


'''simple docstring'''


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

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


if is_sentencepiece_available():
						from .tokenization_pegasus import PegasusTokenizer
else:
						lowerCamelCase__					=   None


lowerCamelCase__					=   logging.get_logger(__name__)


lowerCamelCase__					=   '▁'

lowerCamelCase__					=   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					=   {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

lowerCamelCase__					=   {
    'google/pegasus-xsum': 512,
}






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		int				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		List[Any]				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		List[Any]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Optional[int]				      =   PegasusTokenizer
			lowerCAmelCase       :		Optional[int]				      =   ["input_ids", "attention_mask"]




			def __init__(						self     :   List[str]		,     lowerCamelCase__     :   Dict=None		,     lowerCamelCase__     :   Dict=None		,     lowerCamelCase__     :   Union[str, Any]="<pad>"		,     lowerCamelCase__     :   Union[str, Any]="</s>"		,     lowerCamelCase__     :   str="<unk>"		,     lowerCamelCase__     :   str="<mask_2>"		,     lowerCamelCase__     :   Optional[Any]="<mask_1>"		,     lowerCamelCase__     :   Any=None		,     lowerCamelCase__     :   Optional[Any]=1_03		,     **lowerCamelCase__     :   Optional[int]		,     )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					offset

					if additional_special_tokens is not None:
							if not isinstance(lowerCamelCase__		,     lowerCamelCase__       ):
									raise TypeError(
									    F"""additional_special_tokens should be of type {type(lowerCamelCase__       )}, but is"""
									    F""" {type(lowerCamelCase__       )}"""       )

							_UpperCAmelCase						:				Optional[int]	    =					(
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    F"""<unk_{i}>""" for i in range(len(lowerCamelCase__       )		,     self.offset - 1       )
							]

							if len(set(lowerCamelCase__       )       ) != len(lowerCamelCase__       ):
									raise ValueError(
									    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
									    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
							_UpperCAmelCase						:				Union[str, Any]	    =					additional_special_tokens_extended
					else:
							_UpperCAmelCase						:				int	    =					[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [F"""<unk_{i}>""" for i in range(2		,     self.offset       )]

					super().__init__(
					    lowerCamelCase__		,     tokenizer_file=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     eos_token=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     mask_token_sent=lowerCamelCase__		,     offset=lowerCamelCase__		,     additional_special_tokens=lowerCamelCase__		,     **lowerCamelCase__		,     )
					_UpperCAmelCase						:				Optional[int]	    =					vocab_file
					_UpperCAmelCase						:				Dict	    =					False if not self.vocab_file else True




			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					set(self.all_special_ids       )  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
							raise ValueError(
							    "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
							    F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

					return [1 if x in all_special_ids else 0 for x in seq]




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   List		,     lowerCamelCase__     :   Optional[List] = None		,     lowerCamelCase__     :   bool = False       )      ->List[int]:



					'''simple docstring'''







					if already_has_special_tokens:
							return self._special_token_mask(lowerCamelCase__       )
					elif token_ids_a is None:
							return self._special_token_mask(lowerCamelCase__       ) + [1]
					else:
							return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   Optional[int]=None       )      ->List[int]:



					'''simple docstring'''







					if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]






			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = 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(lowerCamelCase__       ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
							return
					_UpperCAmelCase						:				int	    =					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,)







'''simple docstring'''


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		):




			@cached_property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					tempfile.mkdtemp()
					return TatoebaConverter(save_dir=lowerCamelCase__       )




			@slow
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[str]:



					'''simple docstring'''







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






			@slow
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.resolver.write_model_card("opus-mt-he-en"		,     dry_run=lowerCamelCase__       )
					assert mmeta["long_pair"] == "heb-eng"



