





'''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


snake_case_   :  Dict								=      logging.get_logger(__name__)

snake_case_   :  List[Any]								=      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'''),
    ]
)

snake_case_   :  Any								=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)



def  lowercase__(		_UpperCamelCase  : Dict				)->   Tuple:






					"""simple docstring"""

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

															_UpperCamelCase											=   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.
					_UpperCamelCase											=   importlib.import_module("transformers"				)
					if hasattr(_UpperCAmelCase ,   _UpperCAmelCase				):
										return getattr(_UpperCAmelCase ,   _UpperCAmelCase				)

					return None

class     A_ :

			'''simple docstring'''





			def __init__(					self   ):
								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       a	(					cls			,     A_			,     **A_   ):
								_UpperCamelCase											=   kwargs.pop("config"			,     SCREAMING_SNAKE_CASE_   )
								_UpperCamelCase											=   kwargs.pop("trust_remote_code"			,     SCREAMING_SNAKE_CASE_   )
								_UpperCamelCase											=   True

								_UpperCamelCase											=   None
								_UpperCamelCase											=   None

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

								if processor_class is None:
													# Next, let's check whether the processor class is saved in a tokenizer
													_UpperCamelCase											=   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:
																							_UpperCamelCase											=   json.load(SCREAMING_SNAKE_CASE_   )

																		_UpperCamelCase											=   config_dict.get("processor_class"			,     SCREAMING_SNAKE_CASE_   )
																		if "AutoProcessor" in config_dict.get("auto_map"			,     {}   ):
																							_UpperCamelCase											=   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_   ):
																		_UpperCamelCase											=   AutoConfig.from_pretrained(
																		    SCREAMING_SNAKE_CASE_			,     trust_remote_code=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_   )

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

								if processor_class is not None:
													_UpperCamelCase											=   processor_class_from_name(SCREAMING_SNAKE_CASE_   )

								_UpperCamelCase											=   processor_auto_map is not None
								_UpperCamelCase											=   processor_class is not None or type(SCREAMING_SNAKE_CASE_   ) in PROCESSOR_MAPPING
								_UpperCamelCase											=   resolve_trust_remote_code(
								    SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_   )

								if has_remote_code and trust_remote_code:
													_UpperCamelCase											=   get_class_from_dynamic_module(
													    SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_   )
													_UpperCamelCase											=   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       a	(					A_			,     A_   ):
								PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_   )









'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[int]				)->   list[int]:






					"""simple docstring"""

					if not list_of_ints:
										return []

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

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



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int				)->   list[int]:






					"""simple docstring"""

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

					_UpperCamelCase											=   []
					_UpperCamelCase											=   []
					# 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(_UpperCamelCase				)
										else:
															# number has a zero at bit bit_position
															zeros.append(_UpperCamelCase				)

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

					# recombine lists
					_UpperCamelCase											=   zeros
					res.extend(_UpperCamelCase				)

					return res



def  lowercase__(		_UpperCamelCase  : list[int]				)->   Union[str, Any]:






					"""simple docstring"""

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

					if min(_UpperCamelCase				) < 0:
										raise ValueError("All numbers must be positive"				)

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



def  lowercase__(		_UpperCamelCase  : list[int] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int				)->   Tuple:






					"""simple docstring"""

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

					bit_position -= 1

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

										if changed:
															continue

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

					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)
					_msd_radix_sort_inplace(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



