





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										=					logging.get_logger(__name__)

_UpperCAmelCase										=					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										=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		str:







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

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

       return None







class 				_UpperCamelCase       :

       def __init__(		self:			Optional[Any]		)							->			int:




              """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(a_		)
       def lowercase			(		cls:			str       , _SCREAMING_SNAKE_CASE:			Optional[int]       , **_SCREAMING_SNAKE_CASE:			Tuple		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	kwargs.pop("config"       , a_		)
              UpperCamelCase_ 							=	kwargs.pop("trust_remote_code"       , a_		)
              UpperCamelCase_ 							=	True

              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None

              # First, let's see if we have a preprocessor config.
              # Filter the kwargs for `get_file_from_repo`.
              UpperCamelCase_ 							=	{
                  key: kwargs[key] for key in inspect.signature(a_		).parameters.keys() if key in kwargs
              }
              # Let's start by checking whether the processor class is saved in an image processor
              UpperCamelCase_ 							=	get_file_from_repo(
                  a_       , a_       , **a_		)
              if preprocessor_config_file is not None:
                     UpperCamelCase_							,			UpperCamelCase_ 							=	ImageProcessingMixin.get_image_processor_dict(a_       , **a_		)
                     UpperCamelCase_ 							=	config_dict.get("processor_class"       , a_		)
                     if "AutoProcessor" in config_dict.get("auto_map"       , {}		):
                            UpperCamelCase_ 							=	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:
                     UpperCamelCase_							,			UpperCamelCase_ 							=	FeatureExtractionMixin.get_feature_extractor_dict(a_       , **a_		)
                     UpperCamelCase_ 							=	config_dict.get("processor_class"       , a_		)
                     if "AutoProcessor" in config_dict.get("auto_map"       , {}		):
                            UpperCamelCase_ 							=	config_dict["auto_map"]["AutoProcessor"]

              if processor_class is None:
                     # Next, let's check whether the processor class is saved in a tokenizer
                     UpperCamelCase_ 							=	get_file_from_repo(
                         a_       , a_       , **a_		)
                     if tokenizer_config_file is not None:
                            with open(a_       , encoding="utf-8"		) as reader:
                                   UpperCamelCase_ 							=	json.load(a_		)

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

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

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

              if processor_class is not None:
                     UpperCamelCase_ 							=	processor_class_from_name(a_		)

              UpperCamelCase_ 							=	processor_auto_map is not None
              UpperCamelCase_ 							=	processor_class is not None or type(a_		) in PROCESSOR_MAPPING
              UpperCamelCase_ 							=	resolve_trust_remote_code(
                  a_       , a_       , a_       , a_		)

              if has_remote_code and trust_remote_code:
                     UpperCamelCase_ 							=	get_class_from_dynamic_module(
                         a_       , a_       , **a_		)
                     UpperCamelCase_ 							=	kwargs.pop("code_revision"       , a_		)
                     if os.path.isdir(a_		):
                            processor_class.register_for_auto_class()
                     return processor_class.from_pretrained(
                         a_       , trust_remote_code=a_       , **a_		)
              elif processor_class is not None:
                     return processor_class.from_pretrained(
                         a_       , trust_remote_code=a_       , **a_		)
              # Last try: we use the PROCESSOR_MAPPING.
              elif type(a_		) in PROCESSOR_MAPPING:
                     return PROCESSOR_MAPPING[type(a_		)].from_pretrained(a_       , **a_		)

              # 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(
                         a_       , trust_remote_code=a_       , **a_		)
              except Exception:
                     try:
                            return AutoImageProcessor.from_pretrained(
                                a_       , trust_remote_code=a_       , **a_		)
                     except Exception:
                            pass

                     try:
                            return AutoFeatureExtractor.from_pretrained(
                                a_       , trust_remote_code=a_       , **a_		)
                     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			(		_SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              PROCESSOR_MAPPING.register(a_       , a_		)









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase											=     {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """PegasusForCausalLM""",
         """PegasusForConditionalGeneration""",
         """PegasusModel""",
         """PegasusPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """TFPegasusForConditionalGeneration""",
         """TFPegasusModel""",
         """TFPegasusPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """FlaxPegasusForConditionalGeneration""",
         """FlaxPegasusModel""",
         """FlaxPegasusPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus import PegasusTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus_fast import PegasusTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_pegasus import (
              PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
              PegasusForCausalLM,
              PegasusForConditionalGeneration,
              PegasusModel,
              PegasusPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_pegasus import (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
              FlaxPegasusPreTrainedModel,
          )

else:
     import sys

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


