import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
     from .tokenization_deberta_va import DebertaVaTokenizer
else:
     snake_case__						      =			None

snake_case__						      =			logging.get_logger(__name__)

snake_case__						      =			{"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}

snake_case__						      =			{
    "vocab_file": {
        "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
        "microsoft/deberta-v2-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
        ),
        "microsoft/deberta-v2-xxlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
        ),
    }
}

snake_case__						      =			{
    "microsoft/deberta-v2-xlarge": 512,
    "microsoft/deberta-v2-xxlarge": 512,
    "microsoft/deberta-v2-xlarge-mnli": 512,
    "microsoft/deberta-v2-xxlarge-mnli": 512,
}

snake_case__						      =			{
    "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
    "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
    "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
}



class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	VOCAB_FILES_NAMES
     A_					    =	PRETRAINED_VOCAB_FILES_MAP
     A_					    =	PRETRAINED_INIT_CONFIGURATION
     A_					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_					    =	DebertaVaTokenizer



     def __init__(       self					,					A_=None					,					A_=None					,					A_=False					,					A_=False					,					A_="[CLS]"					,					A_="[SEP]"					,					A_="[UNK]"					,					A_="[SEP]"					,					A_="[PAD]"					,					A_="[CLS]"					,					A_="[MASK]"					,					**A_					,					)			->					None:






           """simple docstring"""
           super().__init__(
               A_					,					tokenizer_file=A_					,					do_lower_case=A_					,					bos_token=A_					,					eos_token=A_					,					unk_token=A_					,					sep_token=A_					,					pad_token=A_					,					cls_token=A_					,					mask_token=A_					,					split_by_punct=A_					,					**A_					,					)

           _lowerCamelCase           =					do_lower_case
           _lowerCamelCase           =					split_by_punct
           _lowerCamelCase           =					vocab_file
           _lowerCamelCase           =					False if not self.vocab_file else True



     def 		UpperCamelCase_    (       self					,					A_					,					A_=None    )			->					Optional[int]:






           """simple docstring"""

           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _lowerCamelCase           =					[self.cls_token_id]
           _lowerCamelCase           =					[self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep



     def 		UpperCamelCase_    (       self					,					A_					,					A_=None					,					A_=False    )			->					Dict:






           """simple docstring"""

           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=A_					,					token_ids_a=A_					,					already_has_special_tokens=A_    )

           if token_ids_a is not None:
                 return [1] + ([0] * len(A_    )) + [1] + ([0] * len(A_    )) + [1]
           return [1] + ([0] * len(A_    )) + [1]



     def 		UpperCamelCase_    (       self					,					A_					,					A_=None    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					[self.sep_token_id]
           _lowerCamelCase           =					[self.cls_token_id]
           if token_ids_a is None:
                 return len(cls + token_ids_a + sep    ) * [0]
           return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]





     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					Tuple[str]:






           """simple docstring"""
           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                     '''tokenizer.'''    )

           if not os.path.isdir(A_    ):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
                 return
           _lowerCamelCase           =					os.path.join(
               A_					,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

           if os.path.abspath(self.vocab_file    ) != os.path.abspath(A_    ):
                 copyfile(self.vocab_file					,					A_    )

           return (out_vocab_file,)






'''simple docstring'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
					]

					for test_file in test_files:
										snake_case_  :							Any            =	get_tester_classes(test_file)
										for tester_class in tester_classes:
															# A few tester classes don't have `parent` parameter in `__init__`.
															# TODO: deal this better
															try:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	5
																														if target is not None and v > target:
																																			failures.append(
																																			    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																			    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																			    f''' than {target}.'''
																																			)

					if len(failures) > 0:
										raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))





