


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


SCREAMING_SNAKE_CASE_      :			Tuple		  =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      :			List[str]		  =      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'''),
    ]
)

SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Dict:
		for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
				if class_name in processors:
						__lowercase					=    model_type_to_module_name(snake_case       )

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

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

		return None


class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    Union[str, Any]      )					->   str:

			'''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       UpperCAmelCase (	cls       :    Union[str, Any]				,			__lowerCamelCase       :    int				,			**__lowerCamelCase       :    Tuple      )					->   List[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       UpperCAmelCase (	__lowerCamelCase       :    int				,			__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			PROCESSOR_MAPPING.register(__lowerCamelCase				,			__lowerCamelCase      )











from __future__ import annotations



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						None:
		__lowercase	,			__lowercase					=    m - 1, 0
		while i >= 0 and j < n:
				if key == mat[i][j]:
						print(F"Key {key} found at row- {i + 1} column- {j + 1}"       )
						return
				if key < mat[i][j]:
						i -= 1
				else:
						j += 1
		print(F"Key {key} not found"       )



def    SCREAMING_SNAKE_CASE							(			)		->						None:
		__lowercase					=    [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		__lowercase					=    int(input('Enter the element to be searched:'       )       )
		print(snake_case       )
		search_in_a_sorted_matrix(snake_case	,      len(snake_case       )	,      len(mat[0]       )	,      snake_case       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




