






"""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 							lowerCAmelCase			(   UpperCamelCase_:				str       )							->				str:







  '''simple docstring'''







  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      _a					      =						model_type_to_module_name(UpperCamelCase_       )

      _a					      =						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					      =						importlib.import_module("transformers"       )
  if hasattr(UpperCamelCase_  ,			UpperCamelCase_       ):
    return getattr(UpperCamelCase_  ,			UpperCamelCase_       )

  return None




class  lowercase_     :






      def __init__(     self						)						->int:







        '''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       lowerCamelCase__     (     cls  ,  a_  ,  **a_						)						->Any:







        '''simple docstring'''


        _a					      =						kwargs.pop("config"  ,  a_						)
        _a					      =						kwargs.pop("trust_remote_code"  ,  a_						)
        _a					      =						True

        _a					      =						None
        _a					      =						None

        # First, let's see if we have a preprocessor config.
        # Filter the kwargs for `get_file_from_repo`.
        _a					      =						{
            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
        _a					      =						get_file_from_repo(
            a_  ,  a_  ,  **a_						)
        if preprocessor_config_file is not None:
          _a      ,						_a					      =						ImageProcessingMixin.get_image_processor_dict(a_  ,  **a_						)
          _a					      =						config_dict.get("processor_class"  ,  a_						)
          if "AutoProcessor" in config_dict.get("auto_map"  ,  {}						):
            _a					      =						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					      =						FeatureExtractionMixin.get_feature_extractor_dict(a_  ,  **a_						)
          _a					      =						config_dict.get("processor_class"  ,  a_						)
          if "AutoProcessor" in config_dict.get("auto_map"  ,  {}						):
            _a					      =						config_dict["auto_map"]["AutoProcessor"]

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

            _a					      =						config_dict.get("processor_class"  ,  a_						)
            if "AutoProcessor" in config_dict.get("auto_map"  ,  {}						):
              _a					      =						config_dict["auto_map"]["AutoProcessor"]

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

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

        if processor_class is not None:
          _a					      =						processor_class_from_name(a_						)

        _a					      =						processor_auto_map is not None
        _a					      =						processor_class is not None or type(a_						) in PROCESSOR_MAPPING
        _a					      =						resolve_trust_remote_code(
            a_  ,  a_  ,  a_  ,  a_						)

        if has_remote_code and trust_remote_code:
          _a					      =						get_class_from_dynamic_module(
              a_  ,  a_  ,  **a_						)
          _a					      =						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       lowerCamelCase__     (     a_  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        PROCESSOR_MAPPING.register(a_  ,  a_						)










"""simple docstring"""




from __future__ import annotations






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				tuple[float, float]:







  '''simple docstring'''







  assert (
      c % greatest_common_divisor(UpperCamelCase_  ,			UpperCamelCase_       ) == 0
  )  # greatest_common_divisor(a,b) function implemented below
  ((_a)      ,						(_a)      ,						(_a))					      =						extended_gcd(UpperCamelCase_  ,			UpperCamelCase_       )  # extended_gcd(a,b) function implemented below
  _a					      =						c / d
  return (r * x, r * y)






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int = 2       )							->				None:







  '''simple docstring'''







  ((_a)      ,						(_a))					      =						diophantine(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )  # Initial value
  _a					      =						greatest_common_divisor(UpperCamelCase_  ,			UpperCamelCase_       )
  _a					      =						a // d
  _a					      =						b // d

  for i in range(UpperCamelCase_       ):
    _a					      =						xa + i * q
    _a					      =						ya - i * p
    print(UpperCamelCase_  ,			UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if a < b:
    _a      ,						_a					      =						b, a

  while a % b != 0:
    _a      ,						_a					      =						b, a % b

  return b






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				tuple[int, int, int]:







  '''simple docstring'''







  assert a >= 0 and b >= 0

  if b == 0:
    _a      ,						_a      ,						_a					      =						a, 1, 0
  else:
    ((_a)      ,						(_a)      ,						(_a))					      =						extended_gcd(UpperCamelCase_  ,			a % b       )
    _a					      =						q
    _a					      =						p - q * (a // b)

  assert a % d == 0 and b % d == 0
  assert d == a * x + b * y

  return (d, x, y)


if __name__ == "__main__":
     from doctest import testmod

     testmod(name="""diophantine""", verbose=True)
     testmod(name="""diophantine_all_soln""", verbose=True)
     testmod(name="""extended_gcd""", verbose=True)
     testmod(name="""greatest_common_divisor""", verbose=True)


