






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:  Any   =   logging.get_logger(__name__)

__UpperCamelCase:  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"""),
    ]
)

__UpperCamelCase:  List[str]   =   _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str )      ->       Dict:




		'''simple docstring'''
		for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
				if class_name in processors:
						lowercase__				:			int            =		model_type_to_module_name(_lowercase )

						lowercase__				:			str            =		importlib.import_module(f""".{module_name}"""      ,						'transformers.models' )
						try:
								return getattr(_lowercase      ,						_lowercase )
						except AttributeError:
								continue

		for processor in PROCESSOR_MAPPING._extra_content.values():
				if getattr(_lowercase      ,						'__name__'      ,						_lowercase ) == 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__				:			Optional[Any]            =		importlib.import_module('transformers' )
		if hasattr(_lowercase      ,						_lowercase ):
				return getattr(_lowercase      ,						_lowercase )

		return None





class 			__lowerCAmelCase				:


	'''simple docstring'''





	def __init__(	self:	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       snake_case__(	cls:	Optional[Any],				lowerCamelCase_:	str,				**lowerCamelCase_:	int ):
			lowercase__				:			Any            =		kwargs.pop('config',				lowerCamelCase_ )
			lowercase__				:			Union[str, Any]            =		kwargs.pop('trust_remote_code',				lowerCamelCase_ )
			lowercase__				:			Optional[Any]            =		True

			lowercase__				:			List[Any]            =		None
			lowercase__				:			Union[str, Any]            =		None

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

			if processor_class is None:
					# Next, let's check whether the processor class is saved in a tokenizer
					lowercase__				:			Tuple            =		get_file_from_repo(
					    lowerCamelCase_,				lowerCamelCase_,				**lowerCamelCase_ )
					if tokenizer_config_file is not None:
							with open(lowerCamelCase_,				encoding='utf-8' ) as reader:
									lowercase__				:			Optional[int]            =		json.load(lowerCamelCase_ )

							lowercase__				:			Union[str, Any]            =		config_dict.get('processor_class',				lowerCamelCase_ )
							if "AutoProcessor" in config_dict.get('auto_map',				{} ):
									lowercase__				:			Union[str, Any]            =		config_dict['auto_map']['AutoProcessor']

			if processor_class is None:
					# Otherwise, load config, if it can be loaded.
					if not isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			int            =		AutoConfig.from_pretrained(
							    lowerCamelCase_,				trust_remote_code=lowerCamelCase_,				**lowerCamelCase_ )

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

			if processor_class is not None:
					lowercase__				:			List[str]            =		processor_class_from_name(lowerCamelCase_ )

			lowercase__				:			Optional[int]            =		processor_auto_map is not None
			lowercase__				:			Any            =		processor_class is not None or type(lowerCamelCase_ ) in PROCESSOR_MAPPING
			lowercase__				:			int            =		resolve_trust_remote_code(
			    lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )

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









import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union


class 			__lowerCAmelCase				(					enum.Enum			):


	'''simple docstring'''





	_A       	=							"timm"
	_A       	=							"transformers"


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[Iterable[str]]      ,						_lowercase    :   Optional[Iterable[int]]      ,						_lowercase    :   Optional[Iterable[str]] )      ->       Optional[int]:




		'''simple docstring'''
		if stage_names is None:
				raise ValueError('Stage_names must be set for transformers backbones' )

		if out_features is not None:
				if not isinstance(_lowercase      ,						(list,) ):
						raise ValueError(f"""out_features must be a list {type(_lowercase )}""" )
				if any(feat not in stage_names for feat in out_features ):
						raise ValueError(f"""out_features must be a subset of stage_names: {stage_names} got {out_features}""" )

		if out_indices is not None:
				if not isinstance(_lowercase      ,						(list, tuple) ):
						raise ValueError(f"""out_indices must be a list or tuple, got {type(_lowercase )}""" )
				if any(idx >= len(_lowercase ) for idx in out_indices ):
						raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}' )

		if out_features is not None and out_indices is not None:
				if len(_lowercase ) != len(_lowercase ):
						raise ValueError('out_features and out_indices should have the same length if both are set' )
				if out_features != [stage_names[idx] for idx in out_indices]:
						raise ValueError('out_features and out_indices should correspond to the same stages if both are set' )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[List[str]]      ,						_lowercase    :   Optional[Union[List[int], Tuple[int]]]      ,						_lowercase    :   List[str]      ,						)      ->       Dict:




		'''simple docstring'''
		if out_indices is None and out_features is None:
				lowercase__				:			str            =		[len(_lowercase ) - 1]
				lowercase__				:			Union[str, Any]            =		[stage_names[-1]]
		elif out_indices is None and out_features is not None:
				lowercase__				:			Optional[int]            =		[stage_names.index(_lowercase ) for layer in out_features]
		elif out_features is None and out_indices is not None:
				lowercase__				:			str            =		[stage_names[idx] for idx in out_indices]
		return out_features, out_indices


