



'''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


UpperCamelCase__	:     Optional[int]    =			logging.get_logger(__name__)

UpperCamelCase__	:     Union[str, 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'''),
    ]
)

UpperCamelCase__	:     str    =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def     lowerCAmelCase_       (     _lowerCamelCase:       str ):

		for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
				if class_name in processors:
						__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_type_to_module_name(_lowerCamelCase )

						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		importlib.import_module(F".{module_name}"	,				"""transformers.models""" )
						try:
								return getattr(_lowerCamelCase	,				_lowerCamelCase )
						except AttributeError:
								continue

		for processor in PROCESSOR_MAPPING._extra_content.values():
				if getattr(_lowerCamelCase	,				"""__name__"""	,				_lowerCamelCase ) == 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.
		__SCREAMING_SNAKE_CASE	:		str   	=		importlib.import_module("""transformers""" )
		if hasattr(_lowerCamelCase	,				_lowerCamelCase ):
				return getattr(_lowerCamelCase	,				_lowerCamelCase )

		return None






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			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(SCREAMING_SNAKE_CASE_						)
			def UpperCamelCase__ (							cls					:			List[Any]							,					lowerCAmelCase__					:			Any							,					**lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		kwargs.pop("""config"""							,					SCREAMING_SNAKE_CASE_						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		kwargs.pop("""trust_remote_code"""							,					SCREAMING_SNAKE_CASE_						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		True

					__SCREAMING_SNAKE_CASE	:		Dict   	=		None
					__SCREAMING_SNAKE_CASE	:		Any   	=		None

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

					if processor_class is None:
							# Next, let's check whether the processor class is saved in a tokenizer
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		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:
											__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		json.load(SCREAMING_SNAKE_CASE_						)

									__SCREAMING_SNAKE_CASE	:		str   	=		config_dict.get("""processor_class"""							,					SCREAMING_SNAKE_CASE_						)
									if "AutoProcessor" in config_dict.get("""auto_map"""							,					{}						):
											__SCREAMING_SNAKE_CASE	:		int   	=		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_						):
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		AutoConfig.from_pretrained(
									    SCREAMING_SNAKE_CASE_							,					trust_remote_code=SCREAMING_SNAKE_CASE_							,					**SCREAMING_SNAKE_CASE_						)

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

					if processor_class is not None:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		processor_class_from_name(SCREAMING_SNAKE_CASE_						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		processor_auto_map is not None
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		processor_class is not None or type(SCREAMING_SNAKE_CASE_						) in PROCESSOR_MAPPING
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		resolve_trust_remote_code(
					    SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_						)

					if has_remote_code and trust_remote_code:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		get_class_from_dynamic_module(
							    SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					**SCREAMING_SNAKE_CASE_						)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		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__ (							lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_						)





'''simple docstring'''




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCamelCase__	:     str    =			TypeVar('''T''', bound=bool)






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		T   	=		value
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

			@property
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					return self._value



			@staticmethod
			def UpperCamelCase__ (							lowerCAmelCase__					:			RandomizedHeapNode[T] | None							,					lowerCAmelCase__					:			RandomizedHeapNode[T] | None						):





					"""simple docstring"""






					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		roota, roota

					if random.choice([True, False]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		roota.right, roota.left

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		RandomizedHeapNode.merge(roota.left							,					lowerCAmelCase__						)

					return roota






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			Iterable[T] | None = ()						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

					if data:
							for item in data:
									self.insert(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		RandomizedHeapNode.merge(self._root							,					RandomizedHeapNode(lowerCAmelCase__						)						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.top()

					if self._root is None:
							return None

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		RandomizedHeapNode.merge(self._root.left							,					self._root.right						)

					return result

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					if not self._root:
							raise IndexError("""Can't get top element for the empty heap."""						)
					return self._root.value

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		[]
					while self:
							result.append(self.pop()						)

					return result



			def __bool__(							self					:			List[Any]						):





					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()