super().__init__(N, subset_K, **kwargs)
        shuffling = np.arange(K)
        np.random.shuffle(shuffling)
        shuffling = np.array(list(categories) + [v for v in shuffling if v not in categories])
        inverted_shuffling = np.arange(K)
        inverted_shuffling[shuffling] = np.arange(K)
        self.shuffling = shuffling
        self.inverted_shuffling = inverted_shuffling
        self.subset_K = subset_K

    def call(self, x):
        """Applies flow to first self.subset_K shuffled categories."""
        x = tf.gather(x, self.shuffling, axis=-1)
        x1 = x[..., :self.subset_K]
        x1 = super().call(x1)
        x2 = x[..., self.subset_K:]
        x = tf.concat([x1, x2], axis=-1)
        x = tf.gather(x, self.inverted_shuffling, axis=-1)
        return x

    def reverse(self, x):
        """Applies reverse flow to first self.subset_K shuffled categories."""
        x = tf.gather(x, self.shuffling, axis=-1)
        x1 = x[..., :self.subset_K]
        x1 = super().reverse(x1)
        x2 = x[..., self.subset_K:]
        x = tf.concat([x1, x2], axis=-1)
        x = tf.gather(x, self.inverted_shuffling, axis=-1)
        return x


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

Class description:
Similar to RunningNorm but uses an exponential weighting.

Method signatures and docstrings:
- def __init__(self, num_features: int, decay: float=0.99, eps: float=1e-05): Builds EMARunningNorm. Args: num_features: Number of features; the length of the non-batch dim. decay: how quickly the weight on past samples decays over time. eps: small constant for numerical stability. Raises: ValueError: if decay is out of range.
- def reset_running_stats(self): Reset the running stats of the normalization layer.
- def update_stats(self, batch: th.Tensor) -> None: Update `self.running_mean` and `self.running_var` in batch mode. Reference Algorithm 3 from: https://github.com/HumanCompatibleAI/imitation/files/9456540/Incremental_batch_EMA_and_EMV.pdf Args: batch: A batch of data to use to update the running mean and variance.

Implement the Python class `EMANorm` described below.

Class description:
Similar to RunningNorm but uses an exponential weighting.

Method signatures and docstrings:
- def __init__(self, num_features: int, decay: float=0.99, eps: float=1e-05): Builds EMARunningNorm. Args: num_features: Number of features; the length of the non-batch dim. decay: how quickly the weight on past samples decays over time. eps: small constant for numerical stability. Raises: ValueError: if decay is out of range.
- def reset_running_stats(self): Reset the running stats of the normalization layer.
- def update_stats(self, batch: th.Tensor) -> None: Update `self.running_mean` and `self.running_var` in batch mode. Reference Algorithm 3 from: https://github.com/HumanCompatibleAI/imitation/files/9456540/Incremental_batch_EMA_and_EMV.pdf Args: batch: A batch of data to use to update the running mean and variance.

<|skeleton|>
class EMANorm:
    """Similar to RunningNorm but uses an exponential weighting."""

    def __init__(self, num_features: int, decay: float=0.99, eps: float=1e-05):
        """Builds EMARunningNorm. Args: num_features: Number of features; the length of the non-batch dim. decay: how quickly the weight on past samples decays over time. eps: small constant for numerical stability. Raises: ValueError: if decay is out of range."""
        <|body_0|>

    def reset_running_stats(self):
        """Reset the running stats of the normalization layer."""
        <|body_1|>

    def update_stats(self, batch: th.Tensor) -> None:
        """Update `self.running_mean` and `self.running_var` in batch mode. Reference Algorithm 3 from: https://github.com/HumanCompatibleAI/imitation/files/9456540/Incremental_batch_EMA_and_EMV.pdf Args: batch: A batch of data to use to update the running mean and variance."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        super().__init__(num_features, eps=eps)
        if not 0 < decay < 1:
            raise ValueError('decay must be between 0 and 1')
        self.decay = decay
        self.register_buffer('inv_learning_rate', th.empty(()))
        self.register_buffer('num_batches', th.empty((), dtype=th.int))
        EMANorm.reset_running_stats(self)
<|end_body_0|>

<|body_start_1|>
        super().reset_running_stats()
        self.inv_learning_rate.zero_()
        self.num_batches.zero_()
<|end_body_1|>

<|body_start_2|>
        b_size = batch.shape[0]
        if len(batch.shape) == 1:
            batch = batch.reshape(b_size, 1)
        self.inv_learning_rate += self.decay ** self.num_batches
        learning_rate = 1 / self.inv_learning_rate
        delta_mean = batch.mean(0) - self.running_mean
        self.running_mean += learning_rate * delta_mean
        batch_var = batch.var(0, unbiased=False)
        delta_var = batch_var + (1 - learning_rate) * delta_mean ** 2 - self.running_var
        self.running_var += learning_rate * delta_var
        self.count += b_size
        self.num_batches += 1
<|end_body_2|>

Solution:
class EMANorm:
    """Similar to RunningNorm but uses an exponential weighting."""

