
'''simple docstring'''







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

from tokenizers import processors

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


if is_sentencepiece_available():
						from .tokenization_mbart import MBartTokenizer
else:
						_snake_case				:   str     =				None


_snake_case				:   Any     =				logging.get_logger(__name__)


_snake_case				:   List[str]     =				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_snake_case				:   Any     =				{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

_snake_case				:   Tuple     =				{
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
_snake_case				:   Any     =				["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					str       =					VOCAB_FILES_NAMES
		a							:					Any       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a							:					int       =					PRETRAINED_VOCAB_FILES_MAP
		a							:					Dict       =					["""input_ids""", """attention_mask"""]
		a							:					Union[str, Any]       =					MBartTokenizer

		a							:					List[int]       =					[]
		a							:					List[int]       =					[]




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

								super().__init__(
								    vocab_file=UpperCamelCase					,						tokenizer_file=UpperCamelCase					,						bos_token=UpperCamelCase					,						eos_token=UpperCamelCase					,						sep_token=UpperCamelCase					,						cls_token=UpperCamelCase					,						unk_token=UpperCamelCase					,						pad_token=UpperCamelCase					,						mask_token=UpperCamelCase					,						src_lang=UpperCamelCase					,						tgt_lang=UpperCamelCase					,						additional_special_tokens=UpperCamelCase					,						**UpperCamelCase					,						)

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

								_SCREAMING_SNAKE_CASE				= FAIRSEQ_LANGUAGE_CODES.copy()

								if additional_special_tokens is not None:
														# Only add those special tokens if they are not already there.
														_additional_special_tokens.extend(
														    [t for t in additional_special_tokens if t not in _additional_special_tokens]      )

								self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}      )
								_SCREAMING_SNAKE_CASE				= {
								    lang_code: self.convert_tokens_to_ids(UpperCamelCase      ) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								_SCREAMING_SNAKE_CASE				= src_lang if src_lang is not None else "en_XX"
								_SCREAMING_SNAKE_CASE				= self.convert_tokens_to_ids(self._src_lang      )
								_SCREAMING_SNAKE_CASE				= tgt_lang
								self.set_src_lang_special_tokens(self._src_lang      )




		@property
		def     lowercase       (				self      ):
								return self._src_lang




		@src_lang.setter
		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= new_src_lang
								self.set_src_lang_special_tokens(self._src_lang      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								if token_ids_a is None:
														return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								_SCREAMING_SNAKE_CASE				= [self.sep_token_id]
								_SCREAMING_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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase      ):
								if src_lang is None or tgt_lang is None:
														raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"      )
								_SCREAMING_SNAKE_CASE				= src_lang
								_SCREAMING_SNAKE_CASE				= self(UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.convert_tokens_to_ids(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tgt_lang_id
								return inputs




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = "en_XX"					,						UpperCamelCase = None					,						UpperCamelCase = "ro_RO"					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= src_lang
								_SCREAMING_SNAKE_CASE				= tgt_lang
								return super().prepare_seqaseq_batch(UpperCamelCase					,						UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self      ):
								return self.set_src_lang_special_tokens(self.src_lang      )




		def     lowercase       (				self      ):
								return self.set_tgt_lang_special_tokens(self.tgt_lang      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.convert_tokens_to_ids(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= []
								_SCREAMING_SNAKE_CASE				= [self.eos_token_id, self.cur_lang_code]

								_SCREAMING_SNAKE_CASE				= self.convert_ids_to_tokens(self.prefix_tokens      )
								_SCREAMING_SNAKE_CASE				= self.convert_ids_to_tokens(self.suffix_tokens      )

								_SCREAMING_SNAKE_CASE				= processors.TemplateProcessing(
								    single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,						self.prefix_tokens + self.suffix_tokens      )      )					,						)




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.convert_tokens_to_ids(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= []
								_SCREAMING_SNAKE_CASE				= [self.eos_token_id, self.cur_lang_code]

								_SCREAMING_SNAKE_CASE				= self.convert_ids_to_tokens(self.prefix_tokens      )
								_SCREAMING_SNAKE_CASE				= self.convert_ids_to_tokens(self.suffix_tokens      )

								_SCREAMING_SNAKE_CASE				= processors.TemplateProcessing(
								    single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,						pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,						self.prefix_tokens + self.suffix_tokens      )      )					,						)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								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(UpperCamelCase      ):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory.'      )
														return
								_SCREAMING_SNAKE_CASE				= os.path.join(
								    UpperCamelCase					,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

								return (out_vocab_file,)

'''simple docstring'''







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Dict       =					DistilBertTokenizer
		a							:					Tuple       =					DistilBertTokenizerFast
		a							:					List[str]       =					True




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DistilBertTokenizer.from_pretrained("distilbert-base-uncased"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						add_special_tokens=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]