
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_lowerCamelCase			:							Dict         =     logging.get_logger(__name__)

_lowerCamelCase			:							List[str]         =     OrderedDict(
    [
        ('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
        ('''beit''', '''BeitFeatureExtractor'''),
        ('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
        ('''clap''', '''ClapFeatureExtractor'''),
        ('''clip''', '''CLIPFeatureExtractor'''),
        ('''clipseg''', '''ViTFeatureExtractor'''),
        ('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
        ('''convnext''', '''ConvNextFeatureExtractor'''),
        ('''cvt''', '''ConvNextFeatureExtractor'''),
        ('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
        ('''data2vec-vision''', '''BeitFeatureExtractor'''),
        ('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
        ('''deit''', '''DeiTFeatureExtractor'''),
        ('''detr''', '''DetrFeatureExtractor'''),
        ('''dinat''', '''ViTFeatureExtractor'''),
        ('''donut-swin''', '''DonutFeatureExtractor'''),
        ('''dpt''', '''DPTFeatureExtractor'''),
        ('''encodec''', '''EncodecFeatureExtractor'''),
        ('''flava''', '''FlavaFeatureExtractor'''),
        ('''glpn''', '''GLPNFeatureExtractor'''),
        ('''groupvit''', '''CLIPFeatureExtractor'''),
        ('''hubert''', '''Wav2Vec2FeatureExtractor'''),
        ('''imagegpt''', '''ImageGPTFeatureExtractor'''),
        ('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
        ('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
        ('''levit''', '''LevitFeatureExtractor'''),
        ('''maskformer''', '''MaskFormerFeatureExtractor'''),
        ('''mctct''', '''MCTCTFeatureExtractor'''),
        ('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
        ('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
        ('''mobilevit''', '''MobileViTFeatureExtractor'''),
        ('''nat''', '''ViTFeatureExtractor'''),
        ('''owlvit''', '''OwlViTFeatureExtractor'''),
        ('''perceiver''', '''PerceiverFeatureExtractor'''),
        ('''poolformer''', '''PoolFormerFeatureExtractor'''),
        ('''regnet''', '''ConvNextFeatureExtractor'''),
        ('''resnet''', '''ConvNextFeatureExtractor'''),
        ('''segformer''', '''SegformerFeatureExtractor'''),
        ('''sew''', '''Wav2Vec2FeatureExtractor'''),
        ('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
        ('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
        ('''speecht5''', '''SpeechT5FeatureExtractor'''),
        ('''swiftformer''', '''ViTFeatureExtractor'''),
        ('''swin''', '''ViTFeatureExtractor'''),
        ('''swinv2''', '''ViTFeatureExtractor'''),
        ('''table-transformer''', '''DetrFeatureExtractor'''),
        ('''timesformer''', '''VideoMAEFeatureExtractor'''),
        ('''tvlt''', '''TvltFeatureExtractor'''),
        ('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
        ('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
        ('''van''', '''ConvNextFeatureExtractor'''),
        ('''videomae''', '''VideoMAEFeatureExtractor'''),
        ('''vilt''', '''ViltFeatureExtractor'''),
        ('''vit''', '''ViTFeatureExtractor'''),
        ('''vit_mae''', '''ViTFeatureExtractor'''),
        ('''vit_msn''', '''ViTFeatureExtractor'''),
        ('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
        ('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
        ('''whisper''', '''WhisperFeatureExtractor'''),
        ('''xclip''', '''CLIPFeatureExtractor'''),
        ('''yolos''', '''YolosFeatureExtractor'''),
    ]
)

_lowerCamelCase			:							List[Any]         =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def 			a_						(			__lowercase  :      List[str]					)					->    Optional[Any]:
			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									_snake_case            =     model_type_to_module_name(lowerCAmelCase__					)

									_snake_case            =     importlib.import_module(f'''.{module_name}'''			,   'transformers.models'					)
									try:
												return getattr(lowerCAmelCase__			,   lowerCAmelCase__					)
									except AttributeError:
												continue

			for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
						if getattr(lowerCAmelCase__			,   '__name__'			,   lowerCAmelCase__					) == class_name:
									return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
			_snake_case            =     importlib.import_module('transformers'					)
			if hasattr(lowerCAmelCase__			,   lowerCAmelCase__					):
						return getattr(lowerCAmelCase__			,   lowerCAmelCase__					)