    def __init__(self, num_features: int, decay: float=0.99, eps: float=1e-05):
        """Builds EMARunningNorm. Args: num_features: Number of features; the length of the non-batch dim. decay: how quickly the weight on past samples decays over time. eps: small constant for numerical stability. Raises: ValueError: if decay is out of range."""
        super().__init__(num_features, eps=eps)
        if not 0 < decay < 1:
            raise ValueError('decay must be between 0 and 1')
        self.decay = decay
        self.register_buffer('inv_learning_rate', th.empty(()))
        self.register_buffer('num_batches', th.empty((), dtype=th.int))
        EMANorm.reset_running_stats(self)

    def reset_running_stats(self):
        """Reset the running stats of the normalization layer."""
        super().reset_running_stats()
        self.inv_learning_rate.zero_()
        self.num_batches.zero_()

    def update_stats(self, batch: th.Tensor) -> None:
        """Update `self.running_mean` and `self.running_var` in batch mode. Reference Algorithm 3 from: https://github.com/HumanCompatibleAI/imitation/files/9456540/Incremental_batch_EMA_and_EMV.pdf Args: batch: A batch of data to use to update the running mean and variance."""
        b_size = batch.shape[0]
        if len(batch.shape) == 1:
            batch = batch.reshape(b_size, 1)
        self.inv_learning_rate += self.decay ** self.num_batches
        learning_rate = 1 / self.inv_learning_rate
        delta_mean = batch.mean(0) - self.running_mean
        self.running_mean += learning_rate * delta_mean
        batch_var = batch.var(0, unbiased=False)
        delta_var = batch_var + (1 - learning_rate) * delta_mean ** 2 - self.running_var
        self.running_var += learning_rate * delta_var
        self.count += b_size
        self.num_batches += 1


plot

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

Class description:
The bundle is made of an array of correlations (correlation.cor), it is not a bundle of correlation objects as this might become very expensive.

Method signatures and docstrings:
- def __init__(self, array, times, flux1_name, flux2_name): Args: array: contanining all the correlations times: times at which the correlations where measured. Couls be taken from one of the correlation instances as instance.times flux1_name: Name of the flux 1, also can be taken from the instance flux2_name: Name of the flux 2, also can be taken from the instance Atributes: cor: contains the correlations as an array, the components are x,y,z, and total in 3D norm: normalisation factor for each dimension t=0 (var1(0) var2(0)) cor_norm: list to store the normalised correlations, the last is the total
- def initial_computes(self): Gets the shape of the correlation array and calls the averaging, Args: self Returns ------- None.
- def averaging(self): np.shape(self.arrays) =[#runs, Dimensions(+average),points Takes the average of the array of correlations. Sometimes the individual correlations have errors, therefore each point has (average + std) At the moment it strips the error from uncertainties and analyses everypoint as independent. Returns ------- None.
- def plot(self, fig, ax, dim=0, alpha=0.4, every=1, ax_label=True, norm=True): Args: ax: axes object fig: Figure dim: is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha: is the transparency of the filling every: to not have so many points norm: True if normalised ax_label: The axis label is given here but it could be renamed later Returns: fig, ax
- def plot_bundle(self, ax, dim, alpha=0.4, every=1): Plots all the correlations from the individual runs in the given direction TODO: Add the normalised option Args: ax axes object fig Figure dim is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha is the transparency of the filling every to not have so many points The axis label is given here but it could be renamed later

Implement the Python class `bundle_correlation` described below.

Class description:
The bundle is made of an array of correlations (correlation.cor), it is not a bundle of correlation objects as this might become very expensive.

Method signatures and docstrings:
- def __init__(self, array, times, flux1_name, flux2_name): Args: array: contanining all the correlations times: times at which the correlations where measured. Couls be taken from one of the correlation instances as instance.times flux1_name: Name of the flux 1, also can be taken from the instance flux2_name: Name of the flux 2, also can be taken from the instance Atributes: cor: contains the correlations as an array, the components are x,y,z, and total in 3D norm: normalisation factor for each dimension t=0 (var1(0) var2(0)) cor_norm: list to store the normalised correlations, the last is the total
- def initial_computes(self): Gets the shape of the correlation array and calls the averaging, Args: self Returns ------- None.
- def averaging(self): np.shape(self.arrays) =[#runs, Dimensions(+average),points Takes the average of the array of correlations. Sometimes the individual correlations have errors, therefore each point has (average + std) At the moment it strips the error from uncertainties and analyses everypoint as independent. Returns ------- None.
- def plot(self, fig, ax, dim=0, alpha=0.4, every=1, ax_label=True, norm=True): Args: ax: axes object fig: Figure dim: is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha: is the transparency of the filling every: to not have so many points norm: True if normalised ax_label: The axis label is given here but it could be renamed later Returns: fig, ax
- def plot_bundle(self, ax, dim, alpha=0.4, every=1): Plots all the correlations from the individual runs in the given direction TODO: Add the normalised option Args: ax axes object fig Figure dim is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha is the transparency of the filling every to not have so many points The axis label is given here but it could be renamed later

<|skeleton|>
class bundle_correlation:
    """The bundle is made of an array of correlations (correlation.cor), it is not a bundle of correlation objects as this might become very expensive."""

    def __init__(self, array, times, flux1_name, flux2_name):
        """Args: array: contanining all the correlations times: times at which the correlations where measured. Couls be taken from one of the correlation instances as instance.times flux1_name: Name of the flux 1, also can be taken from the instance flux2_name: Name of the flux 2, also can be taken from the instance Atributes: cor: contains the correlations as an array, the components are x,y,z, and total in 3D norm: normalisation factor for each dimension t=0 (var1(0) var2(0)) cor_norm: list to store the normalised correlations, the last is the total"""
        <|body_0|>

    def initial_computes(self):
        """Gets the shape of the correlation array and calls the averaging, Args: self Returns ------- None."""
        <|body_1|>

    def averaging(self):
        """np.shape(self.arrays) =[#runs, Dimensions(+average),points Takes the average of the array of correlations. Sometimes the individual correlations have errors, therefore each point has (average + std) At the moment it strips the error from uncertainties and analyses everypoint as independent. Returns ------- None."""
        <|body_2|>

    def plot(self, fig, ax, dim=0, alpha=0.4, every=1, ax_label=True, norm=True):
        """Args: ax: axes object fig: Figure dim: is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha: is the transparency of the filling every: to not have so many points norm: True if normalised ax_label: The axis label is given here but it could be renamed later Returns: fig, ax"""
        <|body_3|>

    def plot_bundle(self, ax, dim, alpha=0.4, every=1):
        """Plots all the correlations from the individual runs in the given direction TODO: Add the normalised option Args: ax axes object fig Figure dim is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha is the transparency of the filling every to not have so many points The axis label is given here but it could be renamed later"""
        <|body_4|>

