



import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Union[str, Any]:



    """simple docstring"""






    return 1.0 / (1.0 + np.exp(-_outputs		))




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    A			:	Optional[Any]										=       np.max(_outputs				,					axis=-1				,					keepdims=lowerCamelCase__		)
    A			:	Union[str, Any]										=       np.exp(_outputs - maxes		)
    return shifted_exp / shifted_exp.sum(axis=-1				,					keepdims=lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__					(		a_   ):




    '''simple docstring'''






    __lowerCamelCase      :			str =				"sigmoid"
    __lowerCamelCase      :			Optional[int] =				"softmax"
    __lowerCamelCase      :			Optional[int] =				"none"






@add_end_docstrings(
    a_			,						r"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "			,						)
class SCREAMING_SNAKE_CASE__					(		a_   ):




    '''simple docstring'''






    __lowerCamelCase      :			str =				False
    __lowerCamelCase      :			Optional[int] =				ClassificationFunction.NONE






    def __init__(	self,		**lowerCamelCase__		):
        super().__init__(**lowerCamelCase__		)

        self.check_model_type(
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
            if self.framework == """tf"""
            else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__=None,		lowerCamelCase__=None,		lowerCamelCase__="",		**lowerCamelCase__		):
        A			:	Union[str, Any]										=       tokenizer_kwargs

        A			:	Any										=       {}
        if hasattr(self.model.config,		"""return_all_scores"""		) and return_all_scores is None:
            A			:	str										=       self.model.config.return_all_scores

        if isinstance(lowerCamelCase__,		lowerCamelCase__		) or top_k is None:
            A			:	Tuple										=       top_k
            A			:	int										=       False
        elif return_all_scores is not None:
            warnings.warn(
                """`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"""
                """ `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.""",		lowerCamelCase__,		)
            if return_all_scores:
                A			:	Optional[Any]										=       None
            else:
                A			:	int										=       1

        if isinstance(lowerCamelCase__,		lowerCamelCase__		):
            A			:	int										=       ClassificationFunction[function_to_apply.upper()]

        if function_to_apply is not None:
            A			:	Any										=       function_to_apply
        return preprocess_params, {}, postprocess_params






    def __call__(	self,		*lowerCamelCase__,		**lowerCamelCase__		):
        A			:	int										=       super().__call__(*lowerCamelCase__,		**lowerCamelCase__		)
        # TODO try and retrieve it in a nicer way from _sanitize_parameters.
        A			:	Tuple										=       """top_k""" not in kwargs
        if isinstance(args[0],		lowerCamelCase__		) and _legacy:
            # This pipeline is odd, and return a list when single item is run
            return [result]
        else:
            return result






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		**lowerCamelCase__		):
        A			:	int										=       self.framework
        if isinstance(lowerCamelCase__,		lowerCamelCase__		):
            return self.tokenizer(**lowerCamelCase__,		return_tensors=lowerCamelCase__,		**lowerCamelCase__		)
        elif isinstance(lowerCamelCase__,		lowerCamelCase__		) and len(lowerCamelCase__		) == 1 and isinstance(inputs[0],		lowerCamelCase__		) and len(inputs[0]		) == 2:
            # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
            return self.tokenizer(
                text=inputs[0][0],		text_pair=inputs[0][1],		return_tensors=lowerCamelCase__,		**lowerCamelCase__		)
        elif isinstance(lowerCamelCase__,		lowerCamelCase__		):
            # This is likely an invalid usage of the pipeline attempting to pass text pairs.
            raise ValueError(
                """The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
                """ dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."""		)
        return self.tokenizer(lowerCamelCase__,		return_tensors=lowerCamelCase__,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.model(**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__=None,		lowerCamelCase__=1,		lowerCamelCase__=True		):







        if function_to_apply is None:
            if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                A			:	Dict										=       ClassificationFunction.SIGMOID
            elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                A			:	List[str]										=       ClassificationFunction.SOFTMAX
            elif hasattr(self.model.config,		"""function_to_apply"""		) and function_to_apply is None:
                A			:	str										=       self.model.config.function_to_apply
            else:
                A			:	Optional[int]										=       ClassificationFunction.NONE