			return None






def 			a_						(			__lowercase  :      List[str]			,   __lowercase  :      int = None			,   __lowercase  :      str = False			,   __lowercase  :      str = False			,   __lowercase  :      Tuple = None			,   __lowercase  :      Tuple = None			,   __lowercase  :      Union[str, Any] = None			,   __lowercase  :      int = False			,   **__lowercase  :      str			,   )					->    int:
			_snake_case            =     get_file_from_repo(
			    lowerCAmelCase__			,   lowerCAmelCase__			,   cache_dir=lowerCAmelCase__			,   force_download=lowerCAmelCase__			,   resume_download=lowerCAmelCase__			,   proxies=lowerCAmelCase__			,   use_auth_token=lowerCAmelCase__			,   revision=lowerCAmelCase__			,   local_files_only=lowerCAmelCase__			,   )
			if resolved_config_file is None:
						logger.info(
						    'Could not locate the feature extractor configuration file, will try to use the model config instead.'					)
						return {}

			with open(lowerCAmelCase__			,   encoding='utf-8'					) as reader:
						return json.load(lowerCAmelCase__					)




class   SCREAMING_SNAKE_CASE__	:





	'''simple docstring'''



	def __init__(    self      :       str      ):


				'''simple docstring'''


				raise EnvironmentError(
				    'AutoFeatureExtractor is designed to be instantiated '
				    'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'      )



	@classmethod
	@replace_list_option_in_docstrings(lowercase      )
	def 					A				(    cls      :       Optional[int]     ,    lowercase      :       Optional[int]     ,    **lowercase      :       List[Any]      ):


				'''simple docstring'''


				_snake_case            =     kwargs.pop('config'     ,    lowercase      )
				_snake_case            =     kwargs.pop('trust_remote_code'     ,    lowercase      )
				_snake_case            =     True

				_snake_case            =     FeatureExtractionMixin.get_feature_extractor_dict(lowercase     ,    **lowercase      )
				_snake_case            =     config_dict.get('feature_extractor_type'     ,    lowercase      )
				_snake_case            =     None
				if "AutoFeatureExtractor" in config_dict.get('auto_map'     ,    {}      ):
							_snake_case            =     config_dict['auto_map']['AutoFeatureExtractor']

				# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
				if feature_extractor_class is None and feature_extractor_auto_map is None:
							if not isinstance(lowercase     ,    lowercase      ):
										_snake_case            =     AutoConfig.from_pretrained(lowercase     ,    **lowercase      )
							# It could be in `config.feature_extractor_type``
							_snake_case            =     getattr(lowercase     ,    'feature_extractor_type'     ,    lowercase      )
							if hasattr(lowercase     ,    'auto_map'      ) and "AutoFeatureExtractor" in config.auto_map:
										_snake_case            =     config.auto_map['AutoFeatureExtractor']

				if feature_extractor_class is not None:
							_snake_case            =     feature_extractor_class_from_name(lowercase      )

				_snake_case            =     feature_extractor_auto_map is not None
				_snake_case            =     feature_extractor_class is not None or type(lowercase      ) in FEATURE_EXTRACTOR_MAPPING
				_snake_case            =     resolve_trust_remote_code(
				    lowercase     ,    lowercase     ,    lowercase     ,    lowercase      )

				if has_remote_code and trust_remote_code:
							_snake_case            =     get_class_from_dynamic_module(
							    lowercase     ,    lowercase     ,    **lowercase      )
							_snake_case            =     kwargs.pop('code_revision'     ,    lowercase      )
							if os.path.isdir(lowercase      ):
										feature_extractor_class.register_for_auto_class()
							return feature_extractor_class.from_dict(lowercase     ,    **lowercase      )
				elif feature_extractor_class is not None:
							return feature_extractor_class.from_dict(lowercase     ,    **lowercase      )
				# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
				elif type(lowercase      ) in FEATURE_EXTRACTOR_MAPPING:
							_snake_case            =     FEATURE_EXTRACTOR_MAPPING[type(lowercase      )]
							return feature_extractor_class.from_dict(lowercase     ,    **lowercase      )

				raise ValueError(
				    f'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
				    f'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
				    f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()      )}'''      )





	@staticmethod
	def 					A				(    lowercase      :       str     ,    lowercase      :       List[Any]      ):


				'''simple docstring'''


				FEATURE_EXTRACTOR_MAPPING.register(lowercase     ,    lowercase      )






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

		with open(lowerCAmelCase__							,'wb'						) as f:
				f.write(json.dumps(lowerCAmelCase__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()
