




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[Any]    						=	logging.get_logger(__name__)

__A    :				Dict    						=	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    :				Any    						=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''

			for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
						if class_name in processors:
									SCREAMING_SNAKE_CASE     =       model_type_to_module_name(_UpperCamelCase )

									SCREAMING_SNAKE_CASE     =       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.
			SCREAMING_SNAKE_CASE     =       importlib.import_module("""transformers""" )
			if hasattr(_UpperCamelCase       ,						_UpperCamelCase ):
						return getattr(_UpperCamelCase       ,						_UpperCamelCase )

			return None




class       UpperCAmelCase_   :




							'''simple docstring'''



							def __init__(       self							:						Union[str, Any]      )      ->       int:
										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							:						Tuple							,  a							:						Tuple							,  **a							:						int      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE     =       kwargs.pop("""config"""							,  lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       kwargs.pop("""trust_remote_code"""							,  lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       True

										SCREAMING_SNAKE_CASE     =       None
										SCREAMING_SNAKE_CASE     =       None

										# First, let's see if we have a preprocessor config.
										# Filter the kwargs for `get_file_from_repo`.
										SCREAMING_SNAKE_CASE     =       {
										    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
										SCREAMING_SNAKE_CASE     =       get_file_from_repo(
										    lowerCamelCase_							,  lowerCamelCase_							,  **lowerCamelCase_      )
										if preprocessor_config_file is not None:
													SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       ImageProcessingMixin.get_image_processor_dict(lowerCamelCase_							,  **lowerCamelCase_      )
													SCREAMING_SNAKE_CASE     =       config_dict.get("""processor_class"""							,  lowerCamelCase_      )
													if "AutoProcessor" in config_dict.get("""auto_map"""							,  {}      ):
																SCREAMING_SNAKE_CASE     =       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     =       FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase_							,  **lowerCamelCase_      )
													SCREAMING_SNAKE_CASE     =       config_dict.get("""processor_class"""							,  lowerCamelCase_      )
													if "AutoProcessor" in config_dict.get("""auto_map"""							,  {}      ):
																SCREAMING_SNAKE_CASE     =       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     =       get_file_from_repo(
													    lowerCamelCase_							,  lowerCamelCase_							,  **lowerCamelCase_      )
													if tokenizer_config_file is not None:
																with open(lowerCamelCase_							,  encoding="""utf-8"""      ) as reader:
																			SCREAMING_SNAKE_CASE     =       json.load(lowerCamelCase_      )

																SCREAMING_SNAKE_CASE     =       config_dict.get("""processor_class"""							,  lowerCamelCase_      )
																if "AutoProcessor" in config_dict.get("""auto_map"""							,  {}      ):
																			SCREAMING_SNAKE_CASE     =       config_dict["""auto_map"""]["""AutoProcessor"""]

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

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

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

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

										if has_remote_code and trust_remote_code:
													SCREAMING_SNAKE_CASE     =       get_class_from_dynamic_module(
													    lowerCamelCase_							,  lowerCamelCase_							,  **lowerCamelCase_      )
													SCREAMING_SNAKE_CASE     =       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  (       a							:						Any							,  a							:						List[str]      )      ->       Any:
										PROCESSOR_MAPPING.register(lowerCamelCase_							,  lowerCamelCase_      )










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a      			= {}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BarthezTokenizerFast"]


if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez import BarthezTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_barthez_fast import BarthezTokenizerFast

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)