
"""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


_lowercase							 =							logging.get_logger(__name__)

_lowercase							 =							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'''),
    ]
)

_lowercase							 =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)



def     lowerCAmelCase__ (		__magic_name__			)	->int:
			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									__lowercase						      =					model_type_to_module_name(__magic_name__			)

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

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

			return None







class 				__a    :



							'''simple docstring'''







							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(_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    cls     ,						_lowerCamelCase     ,						**_lowerCamelCase					) ->       Optional[Any]:




										'''simple docstring'''
										__lowercase						      =					kwargs.pop("config"     ,						_lowerCamelCase					)
										__lowercase						      =					kwargs.pop("trust_remote_code"     ,						_lowerCamelCase					)
										__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(_lowerCamelCase					).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(
										    _lowerCamelCase     ,						_lowerCamelCase     ,						**_lowerCamelCase					)
										if preprocessor_config_file is not None:
													__lowercase      ,				__lowercase						      =					ImageProcessingMixin.get_image_processor_dict(_lowerCamelCase     ,						**_lowerCamelCase					)
													__lowercase						      =					config_dict.get("processor_class"     ,						_lowerCamelCase					)
													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      ,				__lowercase						      =					FeatureExtractionMixin.get_feature_extractor_dict(_lowerCamelCase     ,						**_lowerCamelCase					)
													__lowercase						      =					config_dict.get("processor_class"     ,						_lowerCamelCase					)
													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(
													    _lowerCamelCase     ,						_lowerCamelCase     ,						**_lowerCamelCase					)
													if tokenizer_config_file is not None:
																with open(_lowerCamelCase     ,						encoding="utf-8"					) as reader:
																			__lowercase						      =					json.load(_lowerCamelCase					)

																__lowercase						      =					config_dict.get("processor_class"     ,						_lowerCamelCase					)
																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(_lowerCamelCase     ,						_lowerCamelCase					):
																__lowercase						      =					AutoConfig.from_pretrained(
																    _lowerCamelCase     ,						trust_remote_code=_lowerCamelCase     ,						**_lowerCamelCase					)

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

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

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

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

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

													try:
																return AutoFeatureExtractor.from_pretrained(
																    _lowerCamelCase     ,						trust_remote_code=_lowerCamelCase     ,						**_lowerCamelCase					)
													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      SCREAMING_SNAKE_CASE	(    _lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[int]:




										'''simple docstring'''
										PROCESSOR_MAPPING.register(_lowerCamelCase     ,						_lowerCamelCase					)







"""simple docstring"""






def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->list:
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					len(__magic_name__			)
			__lowercase						      =					(
			    first_input_list_length
			    if first_input_list_length > second_input_list_length
			    else second_input_list_length
			)
			__lowercase						      =					[]
			for char_count in range(__magic_name__			):
						if char_count < first_input_list_length:
									output_result_list.append(first_input_list[char_count]			)
						if char_count < second_input_list_length:
									output_result_list.append(second_input_list[char_count]			)

			return output_result_list


if __name__ == "__main__":
					print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