<|end_skeleton|>

<|body_start_0|>
        self.arrays = np.array(array)
        self.flux1_name = flux1_name
        self.flux2_name = flux2_name
        self.times = times
        self.initial_computes()
        self.dic_label = {0: '$x$', 1: '$y$', 2: '$z$', -1: 'Total', self.dimension: 'Total'}
<|end_body_0|>

<|body_start_1|>
        self.n_runs, self.dimension, self.n_points = np.shape(self.arrays)
        self.norm = self.dimension * [0]
        self.cor_norm = self.dimension * [0]
        self.averaging()
        for dim in range(self.dimension):
            normalisation = self.cor[dim][0]
            self.norm[dim] = normalisation
            self.cor_norm[dim] = self.cor[dim] / normalisation
<|end_body_1|>

<|body_start_2|>
        ddof = 1
        array = self.arrays
        if len(array) == 0:
            ddof = 0
        if isinstance(array[0][0][0], un.UFloat):
            array = unumpy.nominal_values(array)
        self.cor = unumpy.uarray(np.average(array, axis=0), sem(array, axis=0, ddof=ddof))
<|end_body_2|>

<|body_start_3|>
        if norm == True:
            cor = self.cor_norm[dim]
        else:
            cor = self.cor[dim]
        y = np.array([i.n for i in cor])
        y_error = np.array([i.s for i in cor])
        ax.plot(self.times[::every], y[::every])
        ax.fill_between(self.times, y - y_error, y + y_error, alpha=0.4)
        if ax_label == True:
            ax.axhline(y=0, xmin=0, xmax=1, ls='--', c='black')
            ax.set_ylabel('$\\langle %s(t)%s(0) \\rangle$' % (self.flux1_name, self.flux2_name))
            ax.set_xlabel('$t$ ')
        return (fig, ax)
<|end_body_3|>

<|body_start_4|>
        for array in self.arrays:
            cor = array[dim]
            if isinstance(cor[0], un.UFloat):
                y = np.array([i.n for i in cor])
                y_error = np.array([i.s for i in cor])
                times = self.times[::every]
                y = y[::every]
                y_error = y_error[::every]
                ax.plot(times, y, label=self.dic_label[dim])
                ax.fill_between(times, y - y_error, y + y_error, alpha=0.4)
            else:
                ax.plot(self.times[::every], cor[::every], label=self.dic_label[dim])
        return ax
<|end_body_4|>

Solution:
class bundle_correlation:
    """The bundle is made of an array of correlations (correlation.cor), it is not a bundle of correlation objects as this might become very expensive."""

    def __init__(self, array, times, flux1_name, flux2_name):
        """Args: array: contanining all the correlations times: times at which the correlations where measured. Couls be taken from one of the correlation instances as instance.times flux1_name: Name of the flux 1, also can be taken from the instance flux2_name: Name of the flux 2, also can be taken from the instance Atributes: cor: contains the correlations as an array, the components are x,y,z, and total in 3D norm: normalisation factor for each dimension t=0 (var1(0) var2(0)) cor_norm: list to store the normalised correlations, the last is the total"""
        self.arrays = np.array(array)
        self.flux1_name = flux1_name
        self.flux2_name = flux2_name
        self.times = times
        self.initial_computes()
        self.dic_label = {0: '$x$', 1: '$y$', 2: '$z$', -1: 'Total', self.dimension: 'Total'}

    def initial_computes(self):
        """Gets the shape of the correlation array and calls the averaging, Args: self Returns ------- None."""
        self.n_runs, self.dimension, self.n_points = np.shape(self.arrays)
        self.norm = self.dimension * [0]
        self.cor_norm = self.dimension * [0]
        self.averaging()
        for dim in range(self.dimension):
            normalisation = self.cor[dim][0]
            self.norm[dim] = normalisation
            self.cor_norm[dim] = self.cor[dim] / normalisation

    def averaging(self):
        """np.shape(self.arrays) =[#runs, Dimensions(+average),points Takes the average of the array of correlations. Sometimes the individual correlations have errors, therefore each point has (average + std) At the moment it strips the error from uncertainties and analyses everypoint as independent. Returns ------- None."""
        ddof = 1
        array = self.arrays
        if len(array) == 0:
            ddof = 0
        if isinstance(array[0][0][0], un.UFloat):
            array = unumpy.nominal_values(array)
        self.cor = unumpy.uarray(np.average(array, axis=0), sem(array, axis=0, ddof=ddof))

    def plot(self, fig, ax, dim=0, alpha=0.4, every=1, ax_label=True, norm=True):
        """Args: ax: axes object fig: Figure dim: is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha: is the transparency of the filling every: to not have so many points norm: True if normalised ax_label: The axis label is given here but it could be renamed later Returns: fig, ax"""
        if norm == True:
            cor = self.cor_norm[dim]
        else:
            cor = self.cor[dim]
        y = np.array([i.n for i in cor])
        y_error = np.array([i.s for i in cor])
        ax.plot(self.times[::every], y[::every])
        ax.fill_between(self.times, y - y_error, y + y_error, alpha=0.4)
        if ax_label == True:
            ax.axhline(y=0, xmin=0, xmax=1, ls='--', c='black')
            ax.set_ylabel('$\\langle %s(t)%s(0) \\rangle$' % (self.flux1_name, self.flux2_name))
            ax.set_xlabel('$t$ ')
        return (fig, ax)

