


'''simple docstring'''

import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       Union[str, Any]            =       ElectraTokenizer
 _lowerCamelCase			:       Dict            =       ElectraTokenizerFast
 _lowerCamelCase			:       Optional[Any]            =       True
 _lowerCamelCase			:       str            =       True
 _lowerCamelCase			:       Union[str, Any]            =       filter_non_english

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    super().setUp()

    UpperCamelCase__			    =    [
        "[UNK]",
        "[CLS]",
        "[SEP]",
        "[PAD]",
        "[MASK]",
        "want",
        "##want",
        "##ed",
        "wa",
        "un",
        "runn",
        "##ing",
        ",",
        "low",
        "lowest",
    ]
    UpperCamelCase__			    =    os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES["vocab_file"]       )
    with open(self.vocab_file,       "w",       encoding="utf-8"       ) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]       )       )

 def 		lowercase_    (			self			:						str,       a_			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    "UNwant\u00E9d,running"
    UpperCamelCase__			    =    "unwanted, running"
    return input_text, output_text

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.tokenizer_class(self.vocab_file       )

    UpperCamelCase__			    =    tokenizer.tokenize("UNwant\u00E9d,running"       )
    self.assertListEqual(a_,       ["un", "##want", "##ed", ",", "runn", "##ing"]       )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(a_       ),       [9, 6, 7, 12, 10, 11]       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    if not self.test_rust_tokenizer:
       return

    UpperCamelCase__			    =    self.get_tokenizer()
    UpperCamelCase__			    =    self.get_rust_tokenizer()

    UpperCamelCase__			    =    "UNwant\u00E9d,running"

    UpperCamelCase__			    =    tokenizer.tokenize(a_       )
    UpperCamelCase__			    =    rust_tokenizer.tokenize(a_       )
    self.assertListEqual(a_,       a_       )

    UpperCamelCase__			    =    tokenizer.encode(a_,       add_special_tokens=a_       )
    UpperCamelCase__			    =    rust_tokenizer.encode(a_,       add_special_tokens=a_       )
    self.assertListEqual(a_,       a_       )

    UpperCamelCase__			    =    self.get_rust_tokenizer()
    UpperCamelCase__			    =    tokenizer.encode(a_       )
    UpperCamelCase__			    =    rust_tokenizer.encode(a_       )
    self.assertListEqual(a_,       a_       )

    # With lower casing
    UpperCamelCase__			    =    self.get_tokenizer(do_lower_case=a_       )
    UpperCamelCase__			    =    self.get_rust_tokenizer(do_lower_case=a_       )

    UpperCamelCase__			    =    "UNwant\u00E9d,running"

    UpperCamelCase__			    =    tokenizer.tokenize(a_       )
    UpperCamelCase__			    =    rust_tokenizer.tokenize(a_       )
    self.assertListEqual(a_,       a_       )

    UpperCamelCase__			    =    tokenizer.encode(a_,       add_special_tokens=a_       )
    UpperCamelCase__			    =    rust_tokenizer.encode(a_,       add_special_tokens=a_       )
    self.assertListEqual(a_,       a_       )

    UpperCamelCase__			    =    self.get_rust_tokenizer()
    UpperCamelCase__			    =    tokenizer.encode(a_       )
    UpperCamelCase__			    =    rust_tokenizer.encode(a_       )
    self.assertListEqual(a_,       a_       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer()

    self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"       ),       ["ah", "\u535A", "\u63A8", "zz"]       )

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "       ),       ["hello", "!", "how", "are", "you", "?"]       )
    self.assertListEqual(tokenizer.tokenize("H\u00E9llo"       ),       ["hello"]       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_,       strip_accents=a_       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "       ),       ["hällo", "!", "how", "are", "you", "?"]       )
    self.assertListEqual(tokenizer.tokenize("H\u00E9llo"       ),       ["h\u00E9llo"]       )

