



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

A__     :      Optional[int]             =			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__     :      List[Any]             =			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''



				for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
								if class_name in processors:
												lowercase__       			=      model_type_to_module_name(lowerCamelCase_    )

												lowercase__       			=      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.
				lowercase__       			=      importlib.import_module('''transformers'''    )
				if hasattr(lowerCamelCase_		,       lowerCamelCase_    ):
								return getattr(lowerCamelCase_		,       lowerCamelCase_    )

				return None




class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   List[Any]  ):

								'''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     lowercase__       (					cls						:   int,  lowerCamelCase						:   Optional[int],  **lowerCamelCase						:   Tuple  ):

								'''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     lowercase__       (					lowerCamelCase						:   Optional[int],  lowerCamelCase						:   int  ):

								'''simple docstring'''
								PROCESSOR_MAPPING.register(lowerCAmelCase__,  lowerCAmelCase__  )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__     :      Dict             =			{'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SEWDForCTC',
							    'SEWDForSequenceClassification',
							    'SEWDModel',
							    'SEWDPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_sew_d import (
														    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SEWDForCTC,
														    SEWDForSequenceClassification,
														    SEWDModel,
														    SEWDPreTrainedModel,
														)


else:
							import sys

							A__     :      Tuple             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