    def plot_bundle(self, ax, dim, alpha=0.4, every=1):
        """Plots all the correlations from the individual runs in the given direction TODO: Add the normalised option Args: ax axes object fig Figure dim is the dimension, for example:in a 3D vector, 0-x, 1-y, 2-z and 3-total. alpha is the transparency of the filling every to not have so many points The axis label is given here but it could be renamed later"""
        for array in self.arrays:
            cor = array[dim]
            if isinstance(cor[0], un.UFloat):
                y = np.array([i.n for i in cor])
                y_error = np.array([i.s for i in cor])
                times = self.times[::every]
                y = y[::every]
                y_error = y_error[::every]
                ax.plot(times, y, label=self.dic_label[dim])
                ax.fill_between(times, y - y_error, y + y_error, alpha=0.4)
            else:
                ax.plot(self.times[::every], cor[::every], label=self.dic_label[dim])
        return ax


maxSubArray

76.21% 作者：z1m 动态规划，原地修改数组

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

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def maxSubArray_org(self, nums: List[int]) -> int: 76.21 % 思路 滑窗遍历列表； sum < 0 时候 left+1 直到 与right重合； right + 1，重新计算 current 值；
- def maxSubArray(self, nums: List[int]) -> int: 76.21% 作者：z1m 动态规划，原地修改数组

Implement the Python class `Solution` described below.

Class description:
Implement the Solution class.

Method signatures and docstrings:
- def maxSubArray_org(self, nums: List[int]) -> int: 76.21 % 思路 滑窗遍历列表； sum < 0 时候 left+1 直到 与right重合； right + 1，重新计算 current 值；
- def maxSubArray(self, nums: List[int]) -> int: 76.21% 作者：z1m 动态规划，原地修改数组

<|skeleton|>
class Solution:

    def maxSubArray_org(self, nums: List[int]) -> int:
        """76.21 % 思路 滑窗遍历列表； sum < 0 时候 left+1 直到 与right重合； right + 1，重新计算 current 值；"""
        <|body_0|>

    def maxSubArray(self, nums: List[int]) -> int:
        """76.21% 作者：z1m 动态规划，原地修改数组"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        if len(nums) == 0:
            return
        left = 0
        current, maxsum = (0, nums[0])
        for right in range(len(nums)):
            current += nums[right]
            maxsum = max(maxsum, current)
            while current <= 0 and left <= right:
                current -= nums[left]
                left += 1
        return maxsum
<|end_body_0|>

<|body_start_1|>
        maxnum = nums[0]
        for i in range(1, len(nums)):
            if nums[i - 1] > 0:
                nums[i] += nums[i - 1]
            maxnum = max(maxnum, nums[i])
        return maxnum
<|end_body_1|>

Solution:
class Solution:
    def maxSubArray_org(self, nums: List[int]) -> int:
        """76.21 % 思路 滑窗遍历列表； sum < 0 时候 left+1 直到 与right重合； right + 1，重新计算 current 值；"""
        if len(nums) == 0:
            return
        left = 0
        current, maxsum = (0, nums[0])
        for right in range(len(nums)):
            current += nums[right]
            maxsum = max(maxsum, current)
            while current <= 0 and left <= right:
                current -= nums[left]
                left += 1
        return maxsum

    def maxSubArray(self, nums: List[int]) -> int:
        """76.21% 作者：z1m 动态规划，原地修改数组"""
        maxnum = nums[0]
        for i in range(1, len(nums)):
            if nums[i - 1] > 0:
                nums[i] += nums[i - 1]
            maxnum = max(maxnum, nums[i])
        return maxnum


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

Class description:
SPPAS PitchTier, IntensityTier, etc reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: contact@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Support of Praat file formats with only one tier of numerical values like pitch, intensity, etc.

Method signatures and docstrings:
- def __init__(self, name=None): Initialize a new sppasBaseNumericalTier instance. :param name: (str) This transcription name.
- def _read(self, filename): Read a file of any numerical file type. :param filename: (str) the input file name
- def _write(self, filename, file_type): Write a file of the given file type. :param filename: (str) :param file_type: (str) Name of the file type (PitchTier, IntensityTier...)

Implement the Python class `sppasBaseNumericalTier` described below.

Class description:
SPPAS PitchTier, IntensityTier, etc reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: contact@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Support of Praat file formats with only one tier of numerical values like pitch, intensity, etc.

Method signatures and docstrings:
- def __init__(self, name=None): Initialize a new sppasBaseNumericalTier instance. :param name: (str) This transcription name.
- def _read(self, filename): Read a file of any numerical file type. :param filename: (str) the input file name
- def _write(self, filename, file_type): Write a file of the given file type. :param filename: (str) :param file_type: (str) Name of the file type (PitchTier, IntensityTier...)

<|skeleton|>
class sppasBaseNumericalTier:
    """SPPAS PitchTier, IntensityTier, etc reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: contact@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Support of Praat file formats with only one tier of numerical values like pitch, intensity, etc."""

    def __init__(self, name=None):
        """Initialize a new sppasBaseNumericalTier instance. :param name: (str) This transcription name."""
        <|body_0|>

    def _read(self, filename):
        """Read a file of any numerical file type. :param filename: (str) the input file name"""
        <|body_1|>

    def _write(self, filename, file_type):
        """Write a file of the given file type. :param filename: (str) :param file_type: (str) Name of the file type (PitchTier, IntensityTier...)"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if name is None:
            name = self.__class__.__name__
        super(sppasBaseNumericalTier, self).__init__(name)
        self._accept_multi_tiers = False
        self._accept_no_tiers = False
        self._accept_metadata = False
        self._accept_ctrl_vocab = False
        self._accept_media = False
        self._accept_hierarchy = False
        self._accept_interval = False
        self._accept_disjoint = False
        self._accept_alt_localization = False
        self._accept_alt_tag = False
        self._accept_radius = False
        self._accept_gaps = False
        self._accept_overlaps = False
<|end_body_0|>

