
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 					lowercase__(							nn.Module				):



	"""simple docstring"""




	def __init__(			self    :     Optional[int]			)			->	Optional[Any]:
					super().__init__()
					lowercase_	    =   nn.Linear(3			,				4			)
					lowercase_	    =   nn.BatchNormad(4			)
					lowercase_	    =   nn.Linear(4			,				5			)




	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	Tuple:
					return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_			)			)			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     int			,				*SCREAMING_SNAKE_CASE_    :     Dict			,				**SCREAMING_SNAKE_CASE_    :     str			)			->	Optional[Any]:
					return (args[0] + 1,) + args[1:], kwargs

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	List[str]:
					return output + 1

class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					lowercase_	    =   ModelForTest()
					lowercase_	    =   ModelHook()

					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					self.assertEqual(test_model._hf_hook			,				SCREAMING_SNAKE_CASE_			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''_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(SCREAMING_SNAKE_CASE_			)
					self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_			,				'''_hf_hook'''			)			)
					self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_			,				'''_old_forward'''			)			)
	def 		_lowercase    (			self    :     int			)			->	Tuple:
					lowercase_	    =   ModelForTest()
					lowercase_	    =   ModelHook()

					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				append=SCREAMING_SNAKE_CASE_			)

					self.assertEqual(isinstance(test_model._hf_hook			,				SCREAMING_SNAKE_CASE_			)			,				SCREAMING_SNAKE_CASE_			)
					self.assertEqual(len(test_model._hf_hook.hooks			)			,				2			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''_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(SCREAMING_SNAKE_CASE_			)
					self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_			,				'''_hf_hook'''			)			)
					self.assertFalse(hasattr(SCREAMING_SNAKE_CASE_			,				'''_old_forward'''			)			)
	def 		_lowercase    (			self    :     List[Any]			)			->	int:
					lowercase_	    =   ModelForTest()
					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   test_model(x + 1			)
					lowercase_	    =   test_model(x + 2			)

					lowercase_	    =   PreForwardHook()
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=1e-5			)			)

					# Attaching a hook to a model when it already has one replaces, does not chain
					lowercase_	    =   PreForwardHook()
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=1e-5			)			)

					# You need to use the sequential hook to chain two or more hooks
					lowercase_	    =   SequentialHook(PreForwardHook()			,				PreForwardHook()			)
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					assert torch.allclose(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				atol=1e-5			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	int:
					lowercase_	    =   ModelForTest()
					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   PostForwardHook()
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				output + 1			,				atol=1e-5			)			)

					# Attaching a hook to a model when it already has one replaces, does not chain
					lowercase_	    =   PostForwardHook()
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				output + 1			,				atol=1e-5			)			)

					# You need to use the sequential hook to chain two or more hooks
					lowercase_	    =   SequentialHook(PostForwardHook()			,				PostForwardHook()			)
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					assert torch.allclose(SCREAMING_SNAKE_CASE_			,				output + 2			,				atol=1e-5			)
	def 		_lowercase    (			self    :     List[str]			)			->	Optional[Any]:
					lowercase_	    =   ModelForTest()
					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   PostForwardHook()
					add_hook_to_module(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,				output + 1			)			)
					self.assertTrue(outputa.requires_grad			)

					lowercase_	    =   True
					lowercase_	    =   test_model(SCREAMING_SNAKE_CASE_			)
					self.assertFalse(outputa.requires_grad			)
	@require_multi_gpu
	def 		_lowercase    (			self    :     Optional[Any]			)			->	int:
					lowercase_	    =   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
					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			,				AlignDevicesHook(io_same_device=SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.randn(2			,				3			).to(0			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(output.device			,				torch.device(0			)			)
	def 		_lowercase    (			self    :     List[str]			)			->	Dict:
					lowercase_	    =   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
					lowercase_	    =   {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}

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

					# 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
					lowercase_	    =   torch.device(hook_kwargs['''execution_device''']			)
					self.assertEqual(model.batchnorm.running_mean.device			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(output.device			,				SCREAMING_SNAKE_CASE_			)

