


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase    		=       {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

__UpperCamelCase    		=       {
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[int]    =		VOCAB_FILES_NAMES
			_UpperCamelCase		:					Dict    =		PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase		:					Optional[Any]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase		:					Any    =		['input_ids', 'attention_mask']
			_UpperCamelCase		:					Any    =		RobertaTokenizer




			def __init__(							self      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case="replace"      ,						snake_case="<s>"      ,						snake_case="</s>"      ,						snake_case="</s>"      ,						snake_case="<s>"      ,						snake_case="<unk>"      ,						snake_case="<pad>"      ,						snake_case="<mask>"      ,						snake_case=False      ,						snake_case=True      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(
						    snake_case      ,						snake_case      ,						tokenizer_file=snake_case      ,						errors=snake_case      ,						bos_token=snake_case      ,						eos_token=snake_case      ,						sep_token=snake_case      ,						cls_token=snake_case      ,						unk_token=snake_case      ,						pad_token=snake_case      ,						mask_token=snake_case      ,						add_prefix_space=snake_case      ,						trim_offsets=snake_case      ,						**snake_case      ,						)

						UpperCamelCase__					=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
						if pre_tok_state.get("add_prefix_space"      ,						snake_case     ) != add_prefix_space:
									UpperCamelCase__					=       getattr(snake_case      ,						pre_tok_state.pop("type"     )     )
									UpperCamelCase__					=       add_prefix_space
									UpperCamelCase__					=       pre_tok_class(**snake_case     )

						UpperCamelCase__					=       add_prefix_space

						UpperCamelCase__					=       "post_processor"
						UpperCamelCase__					=       getattr(self.backend_tokenizer      ,						snake_case      ,						snake_case     )
						if tokenizer_component_instance:
									UpperCamelCase__					=       json.loads(tokenizer_component_instance.__getstate__()     )

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
												UpperCamelCase__					=       tuple(state["sep"]     )
									if "cls" in state:
												UpperCamelCase__					=       tuple(state["cls"]     )

									UpperCamelCase__					=       False

									if state.get("add_prefix_space"      ,						snake_case     ) != add_prefix_space:
												UpperCamelCase__					=       add_prefix_space
												UpperCamelCase__					=       True

									if state.get("trim_offsets"      ,						snake_case     ) != trim_offsets:
												UpperCamelCase__					=       trim_offsets
												UpperCamelCase__					=       True

									if changes_to_apply:
												UpperCamelCase__					=       getattr(snake_case      ,						state.pop("type"     )     )
												UpperCamelCase__					=       component_class(**snake_case     )
												setattr(self.backend_tokenizer      ,						snake_case      ,						snake_case     )




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if self._mask_token is None:
									if self.verbose:
												logger.error("Using mask_token, but it is not set yet."     )
									return None
						return str(self._mask_token     )




			@mask_token.setter
			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       AddedToken(snake_case      ,						lstrip=snake_case      ,						rstrip=snake_case     ) if isinstance(snake_case      ,						snake_case     ) else value
						UpperCamelCase__					=       value




			def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       kwargs.get("is_split_into_words"      ,						snake_case     )
						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*snake_case      ,						**snake_case     )




			def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       kwargs.get("is_split_into_words"      ,						snake_case     )

						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*snake_case      ,						**snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       self._tokenizer.model.save(snake_case      ,						name=snake_case     )
						return tuple(snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case=None     ):

						'''simple docstring'''


						UpperCamelCase__					=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
									return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       [self.sep_token_id]
						UpperCamelCase__					=       [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]






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property







@unittest.skipUnless(os.path.exists(UpperCAmelCase      )					,	'Tatoeba directory does not exist.'      )
class        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       tempfile.mkdtemp()
						return TatoebaConverter(save_dir=snake_case     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.resolver.convert_models(["heb-eng"]     )







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.resolver.write_model_card("opus-mt-he-en"      ,						dry_run=snake_case     )
						assert mmeta["long_pair"] == "heb-eng"