<|body_start_1|>
        lines = load(filename, sg.__encoding__)
        if len(lines) < 7:
            raise AioLineFormatError(len(lines), lines[-1])
        file_type = sppasBasePraat._parse_string(lines[1])
        tier = self.create_tier(file_type)
        last_line = len(lines) - 1
        cur_line = 6
        is_long = not lines[5].strip().isdigit()
        while cur_line < last_line:
            if is_long:
                cur_line += 1
                if cur_line > len(lines):
                    raise AioLineFormatError(cur_line, lines[-1])
            midpoint = sppasBasePraat._parse_float(lines[cur_line], cur_line + 1)
            localization = sppasBasePraat.make_point(midpoint)
            cur_line += 1
            if cur_line >= len(lines):
                raise AioLineFormatError(cur_line, lines[-1])
            value = sppasBasePraat._parse_float(lines[cur_line], cur_line + 1)
            tag = sppasTag(value, tag_type='float')
            tier.create_annotation(sppasLocation(localization), sppasLabel(tag))
            cur_line += 1
<|end_body_1|>

<|body_start_2|>
        if self.is_empty():
            raise AioNoTiersError(file_type)
        if len(self) != 1:
            tier = self.find(file_type, case_sensitive=False)
            if tier is None:
                raise AioMultiTiersError('Praat ' + file_type)
        else:
            tier = self[0]
        if self.is_empty() is True:
            raise AioEmptyTierError('Praat ' + file_type, tier.get_name())
        if tier.is_point() is False:
            raise AioLocationTypeError(file_type, 'intervals')
        min_time_point = tier.get_first_point()
        max_time_point = tier.get_last_point()
        with codecs.open(filename, 'w', sg.__encoding__, buffering=8096) as fp:
            fp.write(sppasBasePraat._serialize_header(file_type, min_time_point.get_midpoint(), max_time_point.get_midpoint()))
            fp.write('points: size = {:d}\n'.format(len(tier)))
            for a, annotation in enumerate(tier):
                content = 'points [{:d}]:\n'.format(a + 1)
                content += '\tnumber = {}\n'.format(annotation.get_lowest_localization().get_midpoint())
                content += sppasBasePraat._serialize_labels_value(annotation.get_labels())
                fp.write(content)
            fp.close()
<|end_body_2|>

Solution:
class sppasBaseNumericalTier:
    """SPPAS PitchTier, IntensityTier, etc reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: contact@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Support of Praat file formats with only one tier of numerical values like pitch, intensity, etc."""

    def __init__(self, name=None):
        """Initialize a new sppasBaseNumericalTier instance. :param name: (str) This transcription name."""
        if name is None:
            name = self.__class__.__name__
        super(sppasBaseNumericalTier, self).__init__(name)
        self._accept_multi_tiers = False
        self._accept_no_tiers = False
        self._accept_metadata = False
        self._accept_ctrl_vocab = False
        self._accept_media = False
        self._accept_hierarchy = False
        self._accept_interval = False
        self._accept_disjoint = False
        self._accept_alt_localization = False
        self._accept_alt_tag = False
        self._accept_radius = False
        self._accept_gaps = False
        self._accept_overlaps = False

    def _read(self, filename):
        """Read a file of any numerical file type. :param filename: (str) the input file name"""
        lines = load(filename, sg.__encoding__)
        if len(lines) < 7:
            raise AioLineFormatError(len(lines), lines[-1])
        file_type = sppasBasePraat._parse_string(lines[1])
        tier = self.create_tier(file_type)
        last_line = len(lines) - 1
        cur_line = 6
        is_long = not lines[5].strip().isdigit()
        while cur_line < last_line:
            if is_long:
                cur_line += 1
                if cur_line > len(lines):
                    raise AioLineFormatError(cur_line, lines[-1])
            midpoint = sppasBasePraat._parse_float(lines[cur_line], cur_line + 1)
            localization = sppasBasePraat.make_point(midpoint)
            cur_line += 1
            if cur_line >= len(lines):
                raise AioLineFormatError(cur_line, lines[-1])
            value = sppasBasePraat._parse_float(lines[cur_line], cur_line + 1)
            tag = sppasTag(value, tag_type='float')
            tier.create_annotation(sppasLocation(localization), sppasLabel(tag))
            cur_line += 1

    def _write(self, filename, file_type):
        """Write a file of the given file type. :param filename: (str) :param file_type: (str) Name of the file type (PitchTier, IntensityTier...)"""
        if self.is_empty():
            raise AioNoTiersError(file_type)
        if len(self) != 1:
            tier = self.find(file_type, case_sensitive=False)
            if tier is None:
                raise AioMultiTiersError('Praat ' + file_type)
        else:
            tier = self[0]
        if self.is_empty() is True:
            raise AioEmptyTierError('Praat ' + file_type, tier.get_name())
        if tier.is_point() is False:
            raise AioLocationTypeError(file_type, 'intervals')
        min_time_point = tier.get_first_point()
        max_time_point = tier.get_last_point()
        with codecs.open(filename, 'w', sg.__encoding__, buffering=8096) as fp:
            fp.write(sppasBasePraat._serialize_header(file_type, min_time_point.get_midpoint(), max_time_point.get_midpoint()))
            fp.write('points: size = {:d}\n'.format(len(tier)))
            for a, annotation in enumerate(tier):
                content = 'points [{:d}]:\n'.format(a + 1)
                content += '\tnumber = {}\n'.format(annotation.get_lowest_localization().get_midpoint())
                content += sppasBasePraat._serialize_labels_value(annotation.get_labels())
                fp.write(content)
            fp.close()


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

Class description:
Implement the RemoteConnection class.

