




import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


_snake_case										=       threading.Lock()
_snake_case										=       None

_snake_case										=       {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_snake_case										=       logging.WARNING

_snake_case										=       True







def 				lowerCAmelCase_	(     ):
      _A							:     str			=						os.getenv("""TRANSFORMERS_VERBOSITY""",snake_case_     )
      if env_level_str:
            if env_level_str in log_levels:
                  return log_levels[env_level_str]
            else:
                  logging.getLogger().warning(
                      f'''Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '''
                      f'''has to be one of: { ", ".join(log_levels.keys()     ) }'''     )
      return _default_log_level







def 				lowerCAmelCase_	(     ):
      return __name__.split("""."""     )[0]







def 				lowerCAmelCase_	(     ):
      return logging.getLogger(_get_library_name()     )







def 				lowerCAmelCase_	(     ):
      global _default_handler

      with _lock:
            if _default_handler:
                  # This library has already configured the library root logger.
                  return
            _A							:     Union[str, Any]			=						logging.StreamHandler()  # Set sys.stderr as stream.
            _A							:     Tuple			=						sys.stderr.flush

            # Apply our default configuration to the library root logger.
            _A							:     int			=						_get_library_root_logger()
            library_root_logger.addHandler(_default_handler     )
            library_root_logger.setLevel(_get_default_logging_level()     )
            _A							:     List[str]			=						False







def 				lowerCAmelCase_	(     ):
      global _default_handler

      with _lock:
            if not _default_handler:
                  return

            _A							:     List[str]			=						_get_library_root_logger()
            library_root_logger.removeHandler(_default_handler     )
            library_root_logger.setLevel(logging.NOTSET     )
            _A							:     Tuple			=						None







def 				lowerCAmelCase_	(     ):
      return log_levels







def 				lowerCAmelCase_	(     snake_case_ = None     ):

      if name is None:
            _A							:     Optional[int]			=						_get_library_name()

      _configure_library_root_logger()
      return logging.getLogger(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _configure_library_root_logger()
      return _get_library_root_logger().getEffectiveLevel()







def 				lowerCAmelCase_	(     snake_case_     ):
      _configure_library_root_logger()
      _get_library_root_logger().setLevel(snake_case_     )







def 				lowerCAmelCase_	(     ):
      return set_verbosity(snake_case_     )







def 				lowerCAmelCase_	(     ):
      return set_verbosity(snake_case_     )







def 				lowerCAmelCase_	(     ):
      return set_verbosity(snake_case_     )







def 				lowerCAmelCase_	(     ):
      return set_verbosity(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _configure_library_root_logger()

      assert _default_handler is not None
      _get_library_root_logger().removeHandler(_default_handler     )







def 				lowerCAmelCase_	(     ):
      _configure_library_root_logger()

      assert _default_handler is not None
      _get_library_root_logger().addHandler(_default_handler     )







def 				lowerCAmelCase_	(     snake_case_     ):
      _configure_library_root_logger()

      assert handler is not None
      _get_library_root_logger().addHandler(snake_case_     )







def 				lowerCAmelCase_	(     snake_case_     ):
      _configure_library_root_logger()

      assert handler is not None and handler not in _get_library_root_logger().handlers
      _get_library_root_logger().removeHandler(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _configure_library_root_logger()
      _A							:     Union[str, Any]			=						False







def 				lowerCAmelCase_	(     ):
      _configure_library_root_logger()
      _A							:     List[Any]			=						True







def 				lowerCAmelCase_	(     ):
      _A							:     Union[str, Any]			=						_get_library_root_logger().handlers

      for handler in handlers:
            _A							:     str			=						logging.Formatter("""[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"""     )
            handler.setFormatter(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Any			=						_get_library_root_logger().handlers

      for handler in handlers:
            handler.setFormatter(snake_case_     )







def 				lowerCAmelCase_	(     self,*snake_case_,**snake_case_     ):
      _A							:     str			=						os.getenv("""TRANSFORMERS_NO_ADVISORY_WARNINGS""",snake_case_     )
      if no_advisory_warnings:
            return
      self.warning(*snake_case_,**snake_case_     )


_snake_case										=       warning_advice







@functools.lru_cache(snake_case_     )
def 				lowerCAmelCase_	(     self,*snake_case_,**snake_case_     ):
      self.warning(*snake_case_,**snake_case_     )


