

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


_a									=  logging.get_logger(__name__)

_a									=  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'''),
    ]
)

_a									=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       Optional[Any]:

 """simple docstring"""






 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   __lowerCAmelCase:  Optional[int]        = model_type_to_module_name(SCREAMING_SNAKE_CASE						)

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

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

 return None






class 				A_					:


     def __init__(				self     :						Optional[Any]   )				->      str:
      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(_SCREAMING_SNAKE_CASE   )
     def     UpperCAmelCase    (				cls     :						List[Any]     ,    UpperCAmelCase     :						List[str]     ,    **UpperCAmelCase     :						str   )				->      Optional[Any]:
      __lowerCAmelCase:  List[str]        = kwargs.pop('config'     ,    _SCREAMING_SNAKE_CASE   )
      __lowerCAmelCase:  Optional[int]        = kwargs.pop('trust_remote_code'     ,    _SCREAMING_SNAKE_CASE   )
      __lowerCAmelCase:  int        = True

      __lowerCAmelCase:  str        = None
      __lowerCAmelCase:  int        = None

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

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

        __lowerCAmelCase:  Tuple        = config_dict.get('processor_class'     ,    _SCREAMING_SNAKE_CASE   )
        if "AutoProcessor" in config_dict.get('auto_map'     ,    {}   ):
         __lowerCAmelCase:  List[str]        = config_dict['auto_map']['AutoProcessor']

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

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

      if processor_class is not None:
       __lowerCAmelCase:  Optional[int]        = processor_class_from_name(_SCREAMING_SNAKE_CASE   )

      __lowerCAmelCase:  str        = processor_auto_map is not None
      __lowerCAmelCase:  List[Any]        = processor_class is not None or type(_SCREAMING_SNAKE_CASE   ) in PROCESSOR_MAPPING
      __lowerCAmelCase:  int        = resolve_trust_remote_code(
          _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE   )

      if has_remote_code and trust_remote_code:
       __lowerCAmelCase:  Tuple        = get_class_from_dynamic_module(
           _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE   )
       __lowerCAmelCase:  int        = kwargs.pop('code_revision'     ,    _SCREAMING_SNAKE_CASE   )
       if os.path.isdir(_SCREAMING_SNAKE_CASE   ):
        processor_class.register_for_auto_class()
       return processor_class.from_pretrained(
           _SCREAMING_SNAKE_CASE     ,    trust_remote_code=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE   )
      elif processor_class is not None:
       return processor_class.from_pretrained(
           _SCREAMING_SNAKE_CASE     ,    trust_remote_code=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE   )
      # Last try: we use the PROCESSOR_MAPPING.
      elif type(_SCREAMING_SNAKE_CASE   ) in PROCESSOR_MAPPING:
       return PROCESSOR_MAPPING[type(_SCREAMING_SNAKE_CASE   )].from_pretrained(_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_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(
           _SCREAMING_SNAKE_CASE     ,    trust_remote_code=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE   )
      except Exception:
       try:
        return AutoImageProcessor.from_pretrained(
            _SCREAMING_SNAKE_CASE     ,    trust_remote_code=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE   )
       except Exception:
        pass

       try:
        return AutoFeatureExtractor.from_pretrained(
            _SCREAMING_SNAKE_CASE     ,    trust_remote_code=_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_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     UpperCAmelCase    (				UpperCAmelCase     :						List[str]     ,    UpperCAmelCase     :						int   )				->      Optional[Any]:
      PROCESSOR_MAPPING.register(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE   )













import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCAmelCase_     =			pathlib.Path.cwd().joinpath('project_euler')
lowerCAmelCase_     =			pathlib.Path.cwd().joinpath(
    'scripts', 'project_euler_answers.json'
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
					lowerCAmelCase_     =			json.load(file_handle)







def   snake_case		(				UpperCAmelCase						:							pathlib.Path ):
				A									=   importlib.util.spec_from_file_location(file_path.name,    str(UpperCAmelCase ) )
				A									=   importlib.util.module_from_spec(UpperCAmelCase )  # type: ignore
				spec.loader.exec_module(UpperCAmelCase )  # type: ignore
				return module







def   snake_case		(				):
				A									=   []
				for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
								if problem_dir_path.is_file() or problem_dir_path.name.startswith('_' ):
												continue
								for file_path in problem_dir_path.iterdir():
												if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test') ):
																continue
												solution_file_paths.append(UpperCAmelCase )
				return solution_file_paths







def   snake_case		(				):
				with open(os.environ['GITHUB_EVENT_PATH'] ) as file:
								A									=   json.load(UpperCAmelCase )
				return event["pull_request"]["url"] + "/files"







def   snake_case		(				):
				A									=   []
				A									=   {
				    'Accept': 'application/vnd.github.v3+json',
				    'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
				}
				A									=   requests.get(get_files_url(),    headers=UpperCAmelCase ).json()
				for file in files:
								A									=   pathlib.Path.cwd().joinpath(file['filename'] )
								if (
								    filepath.suffix != ".py"
								    or filepath.name.startswith(('_', 'test') )
								    or not filepath.name.startswith('sol' )
								):
												continue
								solution_file_paths.append(UpperCAmelCase )
				return solution_file_paths







def   snake_case		(				):
				if os.environ.get('CI' ) and os.environ.get('GITHUB_EVENT_NAME' ) == "pull_request":
								# Return only if there are any, otherwise default to all solutions
								if filepaths := added_solution_file_path():
												return filepaths
				return all_solution_file_paths()







@pytest.mark.parametrize(
    'solution_path',    collect_solution_file_paths(),    ids=lambda UpperCAmelCase : f'{path.parent.name}/{path.name}',    )
def   snake_case		(				UpperCAmelCase						:							pathlib.Path ):
				A									=   solution_path.parent.name[8:].zfill(3 )
				A									=   PROBLEM_ANSWERS[problem_number]
				A									=   convert_path_to_module(UpperCAmelCase )
				A									=   str(solution_module.solution() )  # type: ignore
				A									=   hashlib.shaaaa(answer.encode() ).hexdigest()
				assert (
				    answer == expected
				), f'Expected solution to {problem_number} to have hash {expected}, got {answer}'