Method signatures and docstrings:
- def remote_inject_response(self, input_strings, delay=0.0): Simulate remote endpoint that sends response. Response is given as strings.
- def remote_inject(self, input_strings, delay=0.0): Simulate remote endpoint that sends response. Response is given as strings.

Implement the Python class `RemoteConnection` described below.

Class description:
Implement the RemoteConnection class.

Method signatures and docstrings:
- def remote_inject_response(self, input_strings, delay=0.0): Simulate remote endpoint that sends response. Response is given as strings.
- def remote_inject(self, input_strings, delay=0.0): Simulate remote endpoint that sends response. Response is given as strings.

<|skeleton|>
class RemoteConnection:

    def remote_inject_response(self, input_strings, delay=0.0):
        """Simulate remote endpoint that sends response. Response is given as strings."""
        <|body_0|>

    def remote_inject(self, input_strings, delay=0.0):
        """Simulate remote endpoint that sends response. Response is given as strings."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        try:
            in_bytes = [data.encode('utf-8') for data in input_strings]
        except UnicodeDecodeError:
            in_bytes = [data.decode('utf-8').encode('utf-8') for data in input_strings]
        self.inject_response(in_bytes, delay)
<|end_body_0|>

<|body_start_1|>
        try:
            in_bytes = [data.encode('utf-8') for data in input_strings]
        except UnicodeDecodeError:
            in_bytes = [data.decode('utf-8').encode('utf-8') for data in input_strings]
        self.inject(in_bytes, delay)
<|end_body_1|>

Solution:
class RemoteConnection:
    def remote_inject_response(self, input_strings, delay=0.0):
        """Simulate remote endpoint that sends response. Response is given as strings."""
        try:
            in_bytes = [data.encode('utf-8') for data in input_strings]
        except UnicodeDecodeError:
            in_bytes = [data.decode('utf-8').encode('utf-8') for data in input_strings]
        self.inject_response(in_bytes, delay)

    def remote_inject(self, input_strings, delay=0.0):
        """Simulate remote endpoint that sends response. Response is given as strings."""
        try:
            in_bytes = [data.encode('utf-8') for data in input_strings]
        except UnicodeDecodeError:
            in_bytes = [data.decode('utf-8').encode('utf-8') for data in input_strings]
        self.inject(in_bytes, delay)


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

Class description:
Customized collater for Pytorch DataLoader in training.

Method signatures and docstrings:
- def __init__(self, batch_max_steps=20480, hop_size=256, aux_context_window=2, use_noise_input=False): Initialize customized collater for PyTorch DataLoader. Args: batch_max_steps (int): The maximum length of input signal in batch. hop_size (int): Hop size of auxiliary features. aux_context_window (int): Context window size for auxiliary feature conv. use_noise_input (bool): Whether to use noise input.
- def __call__(self, batch): Convert into batch tensors. Args: batch (list): list of tuple of the pair of audio and features. Returns: Tensor: Gaussian noise batch (B, 1, T). Tensor: Auxiliary feature batch (B, C, T'), where T = (T' - 2 * aux_context_window) * hop_size. Tensor: Target signal batch (B, 1, T).
- def _adjust_length(self, x, c): Adjust the audio and feature lengths. Note: Basically we assume that the length of x and c are adjusted through preprocessing stage, but if we use other library processed features, this process will be needed.

Implement the Python class `Collater` described below.

Class description:
Customized collater for Pytorch DataLoader in training.

Method signatures and docstrings:
- def __init__(self, batch_max_steps=20480, hop_size=256, aux_context_window=2, use_noise_input=False): Initialize customized collater for PyTorch DataLoader. Args: batch_max_steps (int): The maximum length of input signal in batch. hop_size (int): Hop size of auxiliary features. aux_context_window (int): Context window size for auxiliary feature conv. use_noise_input (bool): Whether to use noise input.
- def __call__(self, batch): Convert into batch tensors. Args: batch (list): list of tuple of the pair of audio and features. Returns: Tensor: Gaussian noise batch (B, 1, T). Tensor: Auxiliary feature batch (B, C, T'), where T = (T' - 2 * aux_context_window) * hop_size. Tensor: Target signal batch (B, 1, T).
- def _adjust_length(self, x, c): Adjust the audio and feature lengths. Note: Basically we assume that the length of x and c are adjusted through preprocessing stage, but if we use other library processed features, this process will be needed.

<|skeleton|>
class Collater:
    """Customized collater for Pytorch DataLoader in training."""

    def __init__(self, batch_max_steps=20480, hop_size=256, aux_context_window=2, use_noise_input=False):
        """Initialize customized collater for PyTorch DataLoader. Args: batch_max_steps (int): The maximum length of input signal in batch. hop_size (int): Hop size of auxiliary features. aux_context_window (int): Context window size for auxiliary feature conv. use_noise_input (bool): Whether to use noise input."""
        <|body_0|>

    def __call__(self, batch):
        """Convert into batch tensors. Args: batch (list): list of tuple of the pair of audio and features. Returns: Tensor: Gaussian noise batch (B, 1, T). Tensor: Auxiliary feature batch (B, C, T'), where T = (T' - 2 * aux_context_window) * hop_size. Tensor: Target signal batch (B, 1, T)."""
        <|body_1|>

    def _adjust_length(self, x, c):
        """Adjust the audio and feature lengths. Note: Basically we assume that the length of x and c are adjusted through preprocessing stage, but if we use other library processed features, this process will be needed."""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if batch_max_steps % hop_size != 0:
            batch_max_steps += -(batch_max_steps % hop_size)
        assert batch_max_steps % hop_size == 0
        self.batch_max_steps = batch_max_steps
        self.batch_max_frames = batch_max_steps // hop_size
        self.hop_size = hop_size
        self.aux_context_window = aux_context_window
        self.use_noise_input = use_noise_input
        self.start_offset = aux_context_window
        self.end_offset = -(self.batch_max_frames + aux_context_window)
        self.mel_threshold = self.batch_max_frames + 2 * aux_context_window
<|end_body_0|>