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_,       strip_accents=a_       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "       ),       ["hallo", "!", "how", "are", "you", "?"]       )
    self.assertListEqual(tokenizer.tokenize("H\u00E9llo"       ),       ["hello"]       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "       ),       ["hallo", "!", "how", "are", "you", "?"]       )
    self.assertListEqual(tokenizer.tokenize("H\u00E9llo"       ),       ["hello"]       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "       ),       ["HeLLo", "!", "how", "Are", "yoU", "?"]       )

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_,       strip_accents=a_       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "       ),       ["HäLLo", "!", "how", "Are", "yoU", "?"]       )

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_,       strip_accents=a_       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "       ),       ["HaLLo", "!", "how", "Are", "yoU", "?"]       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    BasicTokenizer(do_lower_case=a_,       never_split=["[UNK]"]       )

    self.assertListEqual(
        tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"       ),       ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

    UpperCamelCase__			    =    {}
    for i, token in enumerate(a_       ):
       UpperCamelCase__			    =    i
    UpperCamelCase__			    =    WordpieceTokenizer(vocab=a_,       unk_token="[UNK]"       )

    self.assertListEqual(tokenizer.tokenize(""       ),       []       )

    self.assertListEqual(tokenizer.tokenize("unwanted running"       ),       ["un", "##want", "##ed", "runn", "##ing"]       )

    self.assertListEqual(tokenizer.tokenize("unwantedX running"       ),       ["[UNK]", "runn", "##ing"]       )

 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    self.assertTrue(_is_whitespace(" "       )       )
    self.assertTrue(_is_whitespace("\t"       )       )
    self.assertTrue(_is_whitespace("\r"       )       )
    self.assertTrue(_is_whitespace("\n"       )       )
    self.assertTrue(_is_whitespace("\u00A0"       )       )

    self.assertFalse(_is_whitespace("A"       )       )
    self.assertFalse(_is_whitespace("-"       )       )

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    self.assertTrue(_is_control("\u0005"       )       )

    self.assertFalse(_is_control("A"       )       )
    self.assertFalse(_is_control(" "       )       )
    self.assertFalse(_is_control("\t"       )       )
    self.assertFalse(_is_control("\r"       )       )

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    self.assertTrue(_is_punctuation("-"       )       )
    self.assertTrue(_is_punctuation("$"       )       )
    self.assertTrue(_is_punctuation("`"       )       )
    self.assertTrue(_is_punctuation("."       )       )

    self.assertFalse(_is_punctuation("A"       )       )
    self.assertFalse(_is_punctuation(" "       )       )

 def 		lowercase_    (			self			:						List[Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.get_tokenizer()
    UpperCamelCase__			    =    self.get_rust_tokenizer()

    # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
    self.assertListEqual([tokenizer.tokenize(a_       ) for t in ["Test", "\xad", "test"]],       [["[UNK]"], [], ["[UNK]"]]       )

    self.assertListEqual(
        [rust_tokenizer.tokenize(a_       ) for t in ["Test", "\xad", "test"]],       [["[UNK]"], [], ["[UNK]"]]       )

 @slow
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.tokenizer_class.from_pretrained("google/electra-base-discriminator"       )

    UpperCamelCase__			    =    tokenizer.encode("sequence builders",       add_special_tokens=a_       )
    UpperCamelCase__			    =    tokenizer.encode("multi-sequence build",       add_special_tokens=a_       )

    UpperCamelCase__			    =    tokenizer.build_inputs_with_special_tokens(a_       )
    UpperCamelCase__			    =    tokenizer.build_inputs_with_special_tokens(a_,       a_       )

    assert encoded_sentence == [101] + text + [102]
    assert encoded_pair == [101] + text + [102] + text_a + [102]

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
          UpperCamelCase__			    =    self.rust_tokenizer_class.from_pretrained(a_,       **a_       )

          UpperCamelCase__			    =    f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
          UpperCamelCase__			    =    tokenizer_r.encode_plus(
              a_,       return_attention_mask=a_,       return_token_type_ids=a_,       return_offsets_mapping=a_,       add_special_tokens=a_,       )

