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
_SCREAMING_SNAKE_CASE      						=	"src/transformers"
_SCREAMING_SNAKE_CASE      						=	"docs/source/en/tasks"





def        lowercase(     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )						->     int:







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

 UpperCamelCase          =	start_index
 while not lines[end_index].startswith(_lowerCAmelCase    ):
  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.
_SCREAMING_SNAKE_CASE      						=	direct_transformers_import(TRANSFORMERS_PATH)

_SCREAMING_SNAKE_CASE      						=	{
    "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`).
_SCREAMING_SNAKE_CASE      						=	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def        lowercase(     UpperCamelCase_    )						->     Dict:







 '''simple docstring'''
 UpperCamelCase          =	TASK_GUIDE_TO_MODELS[task_guide]
 UpperCamelCase          =	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCAmelCase				, set()    )
 UpperCamelCase          =	{
     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        lowercase(     UpperCamelCase_				, UpperCamelCase_=False    )						->     Dict:







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

 UpperCamelCase          =	get_model_list_for_task(_lowerCAmelCase    )

 if current_list != new_list:
  if overwrite:
   with open(os.path.join(_lowerCAmelCase				, _lowerCAmelCase    )				, """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__":
     _SCREAMING_SNAKE_CASE      						=	argparse.ArgumentParser()
     parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
     _SCREAMING_SNAKE_CASE      						=	parser.parse_args()

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





'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
						import jax.numpy as jnp

if is_tf_available():
						import tensorflow as tf

if is_torch_available():
						import torch

class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Any		)			->		str:
								UpperCAmelCase :       Any 							=		{
								    '''task_specific_params''': {
								        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
								        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
								        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
								    }
								}
								UpperCAmelCase :       int 							=		{
								    '''task_specific_params.summarization.length_penalty''': 1.0,
								    '''task_specific_params.summarization.max_length''': 128,
								    '''task_specific_params.summarization.min_length''': 12,
								    '''task_specific_params.summarization.num_beams''': 4,
								    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
								    '''task_specific_params.summarization_cnn.max_length''': 142,
								    '''task_specific_params.summarization_cnn.min_length''': 56,
								    '''task_specific_params.summarization_cnn.num_beams''': 4,
								    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
								    '''task_specific_params.summarization_xsum.max_length''': 62,
								    '''task_specific_params.summarization_xsum.min_length''': 11,
								    '''task_specific_params.summarization_xsum.num_beams''': 6,
								}

								self.assertEqual(flatten_dict(__snake_case		)		,			__snake_case		)







	def      A	(						self			:     int		)			->		str:
								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			x.transpose()		)		)

								UpperCAmelCase :       str 							=		np.random.randn(3		,			4		,			5		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			x.transpose((1, 2, 0)		)		)		)







	@require_torch
	def      A	(						self			:     str		)			->		Union[str, Any]:
								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			transpose(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Any 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			transpose(__snake_case		,			axes=(1, 2, 0)		).numpy()		)		)







	@require_tf
	def      A	(						self			:     List[str]		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			transpose(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       str 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			transpose(__snake_case		,			axes=(1, 2, 0)		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Tuple		)			->		Any:
								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[str] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		)		,			np.asarray(transpose(__snake_case		)		)		)		)

								UpperCAmelCase :       Dict 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(transpose(__snake_case		,			axes=(1, 2, 0)		)		,			np.asarray(transpose(__snake_case		,			axes=(1, 2, 0)		)		)		)		)







	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			np.reshape(__snake_case		,			(4, 3)		)		)		)

								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		,			5		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			np.reshape(__snake_case		,			(12, 5)		)		)		)







	@require_torch
	def      A	(						self			:     Union[str, Any]		)			->		int:
								UpperCAmelCase :       Dict 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Optional[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			reshape(__snake_case		,			(4, 3)		).numpy()		)		)

								UpperCAmelCase :       List[str] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       List[Any] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			reshape(__snake_case		,			(12, 5)		).numpy()		)		)







	@require_tf
	def      A	(						self			:     int		)			->		List[str]:
								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       List[str] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			reshape(__snake_case		,			(4, 3)		).numpy()		)		)

								UpperCAmelCase :       List[Any] 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Optional[Any] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			reshape(__snake_case		,			(12, 5)		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Any		)			->		Dict:
								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Union[str, Any] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(4, 3)		)		,			np.asarray(reshape(__snake_case		,			(4, 3)		)		)		)		)

								UpperCAmelCase :       Any 							=		np.random.randn(3		,			4		,			5		)
								UpperCAmelCase :       Optional[Any] 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(reshape(__snake_case		,			(12, 5)		)		,			np.asarray(reshape(__snake_case		,			(12, 5)		)		)		)		)







	def      A	(						self			:     List[Any]		)			->		List[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(1		,			3		,			4		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			np.squeeze(__snake_case		)		)		)

								UpperCAmelCase :       str 							=		np.random.randn(1		,			4		,			1		,			5		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			np.squeeze(__snake_case		,			axis=2		)		)		)







	@require_torch
	def      A	(						self			:     Optional[int]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       List[str] 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			squeeze(__snake_case		).numpy()		)		)

								UpperCAmelCase :       Any 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       str 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			squeeze(__snake_case		,			axis=2		).numpy()		)		)







	@require_tf
	def      A	(						self			:     Optional[Any]		)			->		Dict:
								UpperCAmelCase :       int 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			squeeze(__snake_case		).numpy()		)		)

								UpperCAmelCase :       List[str] 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       Optional[int] 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			squeeze(__snake_case		,			axis=2		).numpy()		)		)







	@require_flax
	def      A	(						self			:     List[Any]		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		np.random.randn(1		,			3		,			4		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		)		,			np.asarray(squeeze(__snake_case		)		)		)		)

								UpperCAmelCase :       str 							=		np.random.randn(1		,			4		,			1		,			5		)
								UpperCAmelCase :       int 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(squeeze(__snake_case		,			axis=2		)		,			np.asarray(squeeze(__snake_case		,			axis=2		)		)		)		)







	def      A	(						self			:     Optional[Any]		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		np.random.randn(3		,			4		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			np.expand_dims(__snake_case		,			axis=1		)		)		)







	@require_torch
	def      A	(						self			:     List[str]		)			->		Tuple:
								UpperCAmelCase :       Tuple 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Tuple 							=		torch.tensor(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			expand_dims(__snake_case		,			axis=1		).numpy()		)		)







	@require_tf
	def      A	(						self			:     List[str]		)			->		Union[str, Any]:
								UpperCAmelCase :       Union[str, Any] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       Any 							=		tf.constant(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			expand_dims(__snake_case		,			axis=1		).numpy()		)		)







	@require_flax
	def      A	(						self			:     Any		)			->		List[Any]:
								UpperCAmelCase :       List[str] 							=		np.random.randn(3		,			4		)
								UpperCAmelCase :       str 							=		jnp.array(__snake_case		)
								self.assertTrue(np.allclose(expand_dims(__snake_case		,			axis=1		)		,			np.asarray(expand_dims(__snake_case		,			axis=1		)		)		)		)





