






'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable



def     A__						(	A_    ,      A_		)     ->				List[Any]:
					_lowercase			= iter(__snake_case		)
					while True:
										_lowercase			= tuple(itertools.islice(__snake_case    ,      __snake_case		)		)
										if not chunk:
															return
										yield chunk



def     A__						(	A_		)     ->				List[Any]:
					_lowercase			= "".join([c.upper() for c in dirty if c in string.ascii_letters]		)
					_lowercase			= ""

					if len(__snake_case		) < 2:
										return dirty

					for i in range(len(__snake_case		) - 1		):
										clean += dirty[i]

										if dirty[i] == dirty[i + 1]:
															clean += "X"

					clean += dirty[-1]

					if len(__snake_case		) & 1:
										clean += "X"

					return clean



def     A__						(	A_		)     ->				Dict:
					# I and J are used interchangeably to allow
					# us to use a 5x5 table (25 letters)
					_lowercase			= "ABCDEFGHIKLMNOPQRSTUVWXYZ"
					# we're using a list instead of a '2d' array because it makes the math
					# for setting up the table and doing the actual encoding/decoding simpler
					_lowercase			= []

					# copy key chars into the table if they are in `alphabet` ignoring duplicates
					for char in key.upper():
										if char not in table and char in alphabet:
															table.append(__snake_case		)

    # fill the rest of the table in with the remaining alphabet chars
					for char in alphabet:
										if char not in table:
															table.append(__snake_case		)

					return table



def     A__						(	A_    ,      A_		)     ->				str:
					_lowercase			= generate_table(__snake_case		)
					_lowercase			= prepare_input(__snake_case		)
					_lowercase			= ""

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(__snake_case    ,      2		):
										_lowercase	,			_lowercase			= divmod(table.index(__snake_case		)    ,      5		)
										_lowercase	,			_lowercase			= divmod(table.index(__snake_case		)    ,      5		)

										if rowa == rowa:
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
										elif cola == cola:
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
										else:  # rectangle
															ciphertext += table[rowa * 5 + cola]
															ciphertext += table[rowa * 5 + cola]

					return ciphertext



def     A__						(	A_    ,      A_		)     ->				Union[str, Any]:
					_lowercase			= generate_table(__snake_case		)
					_lowercase			= ""

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(__snake_case    ,      2		):
										_lowercase	,			_lowercase			= divmod(table.index(__snake_case		)    ,      5		)
										_lowercase	,			_lowercase			= divmod(table.index(__snake_case		)    ,      5		)

										if rowa == rowa:
															plaintext += table[rowa * 5 + (cola - 1) % 5]
															plaintext += table[rowa * 5 + (cola - 1) % 5]
										elif cola == cola:
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
										else:  # rectangle
															plaintext += table[rowa * 5 + cola]
															plaintext += table[rowa * 5 + cola]

					return plaintext









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,
)


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

lowerCamelCase      :       Any    =						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'''),
    ]
)

lowerCamelCase      :       Optional[int]    =						_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:
               __lowerCAmelCase             =       model_type_to_module_name(__snake_case							)

               __lowerCAmelCase             =       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.
     __lowerCAmelCase             =       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       ,			):
     __lowerCAmelCase             =       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 				_UpperCamelCase      :


      def __init__(						self						)->						Union[str, 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(__UpperCamelCase						)
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    **__UpperCamelCase						)->						Optional[int]:
           __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 			__UpperCAmelCase		(						__UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(__UpperCamelCase       ,    __UpperCamelCase						)






