

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			:					int						=			logging.get_logger(__name__)

__A			:					Optional[int]						=			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			:					Optional[int]						=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def 	__a     (      A__			:  str       ):
    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            SCREAMING_SNAKE_CASE										=   model_type_to_module_name(A__       )

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

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(A__			,  "__name__"			,  A__       ) == 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.
    SCREAMING_SNAKE_CASE										=   importlib.import_module("transformers"       )
    if hasattr(A__			,  A__       ):
        return getattr(A__			,  A__       )

    return None





def 	__a     (      A__			:  Union[str, os.PathLike]			,  A__			:  Optional[Union[str, os.PathLike]] = None			,  A__			:  bool = False			,  A__			:  bool = False			,  A__			:  Optional[Dict[str, str]] = None			,  A__			:  Optional[Union[bool, str]] = None			,  A__			:  Optional[str] = None			,  A__			:  bool = False			,  **A__			:  Dict			,  ):
    SCREAMING_SNAKE_CASE										=   get_file_from_repo(
        A__			,  A__			,  cache_dir=A__			,  force_download=A__			,  resume_download=A__			,  proxies=A__			,  use_auth_token=A__			,  revision=A__			,  local_files_only=A__			,  )
    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(A__			,  encoding="utf-8"       ) as reader:
        return json.load(A__       )





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Tuple			):
       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(__lowerCamelCase			)
   def      _snake_case		(       cls					:			Optional[Any]     , __lowerCamelCase					:			List[str]     , **__lowerCamelCase					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   kwargs.pop("config"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("trust_remote_code"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   True

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

       if feature_extractor_class is not None:
           SCREAMING_SNAKE_CASE										=   feature_extractor_class_from_name(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   feature_extractor_auto_map is not None
       SCREAMING_SNAKE_CASE										=   feature_extractor_class is not None or type(__lowerCamelCase			) in FEATURE_EXTRACTOR_MAPPING
       SCREAMING_SNAKE_CASE										=   resolve_trust_remote_code(
           __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

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

       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      _snake_case		(       __lowerCamelCase					:			int     , __lowerCamelCase					:			Dict			):
       FEATURE_EXTRACTOR_MAPPING.register(__lowerCamelCase     , __lowerCamelCase			)


import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch





@require_torch
@require_optimum
@slow
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tokenizer("This is me"     , return_tensors="pt"			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       SCREAMING_SNAKE_CASE										=   model.generate(**__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()

       self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

       with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

           self.assertFalse(
               any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

           SCREAMING_SNAKE_CASE										=   model_reloaded.generate(**__lowerCamelCase			)
           self.assertTrue(torch.allclose(__lowerCamelCase     , __lowerCamelCase			)			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-random-t5"
       SCREAMING_SNAKE_CASE										=   AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__lowerCamelCase			):
               model.save_pretrained(__lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   model.reverse_bettertransformer()
           model.save_pretrained(__lowerCamelCase			)