
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__a         =   get_tests_dir('fixtures')
__a         =   get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__a         =   get_tests_dir('fixtures/dummy-config.json')
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     int			)			->	Dict:
					lowercase_	    =   0
	def 		_lowercase    (			self    :     Optional[int]			)			->	List[Any]:
					lowercase_	    =   AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''			)
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	List[str]:
					lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			)			->	Tuple:
					with tempfile.TemporaryDirectory() as tmpdirname:
									lowercase_	    =   WavaVecaConfig()

									# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			).to_dict()

									config_dict.pop('''feature_extractor_type'''			)
									lowercase_	    =   WavaVecaFeatureExtractor(**SCREAMING_SNAKE_CASE_			)

									# save in new folder
									model_config.save_pretrained(SCREAMING_SNAKE_CASE_			)
									config.save_pretrained(SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)

									# make sure private variable is not incorrectly saved
									lowercase_	    =   json.loads(config.to_json_string()			)
									self.assertTrue('''_processor_class''' not in dict_as_saved			)

					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	List[str]:
					lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	Optional[int]:
					with self.assertRaisesRegex(
					    SCREAMING_SNAKE_CASE_			,				'''bert-base is not a local folder and is not a valid model identifier'''			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained('''bert-base'''			)
	def 		_lowercase    (			self    :     str			)			->	str:
					with self.assertRaisesRegex(
					    SCREAMING_SNAKE_CASE_			,				R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			,				revision='''aaaaaa'''			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Tuple:
					with self.assertRaisesRegex(
					    SCREAMING_SNAKE_CASE_			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''			)
	def 		_lowercase    (			self    :     str			)			->	Dict:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			)
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   AutoFeatureExtractor.from_pretrained(
					    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)

					# Test feature extractor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
									feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(reloaded_feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
	def 		_lowercase    (			self    :     Dict			)			->	Union[str, Any]:
					try:
									AutoConfig.register('''custom'''			,				SCREAMING_SNAKE_CASE_			)
									AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(SCREAMING_SNAKE_CASE_			):
													AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									# Now that the config is registered, it can be used as any other config with the auto-API
									lowercase_	    =   CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)
									with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_			)
													lowercase_	    =   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE_			)
													self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




	def 		_lowercase    (			self    :     str			)			->	List[str]:
					class 					lowercase__(							UpperCAmelCase				):



						"""simple docstring"""




						a     :Union[str, Any]       	=      True

					try:
									AutoConfig.register('''custom'''			,				SCREAMING_SNAKE_CASE_			)
									AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									# If remote code is not set, the default is to use local
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			)
									self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
									self.assertTrue(feature_extractor.is_local			)

									# If remote code is disabled, we load the local one.
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
									self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
									self.assertTrue(feature_extractor.is_local			)

									# If remote is enabled, we load from the Hub
									lowercase_	    =   AutoFeatureExtractor.from_pretrained(
									    '''hf-internal-testing/test_dynamic_feature_extractor'''			,				trust_remote_code=SCREAMING_SNAKE_CASE_			)
									self.assertEqual(feature_extractor.__class__.__name__			,				'''NewFeatureExtractor'''			)
									self.assertTrue(not hasattr(SCREAMING_SNAKE_CASE_			,				'''is_local'''			)			)

					finally:
									if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






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)