<|body_start_1|>
        batch = [self._adjust_length(*b) for b in batch if len(b[1]) > self.mel_threshold]
        xs, cs = ([b[0] for b in batch], [b[1] for b in batch])
        c_lengths = [len(c) for c in cs]
        start_frames = np.array([np.random.randint(self.start_offset, cl + self.end_offset) for cl in c_lengths])
        x_starts = start_frames * self.hop_size
        x_ends = x_starts + self.batch_max_steps
        c_starts = start_frames - self.aux_context_window
        c_ends = start_frames + self.batch_max_frames + self.aux_context_window
        y_batch = [x[start:end] for x, start, end in zip(xs, x_starts, x_ends)]
        c_batch = [c[start:end] for c, start, end in zip(cs, c_starts, c_ends)]
        y_batch = torch.tensor(y_batch, dtype=torch.float).unsqueeze(1)
        c_batch = torch.tensor(c_batch, dtype=torch.float).transpose(2, 1)
        if self.use_noise_input:
            z_batch = torch.randn(y_batch.size())
            return ((z_batch, c_batch), y_batch)
        else:
            return ((c_batch,), y_batch)
<|end_body_1|>

<|body_start_2|>
        if len(x) < len(c) * self.hop_size:
            x = np.pad(x, (0, len(c) * self.hop_size - len(x)), mode='edge')
        assert len(x) == len(c) * self.hop_size
        return (x, c)
<|end_body_2|>

Solution:
class Collater:
    """Customized collater for Pytorch DataLoader in training."""

    def __init__(self, batch_max_steps=20480, hop_size=256, aux_context_window=2, use_noise_input=False):
        """Initialize customized collater for PyTorch DataLoader. Args: batch_max_steps (int): The maximum length of input signal in batch. hop_size (int): Hop size of auxiliary features. aux_context_window (int): Context window size for auxiliary feature conv. use_noise_input (bool): Whether to use noise input."""
        if batch_max_steps % hop_size != 0:
            batch_max_steps += -(batch_max_steps % hop_size)
        assert batch_max_steps % hop_size == 0
        self.batch_max_steps = batch_max_steps
        self.batch_max_frames = batch_max_steps // hop_size
        self.hop_size = hop_size
        self.aux_context_window = aux_context_window
        self.use_noise_input = use_noise_input
        self.start_offset = aux_context_window
        self.end_offset = -(self.batch_max_frames + aux_context_window)
        self.mel_threshold = self.batch_max_frames + 2 * aux_context_window

    def __call__(self, batch):
        """Convert into batch tensors. Args: batch (list): list of tuple of the pair of audio and features. Returns: Tensor: Gaussian noise batch (B, 1, T). Tensor: Auxiliary feature batch (B, C, T'), where T = (T' - 2 * aux_context_window) * hop_size. Tensor: Target signal batch (B, 1, T)."""
        batch = [self._adjust_length(*b) for b in batch if len(b[1]) > self.mel_threshold]
        xs, cs = ([b[0] for b in batch], [b[1] for b in batch])
        c_lengths = [len(c) for c in cs]
        start_frames = np.array([np.random.randint(self.start_offset, cl + self.end_offset) for cl in c_lengths])
        x_starts = start_frames * self.hop_size
        x_ends = x_starts + self.batch_max_steps
        c_starts = start_frames - self.aux_context_window
        c_ends = start_frames + self.batch_max_frames + self.aux_context_window
        y_batch = [x[start:end] for x, start, end in zip(xs, x_starts, x_ends)]
        c_batch = [c[start:end] for c, start, end in zip(cs, c_starts, c_ends)]
        y_batch = torch.tensor(y_batch, dtype=torch.float).unsqueeze(1)
        c_batch = torch.tensor(c_batch, dtype=torch.float).transpose(2, 1)
        if self.use_noise_input:
            z_batch = torch.randn(y_batch.size())
            return ((z_batch, c_batch), y_batch)
        else:
            return ((c_batch,), y_batch)

    def _adjust_length(self, x, c):
        """Adjust the audio and feature lengths. Note: Basically we assume that the length of x and c are adjusted through preprocessing stage, but if we use other library processed features, this process will be needed."""
        if len(x) < len(c) * self.hop_size:
            x = np.pad(x, (0, len(c) * self.hop_size - len(x)), mode='edge')
        assert len(x) == len(c) * self.hop_size
        return (x, c)


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

Class description:
Types of mutexes that we can detect deadlocks.

Method signatures and docstrings:
- def get_mutex_type(thread_id, top_line): Returns the probable mutex type, based on the first line of the thread's stack. Returns None if not found.
- def get_mutex_owner_and_address_func_for_type(mutex_type): Returns a function to resolve the mutex owner and address for the given type. The returned function f has the following signature: f: args: (map of thread lwp -> thread id), blocked thread lwp returns: (lwp of thread owning mutex, mutex address) or (None, None) if not found. Returns None if there is no function for this mutex_type.

Implement the Python class `MutexType` described below.

Class description:
Types of mutexes that we can detect deadlocks.

Method signatures and docstrings:
- def get_mutex_type(thread_id, top_line): Returns the probable mutex type, based on the first line of the thread's stack. Returns None if not found.
- def get_mutex_owner_and_address_func_for_type(mutex_type): Returns a function to resolve the mutex owner and address for the given type. The returned function f has the following signature: f: args: (map of thread lwp -> thread id), blocked thread lwp returns: (lwp of thread owning mutex, mutex address) or (None, None) if not found. Returns None if there is no function for this mutex_type.