        A			:	Dict										=       model_outputs["""logits"""][0]
        A			:	Optional[int]										=       outputs.numpy()

        if function_to_apply == ClassificationFunction.SIGMOID:
            A			:	Dict										=       sigmoid(lowerCamelCase__		)
        elif function_to_apply == ClassificationFunction.SOFTMAX:
            A			:	Any										=       softmax(lowerCamelCase__		)
        elif function_to_apply == ClassificationFunction.NONE:
            A			:	Optional[Any]										=       outputs
        else:
            raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''		)

        if top_k == 1 and _legacy:
            return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

        A			:	Optional[int]										=       [
            {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(lowerCamelCase__		)
        ]
        if not _legacy:
            dict_scores.sort(key=lambda lowerCamelCase__		: x["score"],		reverse=lowerCamelCase__		)
            if top_k is not None:
                A			:	List[str]										=       dict_scores[:top_k]
        return dict_scores







import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu






class SCREAMING_SNAKE_CASE__					(		nn.Module   ):




    '''simple docstring'''






    def __init__(	self		):
        super().__init__()
        A			:	Union[str, Any]										=       nn.Linear(3,		4		)
        A			:	Union[str, Any]										=       nn.BatchNormad(4		)
        A			:	Optional[Any]										=       nn.Linear(4,		5		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__		)		)		)






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		*lowerCamelCase__,		**lowerCamelCase__		):
        return (args[0] + 1,) + args[1:], kwargs






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        return output + 1






class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       ModelForTest()
        A			:	Any										=       ModelHook()

        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)
        self.assertEqual(test_model._hf_hook,		lowerCamelCase__		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""_old_forward"""		)		)

        # Check adding the hook did not change the name or the signature
        self.assertEqual(test_model.forward.__name__,		"""forward"""		)
        self.assertListEqual(list(inspect.signature(test_model.forward		).parameters		),		["""x"""]		)

        remove_hook_from_module(lowerCamelCase__		)
        self.assertFalse(hasattr(lowerCamelCase__,		"""_hf_hook"""		)		)
        self.assertFalse(hasattr(lowerCamelCase__,		"""_old_forward"""		)		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       ModelForTest()
        A			:	Optional[int]										=       ModelHook()

        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__,		append=lowerCamelCase__		)

        self.assertEqual(isinstance(test_model._hf_hook,		lowerCamelCase__		),		lowerCamelCase__		)
        self.assertEqual(len(test_model._hf_hook.hooks		),		2		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""_old_forward"""		)		)

        # Check adding the hook did not change the name or the signature
        self.assertEqual(test_model.forward.__name__,		"""forward"""		)
        self.assertListEqual(list(inspect.signature(test_model.forward		).parameters		),		["""x"""]		)

        remove_hook_from_module(lowerCamelCase__		)
        self.assertFalse(hasattr(lowerCamelCase__,		"""_hf_hook"""		)		)
        self.assertFalse(hasattr(lowerCamelCase__,		"""_old_forward"""		)		)






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       ModelForTest()
        A			:	Tuple										=       torch.randn(2,		3		)
        A			:	Optional[int]										=       test_model(x + 1		)
        A			:	List[str]										=       test_model(x + 2		)

        A			:	List[str]										=       PreForwardHook()
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)
        A			:	List[str]										=       test_model(lowerCamelCase__		)
        self.assertTrue(torch.allclose(lowerCamelCase__,		lowerCamelCase__,		atol=1e-5		)		)

        # Attaching a hook to a model when it already has one replaces, does not chain
        A			:	Optional[Any]										=       PreForwardHook()
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)
        A			:	Tuple										=       test_model(lowerCamelCase__		)
        self.assertTrue(torch.allclose(lowerCamelCase__,		lowerCamelCase__,		atol=1e-5		)		)

        # You need to use the sequential hook to chain two or more hooks
        A			:	Any										=       SequentialHook(PreForwardHook(),		PreForwardHook()		)
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)

