
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


lowercase__				:				Optional[Any]						=      logging.get_logger(__name__)

lowercase__				:				Optional[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"),
    ]
)

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




def 						A_			(				snake_case				: str			)   ->     int:



						'''simple docstring'''

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

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

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

						return None






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self      )-> Optional[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(SCREAMING_SNAKE_CASE_      )
							def      A__		(	cls  ,						SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      kwargs.pop('''config'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      kwargs.pop('''trust_remote_code'''  ,						SCREAMING_SNAKE_CASE_      )
													__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(SCREAMING_SNAKE_CASE_      ).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(
													    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
													if preprocessor_config_file is not None:
																			__UpperCamelCase	,				__UpperCamelCase  	=      ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      config_dict.get('''processor_class'''  ,						SCREAMING_SNAKE_CASE_      )
																			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(SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      config_dict.get('''processor_class'''  ,						SCREAMING_SNAKE_CASE_      )
																			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(
																			    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:
																															__UpperCamelCase  	=      json.load(SCREAMING_SNAKE_CASE_      )

																									__UpperCamelCase  	=      config_dict.get('''processor_class'''  ,						SCREAMING_SNAKE_CASE_      )
																									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(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																									__UpperCamelCase  	=      AutoConfig.from_pretrained(
																									    SCREAMING_SNAKE_CASE_  ,						trust_remote_code=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )

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

													if processor_class is not None:
																			__UpperCamelCase  	=      processor_class_from_name(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      processor_auto_map is not None
													__UpperCamelCase  	=      processor_class is not None or type(SCREAMING_SNAKE_CASE_      ) in PROCESSOR_MAPPING
													__UpperCamelCase  	=      resolve_trust_remote_code(
													    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

													if has_remote_code and trust_remote_code:
																			__UpperCamelCase  	=      get_class_from_dynamic_module(
																			    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      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      A__		(	SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )











"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

												A						=    encoder_ffn_dim
												A						=    A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    max_position_embeddings
												A						=    init_std  # Normal(0, this parameter)
												A						=    activation_function

												A						=    DecoderConfig(vocab_size=__UpperCamelCase	,			bos_token_id=__UpperCamelCase						)
												if "decoder" in common_kwargs:
																			del common_kwargs["decoder"]

												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    dropout

												A						=    use_cache
												super().__init__(
												    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			tie_word_embeddings=__UpperCamelCase	,			forced_eos_token_id=__UpperCamelCase	,			max_length=__UpperCamelCase	,			num_beams=__UpperCamelCase	,			length_penalty=__UpperCamelCase	,			early_stopping=__UpperCamelCase	,			**__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


