

"""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


a__			:						int							=			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__			:						List[str]							=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 					__UpperCamelCase (     a__:			Optional[int]      )					->      Dict:




					"""simple docstring"""



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

															__lowercase        =      importlib.import_module(f'''.{module_name}'''			,						'''transformers.models'''      )
															try:
																				return getattr(A__			,						A__      )
															except AttributeError:
																				continue

					for processor in PROCESSOR_MAPPING._extra_content.values():
										if getattr(A__			,						'''__name__'''			,						A__      ) == 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(A__			,						A__      ):
										return getattr(A__			,						A__      )

					return None


class       _snake_case  :







						def __init__(self				)      ->    Union[str, 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(SCREAMING_SNAKE_CASE__				)
						def 							_UpperCamelCase     (cls  ,				UpperCAmelCase_  ,				**UpperCAmelCase_				)      ->    Optional[Any]:
											__lowercase        =      kwargs.pop('''config'''  ,				SCREAMING_SNAKE_CASE__				)
											__lowercase        =      kwargs.pop('''trust_remote_code'''  ,				SCREAMING_SNAKE_CASE__				)
											__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(SCREAMING_SNAKE_CASE__				).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(
											    SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				**SCREAMING_SNAKE_CASE__				)
											if preprocessor_config_file is not None:
																__lowercase        =      ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE__  ,				**SCREAMING_SNAKE_CASE__				)
																__lowercase        =      config_dict.get('''processor_class'''  ,				SCREAMING_SNAKE_CASE__				)
																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        =      FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE__  ,				**SCREAMING_SNAKE_CASE__				)
																__lowercase        =      config_dict.get('''processor_class'''  ,				SCREAMING_SNAKE_CASE__				)
																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(
																    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:
																										__lowercase        =      json.load(SCREAMING_SNAKE_CASE__				)

																					__lowercase        =      config_dict.get('''processor_class'''  ,				SCREAMING_SNAKE_CASE__				)
																					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(SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__				):
																					__lowercase        =      AutoConfig.from_pretrained(
																					    SCREAMING_SNAKE_CASE__  ,				trust_remote_code=SCREAMING_SNAKE_CASE__  ,				**SCREAMING_SNAKE_CASE__				)

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

											if processor_class is not None:
																__lowercase        =      processor_class_from_name(SCREAMING_SNAKE_CASE__				)

											__lowercase        =      processor_auto_map is not None
											__lowercase        =      processor_class is not None or type(SCREAMING_SNAKE_CASE__				) in PROCESSOR_MAPPING
											__lowercase        =      resolve_trust_remote_code(
											    SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__				)

											if has_remote_code and trust_remote_code:
																__lowercase        =      get_class_from_dynamic_module(
																    SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				**SCREAMING_SNAKE_CASE__				)
																__lowercase        =      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     (UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    int:
											PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__				)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_			      =  {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						UpperCAmelCase_			      =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





