

"""simple docstring"""






import importlib
import inspect
import json
import os
from collections import OrderedDict

# 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 ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import 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,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


lowerCamelCase__				:  List[str]													=  logging.get_logger(__name__)

lowerCamelCase__				:  Optional[int]													=  OrderedDict(
    [
        ('''align''', '''AlignProcessor'''),
        ('''altclip''', '''AltCLIPProcessor'''),
        ('''bark''', '''BarkProcessor'''),
        ('''blip''', '''BlipProcessor'''),
        ('''blip-2''', '''Blip2Processor'''),
        ('''bridgetower''', '''BridgeTowerProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPProcessor'''),
        ('''clap''', '''ClapProcessor'''),
        ('''clip''', '''CLIPProcessor'''),
        ('''clipseg''', '''CLIPSegProcessor'''),
        ('''flava''', '''FlavaProcessor'''),
        ('''git''', '''GitProcessor'''),
        ('''groupvit''', '''CLIPProcessor'''),
        ('''hubert''', '''Wav2Vec2Processor'''),
        ('''instructblip''', '''InstructBlipProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2Processor'''),
        ('''layoutlmv3''', '''LayoutLMv3Processor'''),
        ('''markuplm''', '''MarkupLMProcessor'''),
        ('''mctct''', '''MCTCTProcessor'''),
        ('''mgp-str''', '''MgpstrProcessor'''),
        ('''oneformer''', '''OneFormerProcessor'''),
        ('''owlvit''', '''OwlViTProcessor'''),
        ('''pix2struct''', '''Pix2StructProcessor'''),
        ('''sam''', '''SamProcessor'''),
        ('''sew''', '''Wav2Vec2Processor'''),
        ('''sew-d''', '''Wav2Vec2Processor'''),
        ('''speech_to_text''', '''Speech2TextProcessor'''),
        ('''speech_to_text_2''', '''Speech2Text2Processor'''),
        ('''speecht5''', '''SpeechT5Processor'''),
        ('''trocr''', '''TrOCRProcessor'''),
        ('''tvlt''', '''TvltProcessor'''),
        ('''unispeech''', '''Wav2Vec2Processor'''),
        ('''unispeech-sat''', '''Wav2Vec2Processor'''),
        ('''vilt''', '''ViltProcessor'''),
        ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
        ('''wav2vec2''', '''Wav2Vec2Processor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
        ('''wavlm''', '''Wav2Vec2Processor'''),
        ('''whisper''', '''WhisperProcessor'''),
        ('''xclip''', '''XCLIPProcessor'''),
    ]
)

lowerCamelCase__				:  Optional[int]													=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def       UpperCamelCase					(			_lowerCAmelCase     :						str )				->			int:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_UpperCAmelCase    :							Union[str, Any]									=					model_type_to_module_name(_lowerCAmelCase )

			_UpperCAmelCase    :							Tuple									=					importlib.import_module(f'''.{module_name}''',  """transformers.models""" )
			try:
				return getattr(_lowerCAmelCase,  _lowerCAmelCase )
			except AttributeError:
				continue

	for processor in PROCESSOR_MAPPING._extra_content.values():
		if getattr(_lowerCAmelCase,  """__name__""",  _lowerCAmelCase ) == class_name:
			return processor

    # 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.
	_UpperCAmelCase    :							Optional[int]									=					importlib.import_module("""transformers""" )
	if hasattr(_lowerCAmelCase,  _lowerCAmelCase ):
		return getattr(_lowerCAmelCase,  _lowerCAmelCase )

	return None






class     _UpperCAmelCase			:
			def __init__(		self     )    ->    List[Any]:






				'''simple docstring'''







				raise EnvironmentError(
				    """AutoProcessor is designed to be instantiated """
				    """using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."""     )
			@classmethod
			@replace_list_option_in_docstrings(_A     )
			def 							__snake_case (		cls      ,		_A      ,		**_A     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					kwargs.pop("""config"""      ,		_A     )
				_UpperCAmelCase    :							List[Any]									=					kwargs.pop("""trust_remote_code"""      ,		_A     )
				_UpperCAmelCase    :							Dict									=					True

				_UpperCAmelCase    :							List[Any]									=					None
				_UpperCAmelCase    :							Union[str, Any]									=					None

				# First, let's see if we have a preprocessor config.
				# Filter the kwargs for `get_file_from_repo`.
				_UpperCAmelCase    :							int									=					{
				    key: kwargs[key] for key in inspect.signature(_A     ).parameters.keys() if key in kwargs
				}
				# Let's start by checking whether the processor class is saved in an image processor
				_UpperCAmelCase    :							List[Any]									=					get_file_from_repo(
				    _A      ,		_A      ,		**_A     )
				if preprocessor_config_file is not None:
					_UpperCAmelCase						,   _UpperCAmelCase    :							Dict									=					ImageProcessingMixin.get_image_processor_dict(_A      ,		**_A     )
					_UpperCAmelCase    :							Any									=					config_dict.get("""processor_class"""      ,		_A     )
					if "AutoProcessor" in config_dict.get("""auto_map"""      ,		{}     ):
						_UpperCAmelCase    :							Tuple									=					config_dict["""auto_map"""]["""AutoProcessor"""]