<|skeleton|>
class MutexType:
    """Types of mutexes that we can detect deadlocks."""

    def get_mutex_type(thread_id, top_line):
        """Returns the probable mutex type, based on the first line of the thread's stack. Returns None if not found."""
        <|body_0|>

    def get_mutex_owner_and_address_func_for_type(mutex_type):
        """Returns a function to resolve the mutex owner and address for the given type. The returned function f has the following signature: f: args: (map of thread lwp -> thread id), blocked thread lwp returns: (lwp of thread owning mutex, mutex address) or (None, None) if not found. Returns None if there is no function for this mutex_type."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        WAITLIST = ['__lll_lock_wait', 'futex_abstimed_wait', 'futex_abstimed_wait_cancelable', 'futex_reltimed_wait', 'futex_reltimed_wait_cancelable', 'futex_wait', 'futex_wait_cancelable']
        if is_thread_blocked_with_frame(thread_id, top_line, WAITLIST, 'pthread_mutex'):
            return MutexType.PTHREAD_MUTEX_T
        if is_thread_blocked_with_frame(thread_id, top_line, WAITLIST, 'pthread_rwlock'):
            return MutexType.PTHREAD_RWLOCK_T
        return None
<|end_body_0|>

<|body_start_1|>
        if mutex_type == MutexType.PTHREAD_MUTEX_T:
            return get_pthread_mutex_t_owner_and_address
        if mutex_type == MutexType.PTHREAD_RWLOCK_T:
            return get_pthread_rwlock_t_owner_and_address
        return None
<|end_body_1|>

Solution:
class MutexType:
    """Types of mutexes that we can detect deadlocks."""

    def get_mutex_type(thread_id, top_line):
        """Returns the probable mutex type, based on the first line of the thread's stack. Returns None if not found."""
        WAITLIST = ['__lll_lock_wait', 'futex_abstimed_wait', 'futex_abstimed_wait_cancelable', 'futex_reltimed_wait', 'futex_reltimed_wait_cancelable', 'futex_wait', 'futex_wait_cancelable']
        if is_thread_blocked_with_frame(thread_id, top_line, WAITLIST, 'pthread_mutex'):
            return MutexType.PTHREAD_MUTEX_T
        if is_thread_blocked_with_frame(thread_id, top_line, WAITLIST, 'pthread_rwlock'):
            return MutexType.PTHREAD_RWLOCK_T
        return None

    def get_mutex_owner_and_address_func_for_type(mutex_type):
        """Returns a function to resolve the mutex owner and address for the given type. The returned function f has the following signature: f: args: (map of thread lwp -> thread id), blocked thread lwp returns: (lwp of thread owning mutex, mutex address) or (None, None) if not found. Returns None if there is no function for this mutex_type."""
        if mutex_type == MutexType.PTHREAD_MUTEX_T:
            return get_pthread_mutex_t_owner_and_address
        if mutex_type == MutexType.PTHREAD_RWLOCK_T:
            return get_pthread_rwlock_t_owner_and_address
        return None


checkNextPlayer

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

Class description:
Implement the Player class.

Method signatures and docstrings:
- def __init__(self, charac, real): Class initialiser :param charac: Selected character :param boolean real: True if the character is human
- def rollDice(): Rolls the dice :return: a tuple containing the value of each die.
- def checkNextPlayer(self, cards, players): Finds the first card in the suggested card list that another player is holding. If no player has the card, None is returned :param cards: List of cards suggested :param players: List of players in the game :return: Card or None

Implement the Python class `Player` described below.

Class description:
Implement the Player class.

Method signatures and docstrings:
- def __init__(self, charac, real): Class initialiser :param charac: Selected character :param boolean real: True if the character is human
- def rollDice(): Rolls the dice :return: a tuple containing the value of each die.
- def checkNextPlayer(self, cards, players): Finds the first card in the suggested card list that another player is holding. If no player has the card, None is returned :param cards: List of cards suggested :param players: List of players in the game :return: Card or None

<|skeleton|>
class Player:

    def __init__(self, charac, real):
        """Class initialiser :param charac: Selected character :param boolean real: True if the character is human"""
        <|body_0|>

    def rollDice():
        """Rolls the dice :return: a tuple containing the value of each die."""
        <|body_1|>

    def checkNextPlayer(self, cards, players):
        """Finds the first card in the suggested card list that another player is holding. If no player has the card, None is returned :param cards: List of cards suggested :param players: List of players in the game :return: Card or None"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        self.cards = []
        self.character = charac
        self.name = charac.name
        self.real = real
<|end_body_0|>

<|body_start_1|>
        dice1 = randint(1, 6)
        dice2 = randint(1, 6)
        return (dice1, dice2)
<|end_body_1|>

<|body_start_2|>
        for i, p in enumerate(players):
            if p == self:
                for j in range(1, len(players)):
                    if i + j > len(players) - 1:
                        j = -i
                    for card in players[i + j].cards:
                        print('check card ' + card.name)
                        if card in cards:
                            return card
                    print('Check next player')
                return None
<|end_body_2|>

Solution:
class Player:
    def __init__(self, charac, real):
        """Class initialiser :param charac: Selected character :param boolean real: True if the character is human"""
        self.cards = []
        self.character = charac
        self.name = charac.name
        self.real = real

    def rollDice():
        """Rolls the dice :return: a tuple containing the value of each die."""
        dice1 = randint(1, 6)
        dice2 = randint(1, 6)
        return (dice1, dice2)

    def checkNextPlayer(self, cards, players):
        """Finds the first card in the suggested card list that another player is holding. If no player has the card, None is returned :param cards: List of cards suggested :param players: List of players in the game :return: Card or None"""
        for i, p in enumerate(players):
            if p == self:
                for j in range(1, len(players)):
                    if i + j > len(players) - 1:
                        j = -i
                    for card in players[i + j].cards:
                        print('check card ' + card.name)
                        if card in cards:
                            return card
                    print('Check next player')
                return None