




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

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


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		__lowerCAmelCase		:    Any	      =		None


__lowerCAmelCase		:    Optional[int]	      =		logging.get_logger(__name__)


__lowerCAmelCase		:    Dict	      =		'▁'

__lowerCAmelCase		:    Optional[int]	      =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase		:    List[Any]	      =		{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

__lowerCAmelCase		:    Optional[int]	      =		{
    'google/reformer-crime-and-punishment': 52_4288,
}




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_	:							Dict										= PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_	:							Union[str, Any]										= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_	:							int										= ReformerTokenizer





				def __init__(				self    :					Any ,				__lowerCamelCase    :					Tuple=None ,				__lowerCamelCase    :					List[Any]=None ,				__lowerCamelCase    :					Optional[Any]="</s>" ,				__lowerCamelCase    :					Optional[int]="<unk>" ,				__lowerCamelCase    :					List[str]=[] ,				**__lowerCamelCase    :					Dict ,				) ->					int:
							super().__init__(
							    __lowerCamelCase ,				tokenizer_file=__lowerCamelCase ,				eos_token=__lowerCamelCase ,				unk_token=__lowerCamelCase ,				additional_special_tokens=__lowerCamelCase ,				**__lowerCamelCase ,				)

							a   =      vocab_file
							a   =      False if not self.vocab_file else True





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[str] = None							) ->					Tuple[str]:
							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(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a   =      os.path.join(
							    __lowerCamelCase ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

							return (out_vocab_file,)












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