          UpperCamelCase__			    =    tokenizer_r.do_lower_case if hasattr(a_,       "do_lower_case"       ) else False
          UpperCamelCase__			    =    (
              [
                  ((0, 0), tokenizer_r.cls_token),
                  ((0, 1), "A"),
                  ((1, 2), ","),
                  ((3, 5), "na"),
                  ((5, 6), "##ï"),
                  ((6, 8), "##ve"),
                  ((9, 15), tokenizer_r.mask_token),
                  ((16, 21), "Allen"),
                  ((21, 23), "##NL"),
                  ((23, 24), "##P"),
                  ((25, 33), "sentence"),
                  ((33, 34), "."),
                  ((0, 0), tokenizer_r.sep_token),
              ]
              if not do_lower_case
              else [
                  ((0, 0), tokenizer_r.cls_token),
                  ((0, 1), "a"),
                  ((1, 2), ","),
                  ((3, 8), "naive"),
                  ((9, 15), tokenizer_r.mask_token),
                  ((16, 21), "allen"),
                  ((21, 23), "##nl"),
                  ((23, 24), "##p"),
                  ((25, 33), "sentence"),
                  ((33, 34), "."),
                  ((0, 0), tokenizer_r.sep_token),
              ]
          )

          self.assertEqual(
              [e[1] for e in expected_results],       tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]       )       )
          self.assertEqual([e[0] for e in expected_results],       tokens["offset_mapping"]       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    ["的", "人", "有"]
    UpperCamelCase__			    =    "".join(a_       )
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
          UpperCamelCase__			    =    True
          UpperCamelCase__			    =    self.tokenizer_class.from_pretrained(a_,       **a_       )
          UpperCamelCase__			    =    self.rust_tokenizer_class.from_pretrained(a_,       **a_       )

          UpperCamelCase__			    =    tokenizer_p.encode(a_,       add_special_tokens=a_       )
          UpperCamelCase__			    =    tokenizer_r.encode(a_,       add_special_tokens=a_       )

          UpperCamelCase__			    =    tokenizer_r.convert_ids_to_tokens(a_       )
          UpperCamelCase__			    =    tokenizer_p.convert_ids_to_tokens(a_       )

          # it is expected that each Chinese character is not preceded by "##"
          self.assertListEqual(a_,       a_       )
          self.assertListEqual(a_,       a_       )

          UpperCamelCase__			    =    False
          UpperCamelCase__			    =    self.rust_tokenizer_class.from_pretrained(a_,       **a_       )
          UpperCamelCase__			    =    self.tokenizer_class.from_pretrained(a_,       **a_       )

          UpperCamelCase__			    =    tokenizer_r.encode(a_,       add_special_tokens=a_       )
          UpperCamelCase__			    =    tokenizer_p.encode(a_,       add_special_tokens=a_       )

          UpperCamelCase__			    =    tokenizer_r.convert_ids_to_tokens(a_       )
          UpperCamelCase__			    =    tokenizer_p.convert_ids_to_tokens(a_       )

          # it is expected that only the first Chinese character is not preceded by "##".
          UpperCamelCase__			    =    [
              f'##{token}' if idx != 0 else token for idx, token in enumerate(a_       )
          ]
          self.assertListEqual(a_,       a_       )
          self.assertListEqual(a_,       a_       )




"""simple docstring"""


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_								=			logging.get_logger(__name__)

snake_case_								=			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_								=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)

def 					_lowerCAmelCase (  lowercase_		):
						for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
												if class_name in processors:
																		UpperCAmelCase			 =					model_type_to_module_name(lowercase_		)

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

						for processor in PROCESSOR_MAPPING._extra_content.values():
												if getattr(lowercase_						,      '__name__'						,      lowercase_		) == 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.
						UpperCAmelCase			 =					importlib.import_module('transformers'		)
						if hasattr(lowercase_						,      lowercase_		):
												return getattr(lowercase_						,      lowercase_		)

						return None







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Optional[Any]   )							->     str:
												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(lowercase_   )
						def       UpperCAmelCase__	(   cls					:Optional[int]  ,    lowercase_					:Tuple  ,    **lowercase_					:int   )							->     Optional[int]:
												UpperCAmelCase			 =					kwargs.pop('config'  ,    lowercase_   )
												UpperCAmelCase			 =					kwargs.pop('trust_remote_code'  ,    lowercase_   )
												UpperCAmelCase			 =					True