_snake_case										=       warning_once







class 						lowercase							:

     def __init__(  self		,     *_a		,     **_a				)						->							List[Any]:  # pylint: disable=unused-argument
           _A							:     List[str]			=						args[0] if args else None

     def __iter__(  self				)						->							Optional[Any]:
           return iter(self._iterator				)

     def __getattr__(  self		,     _a				)						->							Union[str, Any]:

           def empty_fn(*_a		,     **_a				):  # pylint: disable=unused-argument
                 return

           return empty_fn

     def __enter__(  self				)						->							Tuple:
           return self



     def __exit__(  self		,     _a		,     _a		,     _a				)						->							List[str]:
           return







class 						lowercase							:

     def __call__(  self		,     *_a		,     **_a				)						->							Union[str, Any]:
           if _tqdm_active:
                 return tqdm_lib.tqdm(*_a		,     **_a				)
           else:
                 return EmptyTqdm(*_a		,     **_a				)

     def 					a__	(  self		,     *_a		,     **_a				)						->							Dict:
           _A							:     Optional[Any]			=						None
           if _tqdm_active:
                 return tqdm_lib.tqdm.set_lock(*_a		,     **_a				)



     def 					a__	(  self				)						->							Union[str, Any]:
           if _tqdm_active:
                 return tqdm_lib.tqdm.get_lock()


_snake_case										=       _tqdm_cls()







def 				lowerCAmelCase_	(     ):
      global _tqdm_active
      return bool(_tqdm_active     )







def 				lowerCAmelCase_	(     ):
      global _tqdm_active
      _A							:     Optional[int]			=						True
      hf_hub_utils.enable_progress_bars()







def 				lowerCAmelCase_	(     ):
      global _tqdm_active
      _A							:     List[str]			=						False
      hf_hub_utils.disable_progress_bars()







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,
)


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       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"),
    ]
)

_snake_case										=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 				lowerCAmelCase_	(     snake_case_     ):
      for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _A							:     List[str]			=						model_type_to_module_name(snake_case_     )

                  _A							:     List[Any]			=						importlib.import_module(f'''.{module_name}''',"""transformers.models"""     )
                  try:
                        return getattr(snake_case_,snake_case_     )
                  except AttributeError:
                        continue

      for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
            if getattr(snake_case_,"""__name__""",snake_case_     ) == 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.
      _A							:     List[Any]			=						importlib.import_module("""transformers"""     )
      if hasattr(snake_case_,snake_case_     ):
            return getattr(snake_case_,snake_case_     )

      return None







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = None,snake_case_ = False,snake_case_ = False,snake_case_ = None,snake_case_ = None,snake_case_ = None,snake_case_ = False,**snake_case_,):
      _A							:     Optional[int]			=						get_file_from_repo(
          snake_case_,snake_case_,cache_dir=snake_case_,force_download=snake_case_,resume_download=snake_case_,proxies=snake_case_,use_auth_token=snake_case_,revision=snake_case_,local_files_only=snake_case_,)
      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(snake_case_,encoding="""utf-8"""     ) as reader:
            return json.load(snake_case_     )







class 						lowercase							:

     def __init__(  self				)						->							List[Any]:
           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(_a				)
     def 					a__	(  cls		,     _a		,     **_a				)						->							Any:
           _A							:     Tuple			=						kwargs.pop("""config"""		,     _a				)
           _A							:     Tuple			=						kwargs.pop("""trust_remote_code"""		,     _a				)
           _A							:     List[Any]			=						True

           _A			,   _A							:     Tuple			=						FeatureExtractionMixin.get_feature_extractor_dict(_a		,     **_a				)
           _A							:     Tuple			=						config_dict.get("""feature_extractor_type"""		,     _a				)
           _A							:     int			=						None
           if "AutoFeatureExtractor" in config_dict.get("""auto_map"""		,     {}				):
                 _A							:     Optional[int]			=						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(_a		,     _a				):
                       _A							:     int			=						AutoConfig.from_pretrained(_a		,     **_a				)
                 # It could be in `config.feature_extractor_type``
                 _A							:     Optional[int]			=						getattr(_a		,     """feature_extractor_type"""		,     _a				)
                 if hasattr(_a		,     """auto_map"""				) and "AutoFeatureExtractor" in config.auto_map:
                       _A							:     Tuple			=						config.auto_map["""AutoFeatureExtractor"""]

           if feature_extractor_class is not None:
                 _A							:     Optional[Any]			=						feature_extractor_class_from_name(_a				)

           _A							:     List[Any]			=						feature_extractor_auto_map is not None
           _A							:     Union[str, Any]			=						feature_extractor_class is not None or type(_a				) in FEATURE_EXTRACTOR_MAPPING
           _A							:     Optional[int]			=						resolve_trust_remote_code(
               _a		,     _a		,     _a		,     _a				)

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

           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__	(  _a		,     _a				)						->							Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(_a		,     _a				)