        A			:	Optional[Any]										=       test_model(lowerCamelCase__		)
        assert torch.allclose(lowerCamelCase__,		lowerCamelCase__,		atol=1e-5		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       ModelForTest()
        A			:	Any										=       torch.randn(2,		3		)
        A			:	Any										=       test_model(lowerCamelCase__		)

        A			:	List[Any]										=       PostForwardHook()
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)
        A			:	str										=       test_model(lowerCamelCase__		)
        self.assertTrue(torch.allclose(lowerCamelCase__,		output + 1,		atol=1e-5		)		)

        # Attaching a hook to a model when it already has one replaces, does not chain
        A			:	Tuple										=       PostForwardHook()
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)
        A			:	Optional[int]										=       test_model(lowerCamelCase__		)
        self.assertTrue(torch.allclose(lowerCamelCase__,		output + 1,		atol=1e-5		)		)

        # You need to use the sequential hook to chain two or more hooks
        A			:	Dict										=       SequentialHook(PostForwardHook(),		PostForwardHook()		)
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)

        A			:	List[str]										=       test_model(lowerCamelCase__		)
        assert torch.allclose(lowerCamelCase__,		output + 2,		atol=1e-5		)






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       ModelForTest()
        A			:	Tuple										=       torch.randn(2,		3		)
        A			:	Union[str, Any]										=       test_model(lowerCamelCase__		)

        A			:	List[Any]										=       PostForwardHook()
        add_hook_to_module(lowerCamelCase__,		lowerCamelCase__		)
        A			:	List[Any]										=       test_model(lowerCamelCase__		)
        self.assertTrue(torch.allclose(lowerCamelCase__,		output + 1		)		)
        self.assertTrue(outputa.requires_grad		)

        A			:	int										=       True
        A			:	Tuple										=       test_model(lowerCamelCase__		)
        self.assertFalse(outputa.requires_grad		)






    @require_multi_gpu
    def       _lowerCAmelCase (	self		):
        A			:	int										=       ModelForTest()
        # Everything is on CPU
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)

        # This will move each submodule on different devices
        add_hook_to_module(model.lineara,		AlignDevicesHook(execution_device=0		)		)
        add_hook_to_module(model.batchnorm,		AlignDevicesHook(execution_device=0		)		)
        add_hook_to_module(model.lineara,		AlignDevicesHook(execution_device=1		)		)

        self.assertEqual(model.lineara.weight.device,		torch.device(0		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device(0		)		)
        self.assertEqual(model.batchnorm.running_mean.device,		torch.device(0		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device(1		)		)

        # We can still make a forward pass. The input does not need to be on any particular device
        A			:	str										=       torch.randn(2,		3		)
        A			:	int										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		torch.device(1		)		)

        # We can add a general hook to put back output on same device as input.
        add_hook_to_module(lowerCamelCase__,		AlignDevicesHook(io_same_device=lowerCamelCase__		)		)
        A			:	int										=       torch.randn(2,		3		).to(0		)
        A			:	str										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		torch.device(0		)		)






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       ModelForTest()

        # Everything is on CPU
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)

        # This will move each submodule on different devices
        A			:	int										=       {"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

        add_hook_to_module(model.lineara,		AlignDevicesHook(**lowerCamelCase__		)		)
        add_hook_to_module(model.batchnorm,		AlignDevicesHook(**lowerCamelCase__		)		)
        add_hook_to_module(model.lineara,		AlignDevicesHook(**lowerCamelCase__		)		)

        # Parameters have been offloaded, so on the meta device
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        # Buffers are not included in the offload by default, so are on the execution device
        A			:	Dict										=       torch.device(hook_kwargs["""execution_device"""]		)
        self.assertEqual(model.batchnorm.running_mean.device,		lowerCamelCase__		)

        A			:	int										=       torch.randn(2,		3		)
        A			:	List[Any]										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		lowerCamelCase__		)

        # Removing hooks loads back the weights in the model.
        remove_hook_from_module(model.lineara		)
        remove_hook_from_module(model.batchnorm		)
        remove_hook_from_module(model.lineara		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)