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

												# First, let's see if we have a preprocessor config.
												# Filter the kwargs for `get_file_from_repo`.
												UpperCAmelCase			 =					{
												    key: kwargs[key] for key in inspect.signature(lowercase_   ).parameters.keys() if key in kwargs
												}
												# Let's start by checking whether the processor class is saved in an image processor
												UpperCAmelCase			 =					get_file_from_repo(
												    lowercase_  ,    lowercase_  ,    **lowercase_   )
												if preprocessor_config_file is not None:
																		UpperCAmelCase ,       UpperCAmelCase			 =					ImageProcessingMixin.get_image_processor_dict(lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																		if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																								UpperCAmelCase			 =					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:
																		UpperCAmelCase ,       UpperCAmelCase			 =					FeatureExtractionMixin.get_feature_extractor_dict(lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																		if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																								UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

												if processor_class is None:
																		# Next, let's check whether the processor class is saved in a tokenizer
																		UpperCAmelCase			 =					get_file_from_repo(
																		    lowercase_  ,    lowercase_  ,    **lowercase_   )
																		if tokenizer_config_file is not None:
																								with open(lowercase_  ,    encoding='utf-8'   ) as reader:
																														UpperCAmelCase			 =					json.load(lowercase_   )

																								UpperCAmelCase			 =					config_dict.get('processor_class'  ,    lowercase_   )
																								if "AutoProcessor" in config_dict.get('auto_map'  ,    {}   ):
																														UpperCAmelCase			 =					config_dict['auto_map']['AutoProcessor']

												if processor_class is None:
																		# Otherwise, load config, if it can be loaded.
																		if not isinstance(lowercase_  ,    lowercase_   ):
																								UpperCAmelCase			 =					AutoConfig.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )

																		# And check if the config contains the processor class.
																		UpperCAmelCase			 =					getattr(lowercase_  ,    'processor_class'  ,    lowercase_   )
																		if hasattr(lowercase_  ,    'auto_map'   ) and "AutoProcessor" in config.auto_map:
																								UpperCAmelCase			 =					config.auto_map['AutoProcessor']

												if processor_class is not None:
																		UpperCAmelCase			 =					processor_class_from_name(lowercase_   )

												UpperCAmelCase			 =					processor_auto_map is not None
												UpperCAmelCase			 =					processor_class is not None or type(lowercase_   ) in PROCESSOR_MAPPING
												UpperCAmelCase			 =					resolve_trust_remote_code(
												    lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )

												if has_remote_code and trust_remote_code:
																		UpperCAmelCase			 =					get_class_from_dynamic_module(
																		    lowercase_  ,    lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					kwargs.pop('code_revision'  ,    lowercase_   )
																		if os.path.isdir(lowercase_   ):
																								processor_class.register_for_auto_class()
																		return processor_class.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												elif processor_class is not None:
																		return processor_class.from_pretrained(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												# Last try: we use the PROCESSOR_MAPPING.
												elif type(lowercase_   ) in PROCESSOR_MAPPING:
																		return PROCESSOR_MAPPING[type(lowercase_   )].from_pretrained(lowercase_  ,    **lowercase_   )

												# 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(
																		    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
												except Exception:
																		try:
																								return AutoImageProcessor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		except Exception:
																								pass

																		try:
																								return AutoFeatureExtractor.from_pretrained(
																								    lowercase_  ,    trust_remote_code=lowercase_  ,    **lowercase_   )
																		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       UpperCAmelCase__	(   lowercase_					:int  ,    lowercase_					:Union[str, Any]   )							->     Tuple:
												PROCESSOR_MAPPING.register(lowercase_  ,    lowercase_   )





