


"""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


__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__			(							_UpperCamelCase			):





						"""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 				lowerCamelCase							:


							"""simple docstring"""






							def __init__(    self			:  Union[str, Any]					):
													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        lowerCAmelCase  (    cls			:  Tuple		,			lowerCamelCase_			:  Tuple		,			**lowerCamelCase_			:  int					):
													UpperCAmelCase__												=     kwargs.pop('''config'''		,			lowerCamelCase_					)
													UpperCAmelCase__												=     kwargs.pop('''trust_remote_code'''		,			lowerCamelCase_					)
													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(lowerCamelCase_					).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(
													    lowerCamelCase_		,			lowerCamelCase_		,			**lowerCamelCase_					)
													if preprocessor_config_file is not None:
																			UpperCAmelCase__						,					UpperCAmelCase__												=     ImageProcessingMixin.get_image_processor_dict(lowerCamelCase_		,			**lowerCamelCase_					)
																			UpperCAmelCase__												=     config_dict.get('''processor_class'''		,			lowerCamelCase_					)
																			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(lowerCamelCase_		,			**lowerCamelCase_					)
																			UpperCAmelCase__												=     config_dict.get('''processor_class'''		,			lowerCamelCase_					)
																			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(
																			    lowerCamelCase_		,			lowerCamelCase_		,			**lowerCamelCase_					)
																			if tokenizer_config_file is not None:
																									with open(lowerCamelCase_		,			encoding='''utf-8'''					) as reader:
																															UpperCAmelCase__												=     json.load(lowerCamelCase_					)

																									UpperCAmelCase__												=     config_dict.get('''processor_class'''		,			lowerCamelCase_					)
																									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(lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     AutoConfig.from_pretrained(
																									    lowerCamelCase_		,			trust_remote_code=lowerCamelCase_		,			**lowerCamelCase_					)

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

													if processor_class is not None:
																			UpperCAmelCase__												=     processor_class_from_name(lowerCamelCase_					)

													UpperCAmelCase__												=     processor_auto_map is not None
													UpperCAmelCase__												=     processor_class is not None or type(lowerCamelCase_					) in PROCESSOR_MAPPING
													UpperCAmelCase__												=     resolve_trust_remote_code(
													    lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

													if has_remote_code and trust_remote_code:
																			UpperCAmelCase__												=     get_class_from_dynamic_module(
																			    lowerCamelCase_		,			lowerCamelCase_		,			**lowerCamelCase_					)
																			UpperCAmelCase__												=     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        lowerCAmelCase  (    lowerCamelCase_			:  Any		,			lowerCamelCase_			:  List[str]					):
													PROCESSOR_MAPPING.register(lowerCamelCase_		,			lowerCamelCase_					)



"""simple docstring"""







from __future__ import annotations






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     sorted(_UpperCamelCase			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)






def        a__			(							):





						"""simple docstring"""

						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()