					# 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
					lowercase_	    =   {
					    '''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
					    '''offload''': True,
					    '''offload_buffers''': True,
					}

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

					# 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'''			)			)

					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(output.device			,				SCREAMING_SNAKE_CASE_			)

					# 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 		_lowercase    (			self    :     Optional[Any]			)			->	List[str]:
					lowercase_	    =   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
					lowercase_	    =   0 if torch.cuda.is_available() else '''cpu'''
					attach_align_device_hook(SCREAMING_SNAKE_CASE_			,				execution_device=SCREAMING_SNAKE_CASE_			,				offload=SCREAMING_SNAKE_CASE_			)

					# 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
					lowercase_	    =   torch.device(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(model.batchnorm.running_mean.device			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(output.device			,				SCREAMING_SNAKE_CASE_			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(SCREAMING_SNAKE_CASE_			)
					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(SCREAMING_SNAKE_CASE_			,				execution_device=SCREAMING_SNAKE_CASE_			,				offload=SCREAMING_SNAKE_CASE_			,				offload_buffers=SCREAMING_SNAKE_CASE_			)

					# 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'''			)			)

					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(output.device			,				SCREAMING_SNAKE_CASE_			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(SCREAMING_SNAKE_CASE_			)
					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 		_lowercase    (			self    :     Dict			)			->	Any:
					lowercase_	    =   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
					lowercase_	    =   0 if torch.cuda.is_available() else '''cpu'''
					attach_align_device_hook(
					    SCREAMING_SNAKE_CASE_			,				execution_device=SCREAMING_SNAKE_CASE_			,				offload=SCREAMING_SNAKE_CASE_			,				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
					lowercase_	    =   torch.device(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(model.batchnorm.running_mean.device			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(output.device			,				SCREAMING_SNAKE_CASE_			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(SCREAMING_SNAKE_CASE_			)
					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(
					    SCREAMING_SNAKE_CASE_			,				execution_device=SCREAMING_SNAKE_CASE_			,				offload=SCREAMING_SNAKE_CASE_			,				weights_map=model.state_dict()			,				offload_buffers=SCREAMING_SNAKE_CASE_			,				)

					# 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'''			)			)

					lowercase_	    =   torch.randn(2			,				3			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(output.device			,				SCREAMING_SNAKE_CASE_			)

					# Removing hooks loads back the weights in the model.
					remove_hook_from_submodules(SCREAMING_SNAKE_CASE_			)
					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'''			)			)






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__a         =   'src/transformers'
__a         =   'docs/source/en/tasks'




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


				'''simple docstring'''
				with open(snake_case__		,  '''r'''		,  encoding='''utf-8'''		,  newline='''\n'''   ) as f:
								lowercase_	    =   f.readlines()
				# Find the start prompt.
				lowercase_	    =   0
				while not lines[start_index].startswith(snake_case__   ):
								start_index += 1
				start_index += 1

				lowercase_	    =   start_index
				while not lines[end_index].startswith(snake_case__   ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]   ) <= 1:
								start_index += 1
				while len(lines[end_index]   ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
__a         =   direct_transformers_import(TRANSFORMERS_PATH)

__a         =   {
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__a         =   {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   TASK_GUIDE_TO_MODELS[task_guide]
				lowercase_	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__		,  set()   )
				lowercase_	    =   {
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()]   ) + "\n"




def 							a		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False   ):


				'''simple docstring'''
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   _find_text_in_file(
				    filename=os.path.join(snake_case__		,  snake_case__   )		,  start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''		,  end_prompt='''<!--End of the generated tip-->'''		,  )

				lowercase_	    =   get_model_list_for_task(snake_case__   )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(snake_case__		,  snake_case__   )		,  '''w'''		,  encoding='''utf-8'''		,  newline='''\n'''   ) as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
								else:
												raise ValueError(
												    F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
												    ''' to fix this.'''   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a         =   parser.parse_args()

						for task_guide in TASK_GUIDE_TO_MODELS.keys():
												check_model_list_for_task(task_guide, args.fix_and_overwrite)