        # Now test with buffers included in the offload
        A			:	int										=       {
            """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
            """offload""": True,
            """offload_buffers""": True,
        }

        add_hook_to_module(model.lineara,		AlignDevicesHook(**lowerCamelCase__		)		)
        add_hook_to_module(model.batchnorm,		AlignDevicesHook(**lowerCamelCase__		)		)
        add_hook_to_module(model.lineara,		AlignDevicesHook(**lowerCamelCase__		)		)

        # Parameters have been offloaded, so on the meta device, buffers included
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.running_mean.device,		torch.device("""meta"""		)		)

        A			:	int										=       torch.randn(2,		3		)
        A			:	str										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		lowerCamelCase__		)

        # Removing hooks loads back the weights in the model.
        remove_hook_from_module(model.lineara		)
        remove_hook_from_module(model.batchnorm		)
        remove_hook_from_module(model.lineara		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       ModelForTest()

        # Everything is on CPU
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)

        # This will move each submodule on different devices
        A			:	Tuple										=       0 if torch.cuda.is_available() else """cpu"""
        attach_align_device_hook(lowerCamelCase__,		execution_device=lowerCamelCase__,		offload=lowerCamelCase__		)

        # Parameters have been offloaded, so on the meta device
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        # Buffers are not included in the offload by default, so are on the execution device
        A			:	Optional[int]										=       torch.device(lowerCamelCase__		)
        self.assertEqual(model.batchnorm.running_mean.device,		lowerCamelCase__		)

        A			:	List[str]										=       torch.randn(2,		3		)
        A			:	Optional[int]										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		lowerCamelCase__		)

        # Removing hooks loads back the weights in the model.
        remove_hook_from_submodules(lowerCamelCase__		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)

        # Now test with buffers included in the offload
        attach_align_device_hook(lowerCamelCase__,		execution_device=lowerCamelCase__,		offload=lowerCamelCase__,		offload_buffers=lowerCamelCase__		)

        # Parameters have been offloaded, so on the meta device, buffers included
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.running_mean.device,		torch.device("""meta"""		)		)

        A			:	List[str]										=       torch.randn(2,		3		)
        A			:	List[str]										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		lowerCamelCase__		)

        # Removing hooks loads back the weights in the model.
        remove_hook_from_submodules(lowerCamelCase__		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)






    def       _lowerCAmelCase (	self		):
        A			:	int										=       ModelForTest()

        # Everything is on CPU
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)

        # This will move each submodule on different devices
        A			:	List[str]										=       0 if torch.cuda.is_available() else """cpu"""
        attach_align_device_hook(
            lowerCamelCase__,		execution_device=lowerCamelCase__,		offload=lowerCamelCase__,		weights_map=model.state_dict()		)

        # Parameters have been offloaded, so on the meta device
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        # Buffers are not included in the offload by default, so are on the execution device
        A			:	Any										=       torch.device(lowerCamelCase__		)
        self.assertEqual(model.batchnorm.running_mean.device,		lowerCamelCase__		)

        A			:	Optional[Any]										=       torch.randn(2,		3		)
        A			:	Tuple										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		lowerCamelCase__		)

        # Removing hooks loads back the weights in the model.
        remove_hook_from_submodules(lowerCamelCase__		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)

        # Now test with buffers included in the offload
        attach_align_device_hook(
            lowerCamelCase__,		execution_device=lowerCamelCase__,		offload=lowerCamelCase__,		weights_map=model.state_dict(),		offload_buffers=lowerCamelCase__,		)

        # Parameters have been offloaded, so on the meta device, buffers included
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""meta"""		)		)
        self.assertEqual(model.batchnorm.running_mean.device,		torch.device("""meta"""		)		)

        A			:	List[Any]										=       torch.randn(2,		3		)
        A			:	Dict										=       model(lowerCamelCase__		)
        self.assertEqual(output.device,		lowerCamelCase__		)

        # Removing hooks loads back the weights in the model.
        remove_hook_from_submodules(lowerCamelCase__		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.batchnorm.weight.device,		torch.device("""cpu"""		)		)
        self.assertEqual(model.lineara.weight.device,		torch.device("""cpu"""		)		)


