





"""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_	:		str         =   logging.get_logger(__name__)

lowerCamelCase_	:		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_	:		Any         =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      A_    :			int									=    model_type_to_module_name(_UpperCAmelCase   )

      A_    :			Optional[int]									=    importlib.import_module(f""".{module_name}"""					,   'transformers.models'   )
      try:
        return getattr(_UpperCAmelCase					,   _UpperCAmelCase   )
      except AttributeError:
        continue

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(_UpperCAmelCase					,   '__name__'					,   _UpperCAmelCase   ) == 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.
  A_    :			Any									=    importlib.import_module('transformers'   )
  if hasattr(_UpperCAmelCase					,   _UpperCAmelCase   ):
    return getattr(_UpperCAmelCase					,   _UpperCAmelCase   )

  return None





class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self    ):

        """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(snake_case_    )
      def    lowerCamelCase_     (	cls   , snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    kwargs.pop('config'   , snake_case_    )
        A_    :			Tuple									=    kwargs.pop('trust_remote_code'   , snake_case_    )
        A_    :			Optional[Any]									=    True

        A_    :			Optional[Any]									=    None
        A_    :			Optional[int]									=    None

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

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

            A_    :			str									=    config_dict.get('processor_class'   , snake_case_    )
            if "AutoProcessor" in config_dict.get('auto_map'   , {}    ):
              A_    :			Optional[int]									=    config_dict['auto_map']['AutoProcessor']

        if processor_class is None:
          # Otherwise, load config, if it can be loaded.
          if not isinstance(snake_case_   , snake_case_    ):
            A_    :			Any									=    AutoConfig.from_pretrained(
                snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )

          # And check if the config contains the processor class.
          A_    :			Dict									=    getattr(snake_case_   , 'processor_class'   , snake_case_    )
          if hasattr(snake_case_   , 'auto_map'    ) and "AutoProcessor" in config.auto_map:
            A_    :			Dict									=    config.auto_map['AutoProcessor']

        if processor_class is not None:
          A_    :			List[Any]									=    processor_class_from_name(snake_case_    )

        A_    :			Union[str, Any]									=    processor_auto_map is not None
        A_    :			int									=    processor_class is not None or type(snake_case_    ) in PROCESSOR_MAPPING
        A_    :			Any									=    resolve_trust_remote_code(
            snake_case_   , snake_case_   , snake_case_   , snake_case_    )

        if has_remote_code and trust_remote_code:
          A_    :			Tuple									=    get_class_from_dynamic_module(
              snake_case_   , snake_case_   , **snake_case_    )
          A_    :			List[str]									=    kwargs.pop('code_revision'   , snake_case_    )
          if os.path.isdir(snake_case_    ):
            processor_class.register_for_auto_class()
          return processor_class.from_pretrained(
              snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
        elif processor_class is not None:
          return processor_class.from_pretrained(
              snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
        # Last try: we use the PROCESSOR_MAPPING.
        elif type(snake_case_    ) in PROCESSOR_MAPPING:
          return PROCESSOR_MAPPING[type(snake_case_    )].from_pretrained(snake_case_   , **snake_case_    )

        # 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(
              snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
        except Exception:
          try:
            return AutoImageProcessor.from_pretrained(
                snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
          except Exception:
            pass

          try:
            return AutoFeatureExtractor.from_pretrained(
                snake_case_   , trust_remote_code=snake_case_   , **snake_case_    )
          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    lowerCamelCase_     (	snake_case_   , snake_case_    ):

        """simple docstring"""

        PROCESSOR_MAPPING.register(snake_case_   , snake_case_    )






'''simple docstring'''






import timeit


class 	_lowercase    :


   def __init__(			self     , _UpperCAmelCase       ):
       if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ):  # Initializes a matrix identical to the one provided.
           A							:							List[Any]				=    arg
           A							:							Dict				=    len(_UpperCAmelCase       )
       else:  # Initializes a square matrix of the given size and set values to zero.
           A							:							str				=    arg
           A							:							Union[str, Any]				=    [[0 for _ in range(self.n       )] for _ in range(self.n       )]


   def __mul__(			self     , _UpperCAmelCase       ):
       A							:							List[str]				=    Matrix(self.n       )
       for i in range(self.n       ):
           for j in range(self.n       ):
               for k in range(self.n       ):
                   matrix.t[i][j] += self.t[i][k] * b.t[k][j]
       return matrix







def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			int		)		->	Union[str, Any]:
    A							:							Tuple				=    Matrix([[1, 0], [0, 1]]		)
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 				_lowerCamelCase(	UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			Any		)		->	Any:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    A							:							Optional[Any]				=    Matrix([[1, 1], [1, 0]]		)
    A							:							int				=    modular_exponentiation(UpperCamelCase__     ,							n - 2		)
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			Any		)		->	List[Any]:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    A							:							str				=    fa
    A							:							List[Any]				=    fa
    n -= 2

    while n > 0:
        A,						A							:							List[str]				=    fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 				_lowerCamelCase(	)		->	Union[str, Any]:
    A							:							Any				=    '''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
    A							:							Optional[int]				=    '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
    A							:							List[Any]				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print('''With matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							str				=    '''
from random import randint
from __main__ import simple_fibonacci
'''
    A							:							Any				=    '''simple_fibonacci(randint(1,70000), 1, 1)'''
    A							:							str				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print(
        '''Without matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	Optional[Any]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()