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[List[str]]      ,						_lowercase    :   Optional[Union[List[int], Tuple[int]]]      ,						_lowercase    :   List[str]      ,						)      ->       Tuple[List[str], List[int]]:




		'''simple docstring'''
		verify_out_features_out_indices(out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		lowercase__      ,							lowercase__				:			List[str]            =		_align_output_features_output_indices(
		    out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		# Verify that the aligned out_features and out_indices are valid
		verify_out_features_out_indices(out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		return output_features, output_indices


class 			__lowerCAmelCase				:


	'''simple docstring'''





	_A       	=							None

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	Optional[Any] ):
			if getattr(self,				'_backbone',				lowerCamelCase_ ) is None:
					raise ValueError('self._backbone must be set before calling _init_timm_backbone' )

			# These will diagree with the defaults for the transformers models e.g. for resnet50
			# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
			# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
			lowercase__				:			Tuple            =		[stage['module'] for stage in self._backbone.feature_info.info]
			lowercase__				:			Union[str, Any]            =		[stage['num_chs'] for stage in self._backbone.feature_info.info]
			lowercase__				:			Dict            =		self._backbone.feature_info.out_indices
			lowercase__				:			Tuple            =		self._backbone.feature_info.module_name()

			# We verify the out indices and out features are valid
			verify_out_features_out_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )
			lowercase__      ,							lowercase__				:			List[Any]            =		out_features, out_indices

	def       snake_case__(	self:	Any,				lowerCamelCase_:	List[str] ):
			lowercase__				:			Dict            =		getattr(lowerCamelCase_,				'stage_names' )
			lowercase__				:			List[str]            =		getattr(lowerCamelCase_,				'out_features',				lowerCamelCase_ )
			lowercase__				:			Optional[int]            =		getattr(lowerCamelCase_,				'out_indices',				lowerCamelCase_ )

			lowercase__				:			Optional[int]            =		stage_names
			lowercase__      ,							lowercase__				:			Union[str, Any]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=lowerCamelCase_ )
			# Number of channels for each stage. This is set in the transformer backbone model init
			lowercase__				:			Dict            =		None

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	List[str] ):
			lowercase__				:			List[Any]            =		config

			lowercase__				:			Optional[int]            =		getattr(lowerCamelCase_,				'use_timm_backbone',				lowerCamelCase_ )
			lowercase__				:			List[Any]            =		BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

			if self.backbone_type == BackboneType.TIMM:
					self._init_timm_backbone(lowerCamelCase_ )
			elif self.backbone_type == BackboneType.TRANSFORMERS:
					self._init_transformers_backbone(lowerCamelCase_ )
			else:
					raise ValueError(F"""backbone_type {self.backbone_type} not supported.""" )

	@property
	def       snake_case__(	self:	List[str] ):
			return self._out_features

	@out_features.setter
	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	List[str] ):
			lowercase__      ,							lowercase__				:			Optional[int]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	List[Any] ):
			return self._out_indices

	@out_indices.setter
	def       snake_case__(	self:	Tuple,				lowerCamelCase_:	Union[Tuple[int], List[int]] ):
			lowercase__      ,							lowercase__				:			List[str]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	str ):
			# the current backbones will output the number of channels for each stage
			# even if that stage is not in the out_features list.
			return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}

	@property
	def       snake_case__(	self:	Optional[int] ):
			return [self.out_feature_channels[name] for name in self.out_features]

	def       snake_case__(	self:	List[Any],				*lowerCamelCase_:	List[Any],				**lowerCamelCase_:	Optional[Any] ):
			lowercase__				:			Any            =		dict(inspect.signature(self.forward ).parameters )
			lowercase__				:			List[Any]            =		{k: v for k, v in kwargs.items() if k in signature}
			return self(*lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	int,				lowerCamelCase_:	str,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	Optional[bool] = None,				):
			raise NotImplementedError('This method should be implemented by the derived class.' )





	def       snake_case__(	self:	Dict ):
			lowercase__				:			List[Any]            =		super().to_dict()
			lowercase__				:			Union[str, Any]            =		output.pop('_out_features' )
			lowercase__				:			Any            =		output.pop('_out_indices' )
			return output


class 			__lowerCAmelCase				:


	'''simple docstring'''





	@property
	def       snake_case__(	self:	Optional[int] ):
			return self._out_features

	@out_features.setter
	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	List[str] ):
			lowercase__      ,							lowercase__				:			List[Any]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	Dict ):
			return self._out_indices

	@out_indices.setter
	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	Union[Tuple[int], List[int]] ):
			lowercase__      ,							lowercase__				:			Any            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )





	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			List[str]            =		super().to_dict()
			lowercase__				:			str            =		output.pop('_out_features' )
			lowercase__				:			str            =		output.pop('_out_indices' )
			return output


