
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


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

_snake_case			:       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"),
    ]
)

_snake_case			:       Union[str, Any]      =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


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

												lowercase						    =			importlib.import_module(F'''.{module_name}'''		,						"transformers.models"	)
												try:
																return getattr(__magic_name__		,						__magic_name__	)
												except AttributeError:
																continue

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

				return None





class      UpperCamelCase_			:







						'''simple docstring'''



						def __init__( self       :List[str]    )     ->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(_lowerCamelCase    )
						def 					SCREAMING_SNAKE_CASE( cls       :Optional[Any]		,						lowerCAmelCase__       :Optional[Any]		,						**lowerCAmelCase__       :Dict    )     ->Union[str, Any]:
										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						    =			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						    =			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 					SCREAMING_SNAKE_CASE( lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Optional[int]    )     ->Union[str, Any]:
										PROCESSOR_MAPPING.register(_lowerCamelCase		,						_lowerCamelCase    )


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


_snake_case			:       List[Any]      =						logging.get_logger(__name__)

_snake_case			:       Dict      =						{
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Any							      =				'''reformer'''
						UpperCamelCase			:     List[str]							      =				['''past_buckets_states''']
						UpperCamelCase			:     Optional[int]							      =				{}





						def __init__( self       :Dict		,						lowerCAmelCase__       :Any=64		,						lowerCAmelCase__       :str=["local", "lsh", "local", "lsh", "local", "lsh"]		,						lowerCAmelCase__       :Optional[Any]=1.0		,						lowerCAmelCase__       :List[str]=True		,						lowerCAmelCase__       :Optional[Any]=[64, 64]		,						lowerCAmelCase__       :Tuple=[64, 192]		,						lowerCAmelCase__       :Union[str, Any]=0		,						lowerCAmelCase__       :Optional[Any]=2		,						lowerCAmelCase__       :List[str]=512		,						lowerCAmelCase__       :Any=None		,						lowerCAmelCase__       :int="relu"		,						lowerCAmelCase__       :Tuple=0.05		,						lowerCAmelCase__       :int=256		,						lowerCAmelCase__       :Optional[Any]=0.02		,						lowerCAmelCase__       :Optional[int]=False		,						lowerCAmelCase__       :Optional[Any]=1E-1_2		,						lowerCAmelCase__       :int=1		,						lowerCAmelCase__       :Optional[Any]=0		,						lowerCAmelCase__       :Any=0.05		,						lowerCAmelCase__       :Optional[int]=64		,						lowerCAmelCase__       :List[Any]=64		,						lowerCAmelCase__       :Tuple=0.0		,						lowerCAmelCase__       :Tuple=1		,						lowerCAmelCase__       :List[str]=0		,						lowerCAmelCase__       :Dict=4096		,						lowerCAmelCase__       :Tuple=12		,						lowerCAmelCase__       :Tuple=None		,						lowerCAmelCase__       :Union[str, Any]=1		,						lowerCAmelCase__       :int=0		,						lowerCAmelCase__       :Optional[Any]=320		,						lowerCAmelCase__       :Optional[Any]=False		,						lowerCAmelCase__       :Optional[int]=True		,						lowerCAmelCase__       :str=None		,						**lowerCAmelCase__       :Any		,						)     ->Any:
										lowercase						    =			hash_seed
										lowercase						    =			vocab_size
										lowercase						    =			attention_head_size
										lowercase						    =			hidden_size
										lowercase						    =			num_attention_heads
										lowercase						    =			num_hashes
										lowercase						    =			len(lowerCAmelCase__    )
										lowercase						    =			tuple(lowerCAmelCase__    ) if isinstance(lowerCAmelCase__		,						lowerCAmelCase__    ) else num_buckets
										lowercase						    =			lsh_attn_chunk_length
										lowercase						    =			local_attn_chunk_length
										lowercase						    =			lsh_num_chunks_after
										lowercase						    =			lsh_num_chunks_before
										lowercase						    =			local_num_chunks_after
										lowercase						    =			local_num_chunks_before
										lowercase						    =			hidden_act
										lowercase						    =			feed_forward_size
										lowercase						    =			hidden_dropout_prob
										lowercase						    =			lsh_attention_probs_dropout_prob
										lowercase						    =			local_attention_probs_dropout_prob
										lowercase						    =			max_position_embeddings
										lowercase						    =			initializer_range
										lowercase						    =			layer_norm_eps
										lowercase						    =			axial_pos_embds
										lowercase						    =			tuple(lowerCAmelCase__    )
										lowercase						    =			tuple(lowerCAmelCase__    )
										lowercase						    =			axial_norm_std
										lowercase						    =			chunk_size_lm_head
										lowercase						    =			attn_layers
										lowercase						    =			use_cache
										lowercase						    =			classifier_dropout
										super().__init__(
										    pad_token_id=lowerCAmelCase__		,						eos_token_id=lowerCAmelCase__		,						is_decoder=lowerCAmelCase__		,						tie_word_embeddings=lowerCAmelCase__		,						**lowerCAmelCase__		,						)
