





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

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


if is_sentencepiece_available():
					from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
					UpperCAmelCase_   				=  None


UpperCAmelCase_   				=  logging.get_logger(__name__)

UpperCAmelCase_   				=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_   				=  {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase_   				=  {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						VOCAB_FILES_NAMES
					lowerCAmelCase_				=						PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				=						['''input_ids''', '''attention_mask''']
					lowerCAmelCase_				=						XLMRobertaTokenizer




					def __init__(		self					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    lowerCAmelCase_="<s>"					,    lowerCAmelCase_="</s>"					,    lowerCAmelCase_="</s>"					,    lowerCAmelCase_="<s>"					,    lowerCAmelCase_="<unk>"					,    lowerCAmelCase_="<pad>"					,    lowerCAmelCase_="<mask>"					,    **lowerCAmelCase_					,    )		->				Dict:
										# Mask token behave like a normal word, i.e. include the space before it
										_snake_case						=					AddedToken(lowerCAmelCase_					,    lstrip=lowerCAmelCase_					,    rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) else mask_token

										super().__init__(
										    lowerCAmelCase_					,    tokenizer_file=lowerCAmelCase_					,    bos_token=lowerCAmelCase_					,    eos_token=lowerCAmelCase_					,    sep_token=lowerCAmelCase_					,    cls_token=lowerCAmelCase_					,    unk_token=lowerCAmelCase_					,    pad_token=lowerCAmelCase_					,    mask_token=lowerCAmelCase_					,    **lowerCAmelCase_					,    )

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




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = None	)		->				List[int]:

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




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = None	)		->				List[int]:
										_snake_case						=					[self.sep_token_id]
										_snake_case						=					[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 + sep + token_ids_a + sep	) * [0]






					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = 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
										_snake_case						=					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, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
					import torch
class       UpperCamelCase_		(	unittest.TestCase				,	_lowerCamelCase       ):




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					load_tool('speech-to-text'	)
										self.tool.setup()




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.tool(torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)






					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					self.tool(audio=torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)