        # If not found, let's check whether the processor class is saved in a feature extractor config
				if preprocessor_config_file is not None and processor_class is None:
					_UpperCAmelCase						,   _UpperCAmelCase    :							str									=					FeatureExtractionMixin.get_feature_extractor_dict(_A      ,		**_A     )
					_UpperCAmelCase    :							List[Any]									=					config_dict.get("""processor_class"""      ,		_A     )
					if "AutoProcessor" in config_dict.get("""auto_map"""      ,		{}     ):
						_UpperCAmelCase    :							Dict									=					config_dict["""auto_map"""]["""AutoProcessor"""]

				if processor_class is None:
					# Next, let's check whether the processor class is saved in a tokenizer
					_UpperCAmelCase    :							Tuple									=					get_file_from_repo(
					    _A      ,		_A      ,		**_A     )
					if tokenizer_config_file is not None:
						with open(_A      ,		encoding="""utf-8"""     ) as reader:
							_UpperCAmelCase    :							Optional[int]									=					json.load(_A     )

						_UpperCAmelCase    :							Any									=					config_dict.get("""processor_class"""      ,		_A     )
						if "AutoProcessor" in config_dict.get("""auto_map"""      ,		{}     ):
							_UpperCAmelCase    :							Dict									=					config_dict["""auto_map"""]["""AutoProcessor"""]

				if processor_class is None:
					# Otherwise, load config, if it can be loaded.
					if not isinstance(_A      ,		_A     ):
						_UpperCAmelCase    :							Optional[Any]									=					AutoConfig.from_pretrained(
						    _A      ,		trust_remote_code=_A      ,		**_A     )

					# And check if the config contains the processor class.
					_UpperCAmelCase    :							Any									=					getattr(_A      ,		"""processor_class"""      ,		_A     )
					if hasattr(_A      ,		"""auto_map"""     ) and "AutoProcessor" in config.auto_map:
						_UpperCAmelCase    :							Optional[Any]									=					config.auto_map["""AutoProcessor"""]

				if processor_class is not None:
					_UpperCAmelCase    :							str									=					processor_class_from_name(_A     )

				_UpperCAmelCase    :							Dict									=					processor_auto_map is not None
				_UpperCAmelCase    :							Optional[Any]									=					processor_class is not None or type(_A     ) in PROCESSOR_MAPPING
				_UpperCAmelCase    :							Tuple									=					resolve_trust_remote_code(
				    _A      ,		_A      ,		_A      ,		_A     )

				if has_remote_code and trust_remote_code:
					_UpperCAmelCase    :							Any									=					get_class_from_dynamic_module(
					    _A      ,		_A      ,		**_A     )
					_UpperCAmelCase    :							Any									=					kwargs.pop("""code_revision"""      ,		_A     )
					if os.path.isdir(_A     ):
						processor_class.register_for_auto_class()
					return processor_class.from_pretrained(
					    _A      ,		trust_remote_code=_A      ,		**_A     )
				elif processor_class is not None:
					return processor_class.from_pretrained(
					    _A      ,		trust_remote_code=_A      ,		**_A     )
				# Last try: we use the PROCESSOR_MAPPING.
				elif type(_A     ) in PROCESSOR_MAPPING:
					return PROCESSOR_MAPPING[type(_A     )].from_pretrained(_A      ,		**_A     )

				# At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
				# tokenizer.
				try:
					return AutoTokenizer.from_pretrained(
					    _A      ,		trust_remote_code=_A      ,		**_A     )
				except Exception:
					try:
						return AutoImageProcessor.from_pretrained(
						    _A      ,		trust_remote_code=_A      ,		**_A     )
					except Exception:
						pass

					try:
						return AutoFeatureExtractor.from_pretrained(
						    _A      ,		trust_remote_code=_A      ,		**_A     )
					except Exception:
						pass

				raise ValueError(
				    f'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
				    """tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"""
				    """the files of at least one of those processing classes."""     )
			@staticmethod
			def 							__snake_case (		_A      ,		_A     )    ->    Dict:






				'''simple docstring'''







				PROCESSOR_MAPPING.register(_A      ,		_A     )








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int = 1000 )				->			str:
	if (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (digit_position <= 0):
		raise ValueError("""Digit position must be a positive integer""" )
	elif (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (precision < 0):
		raise ValueError("""Precision must be a nonnegative integer""" )

	# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
	# accurate
	_UpperCAmelCase    :							List[Any]									=					(
	    4 * _subsum(_lowerCAmelCase,  1,  _lowerCAmelCase )
	    - 2 * _subsum(_lowerCAmelCase,  4,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  5,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  6,  _lowerCAmelCase )
	)

	# return the first hex digit of the fractional part of the result
	return hex(int((sum_result % 1) * 16 ) )[2:]
def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						int )				->			float:
	_UpperCAmelCase    :							Optional[int]									=					0.0
	for sum_index in range(digit_pos_to_extract + precision ):
		_UpperCAmelCase    :							Optional[Any]									=					8 * sum_index + denominator_addend
		if sum_index < digit_pos_to_extract:
			# if the exponential term is an integer and we mod it by the denominator
			# before dividing, only the integer part of the sum will change;
			# the fractional part will not
			_UpperCAmelCase    :							Optional[Any]									=					pow(
			    16,  digit_pos_to_extract - 1 - sum_index,  _lowerCAmelCase )
		else:
			_UpperCAmelCase    :							int									=					pow(16,  digit_pos_to_extract - 1 - sum_index )
		total += exponential_term / denominator
	return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()





