

'''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


UpperCAmelCase							=					logging.get_logger(__name__)

UpperCAmelCase							=					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'''),
    ]
)

UpperCAmelCase							=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def 			__UpperCamelCase							(		lowercase__  :					List[Any]	):






     '''simple docstring'''





     for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
          if class_name in processors:
               __lowercase     						=model_type_to_module_name(A__	)

               __lowercase     						=importlib.import_module(F'''.{module_name}''',   'transformers.models'	)
               try:
                    return getattr(A__,   A__	)
               except AttributeError:
                    continue

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

     return None







class 					lowerCAmelCase       :







       def __init__(							self     :	Union[str, 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(__UpperCamelCase )
       def      snake_case				(							cls     :	Any  , __lowercase     :	Tuple  , **__lowercase     :	Dict ):







            """simple docstring"""







            __lowercase     						=kwargs.pop('config'  , __UpperCamelCase )
            __lowercase     						=kwargs.pop('trust_remote_code'  , __UpperCamelCase )
            __lowercase     						=True

            __lowercase     						=None
            __lowercase     						=None

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

            if processor_class is None:
                 # Next, let's check whether the processor class is saved in a tokenizer
                 __lowercase     						=get_file_from_repo(
                     __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase )
                 if tokenizer_config_file is not None:
                      with open(__UpperCamelCase  , encoding='utf-8' ) as reader:
                           __lowercase     						=json.load(__UpperCamelCase )

                      __lowercase     						=config_dict.get('processor_class'  , __UpperCamelCase )
                      if "AutoProcessor" in config_dict.get('auto_map'  , {} ):
                           __lowercase     						=config_dict['auto_map']['AutoProcessor']

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

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

            if processor_class is not None:
                 __lowercase     						=processor_class_from_name(__UpperCamelCase )

            __lowercase     						=processor_auto_map is not None
            __lowercase     						=processor_class is not None or type(__UpperCamelCase ) in PROCESSOR_MAPPING
            __lowercase     						=resolve_trust_remote_code(
                __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase )

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

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

                 try:
                      return AutoFeatureExtractor.from_pretrained(
                          __UpperCamelCase  , trust_remote_code=__UpperCamelCase  , **__UpperCamelCase )
                 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				(							__lowercase     :	Union[str, Any]  , __lowercase     :	Union[str, Any] ):







            """simple docstring"""







            PROCESSOR_MAPPING.register(__UpperCamelCase  , __UpperCamelCase )




'''simple docstring'''

from __future__ import annotations

def 			__UpperCamelCase							(		lowercase__  :					list[int]	):






     '''simple docstring'''





     if not list_of_ints:
          return []

     if min(lowercase__	) < 0:
          raise ValueError('All numbers must be positive'	)

     __lowercase     						=max(len(bin(lowercase__	)[2:]	) for x in list_of_ints	)
     return _msd_radix_sort(lowercase__,   lowercase__	)

def 			__UpperCamelCase							(		lowercase__  :					list[int],   lowercase__  :					int	):






     '''simple docstring'''





     if bit_position == 0 or len(lowercase__	) in [0, 1]:
          return list_of_ints

     __lowercase     						=[]
     __lowercase     						=[]
     # Split numbers based on bit at bit_position from the right
     for number in list_of_ints:
          if (number >> (bit_position - 1)) & 1:
               # number has a one at bit bit_position
               ones.append(lowercase__	)
          else:
               # number has a zero at bit bit_position
               zeros.append(lowercase__	)

    # recursively split both lists further
     __lowercase     						=_msd_radix_sort(lowercase__,   bit_position - 1	)
     __lowercase     						=_msd_radix_sort(lowercase__,   bit_position - 1	)

     # recombine lists
     __lowercase     						=zeros
     res.extend(lowercase__	)

     return res

def 			__UpperCamelCase							(		lowercase__  :					list[int]	):






     '''simple docstring'''





     __lowercase     						=len(lowercase__	)
     if not list_of_ints or length == 1:
          return

     if min(lowercase__	) < 0:
          raise ValueError('All numbers must be positive'	)

     __lowercase     						=max(len(bin(lowercase__	)[2:]	) for x in list_of_ints	)
     _msd_radix_sort_inplace(lowercase__,   lowercase__,   0,   lowercase__	)

def 			__UpperCamelCase							(		lowercase__  :					list[int],   lowercase__  :					int,   lowercase__  :					int,   lowercase__  :					int	):






     '''simple docstring'''





     if bit_position == 0 or end_index - begin_index <= 1:
          return

     bit_position -= 1

     __lowercase     						=begin_index
     __lowercase     						=end_index - 1
     while i <= j:
          __lowercase     						=False
          if not (list_of_ints[i] >> bit_position) & 1:
               # found zero at the beginning
               i += 1
               __lowercase     						=True
          if (list_of_ints[j] >> bit_position) & 1:
               # found one at the end
               j -= 1
               __lowercase     						=True

          if changed:
               continue

          __lowercase						,       __lowercase     						=list_of_ints[j], list_of_ints[i]
          j -= 1
          if j != i:
               i += 1

     _msd_radix_sort_inplace(lowercase__,   lowercase__,   lowercase__,   lowercase__	)
     _msd_radix_sort_inplace(lowercase__,   lowercase__,   lowercase__,   lowercase__	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


