
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# 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 ...utils import CONFIG_NAME, 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,
)


_A   =     logging.get_logger(__name__)

_A   =     OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

_A   =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)






def    lowerCamelCase__				(				__lowerCAmelCase  :  str				):




			"""simple docstring"""


			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									lowerCAmelCase_						=	model_type_to_module_name(__lowerCAmelCase				)

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

			for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
						if getattr(__lowerCAmelCase    ,							"__name__"    ,							__lowerCAmelCase				) == class_name:
									return extractor

    # 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.
			lowerCAmelCase_						=	importlib.import_module("transformers"				)
			if hasattr(__lowerCAmelCase    ,							__lowerCAmelCase				):
						return getattr(__lowerCAmelCase    ,							__lowerCAmelCase				)

			return None






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, os.PathLike]    ,							__lowerCAmelCase  :  Optional[Union[str, os.PathLike]] = None    ,							__lowerCAmelCase  :  bool = False    ,							__lowerCAmelCase  :  bool = False    ,							__lowerCAmelCase  :  Optional[Dict[str, str]] = None    ,							__lowerCAmelCase  :  Optional[Union[bool, str]] = None    ,							__lowerCAmelCase  :  Optional[str] = None    ,							__lowerCAmelCase  :  bool = False    ,							**__lowerCAmelCase  :  Dict    ,							):




			"""simple docstring"""


			lowerCAmelCase_						=	get_file_from_repo(
			    __lowerCAmelCase    ,							__lowerCAmelCase    ,							cache_dir=__lowerCAmelCase    ,							force_download=__lowerCAmelCase    ,							resume_download=__lowerCAmelCase    ,							proxies=__lowerCAmelCase    ,							use_auth_token=__lowerCAmelCase    ,							revision=__lowerCAmelCase    ,							local_files_only=__lowerCAmelCase    ,							)
			if resolved_config_file is None:
						logger.info(
						    "Could not locate the feature extractor configuration file, will try to use the model config instead."				)
						return {}

			with open(__lowerCAmelCase    ,							encoding="utf-8"				) as reader:
						return json.load(__lowerCAmelCase				)





class 	_lowerCAmelCase					:




				def __init__(					self     )				->      Optional[int]:
							raise EnvironmentError(
							    "AutoFeatureExtractor is designed to be instantiated "
							    "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."     )




				@classmethod
				@replace_list_option_in_docstrings(_UpperCamelCase     )
				def      __a						(					cls				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	kwargs.pop("config"				,				_UpperCamelCase     )
							lowerCAmelCase_						=	kwargs.pop("trust_remote_code"				,				_UpperCamelCase     )
							lowerCAmelCase_						=	True

							lowerCAmelCase_	,  lowerCAmelCase_						=	FeatureExtractionMixin.get_feature_extractor_dict(_UpperCamelCase				,				**_UpperCamelCase     )
							lowerCAmelCase_						=	config_dict.get("feature_extractor_type"				,				_UpperCamelCase     )
							lowerCAmelCase_						=	None
							if "AutoFeatureExtractor" in config_dict.get("auto_map"				,				{}     ):
										lowerCAmelCase_						=	config_dict["auto_map"]["AutoFeatureExtractor"]

							# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
							if feature_extractor_class is None and feature_extractor_auto_map is None:
										if not isinstance(_UpperCamelCase				,				_UpperCamelCase     ):
													lowerCAmelCase_						=	AutoConfig.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
										# It could be in `config.feature_extractor_type``
										lowerCAmelCase_						=	getattr(_UpperCamelCase				,				"feature_extractor_type"				,				_UpperCamelCase     )
										if hasattr(_UpperCamelCase				,				"auto_map"     ) and "AutoFeatureExtractor" in config.auto_map:
													lowerCAmelCase_						=	config.auto_map["AutoFeatureExtractor"]

							if feature_extractor_class is not None:
										lowerCAmelCase_						=	feature_extractor_class_from_name(_UpperCamelCase     )

							lowerCAmelCase_						=	feature_extractor_auto_map is not None
							lowerCAmelCase_						=	feature_extractor_class is not None or type(_UpperCamelCase     ) in FEATURE_EXTRACTOR_MAPPING
							lowerCAmelCase_						=	resolve_trust_remote_code(
							    _UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )

							if has_remote_code and trust_remote_code:
										lowerCAmelCase_						=	get_class_from_dynamic_module(
										    _UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase     )
										lowerCAmelCase_						=	kwargs.pop("code_revision"				,				_UpperCamelCase     )
										if os.path.isdir(_UpperCamelCase     ):
													feature_extractor_class.register_for_auto_class()
										return feature_extractor_class.from_dict(_UpperCamelCase				,				**_UpperCamelCase     )
							elif feature_extractor_class is not None:
										return feature_extractor_class.from_dict(_UpperCamelCase				,				**_UpperCamelCase     )
							# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
							elif type(_UpperCamelCase     ) in FEATURE_EXTRACTOR_MAPPING:
										lowerCAmelCase_						=	FEATURE_EXTRACTOR_MAPPING[type(_UpperCamelCase     )]
										return feature_extractor_class.from_dict(_UpperCamelCase				,				**_UpperCamelCase     )

							raise ValueError(
							    f"""Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a """
							    f"""`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following """
							    f"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()     )}"""     )




				@staticmethod
				def      __a						(					_UpperCamelCase				,				_UpperCamelCase     )				->      Tuple:
							FEATURE_EXTRACTOR_MAPPING.register(_UpperCamelCase				,				_UpperCamelCase     )





from typing import TYPE_CHECKING

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


_A   =     {}

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

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


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

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

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



