

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:
			a_     =			None


a_     =			logging.get_logger(__name__)


a_     =			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

a_     =			{
    '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',
    },
}

a_     =			{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
a_     =			['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 _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							=VOCAB_FILES_NAMES
			lowerCamelCase__							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__							=PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__							=['input_ids', 'attention_mask']
			lowerCamelCase__							=MBartTokenizer

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				AddedToken(a					,					lstrip=a					,					rstrip=a		) if isinstance(a					,					a		) else mask_token

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

				SCREAMING_SNAKE_CASE      :					Any    =				vocab_file
				SCREAMING_SNAKE_CASE      :					List[Any]    =				False if not self.vocab_file else True

				SCREAMING_SNAKE_CASE      :					List[str]    =				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      :					str    =				{
				    lang_code: self.convert_tokens_to_ids(a		) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				SCREAMING_SNAKE_CASE      :					Any    =				src_lang if src_lang is not None else "en_XX"
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.convert_tokens_to_ids(self._src_lang		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				tgt_lang
				self.set_src_lang_special_tokens(self._src_lang		)
			@property
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				str:

				"""simple docstring"""

				return self._src_lang
			@src_lang.setter
			def 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       str		)							->				None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				new_src_lang
				self.set_src_lang_special_tokens(self._src_lang		)
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       List[int]					,					a							:       Optional[List[int]] = None		)							->				List[int]:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       List[int]					,					a							:       Optional[List[int]] = None		)							->				List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[self.sep_token_id]
				SCREAMING_SNAKE_CASE      :					int    =				[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 							__UpperCamelCase       (	self							:       List[str]					,					a							:       Optional[Any]					,					a							:       str					,					a							:       Optional[str]					,					a							:       Optional[str]					,					**a							:       Optional[Any]		)							->				str:

				"""simple docstring"""

				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      :					List[str]    =				src_lang
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self(a					,					add_special_tokens=a					,					return_tensors=a					,					**a		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.convert_tokens_to_ids(a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tgt_lang_id
				return inputs
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       List[str]					,					a							:       str = "en_XX"					,					a							:       Optional[List[str]] = None					,					a							:       str = "ro_RO"					,					**a							:       Tuple					,					)							->				BatchEncoding:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				src_lang
				SCREAMING_SNAKE_CASE      :					Any    =				tgt_lang
				return super().prepare_seqaseq_batch(a					,					a					,					**a		)
			def 							__UpperCamelCase       (	self							:       str		)							->				Dict:

				"""simple docstring"""

				return self.set_src_lang_special_tokens(self.src_lang		)
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Optional[int]:

				"""simple docstring"""

				return self.set_tgt_lang_special_tokens(self.tgt_lang		)
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       Optional[Any]		)							->				None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.convert_tokens_to_ids(a		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				[]
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				[self.eos_token_id, self.cur_lang_code]

				SCREAMING_SNAKE_CASE      :					str    =				self.convert_ids_to_tokens(self.prefix_tokens		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.convert_ids_to_tokens(self.suffix_tokens		)

				SCREAMING_SNAKE_CASE      :					int    =				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 							__UpperCamelCase       (	self							:       str					,					a							:       str		)							->				None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.convert_tokens_to_ids(a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[]
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[self.eos_token_id, self.cur_lang_code]

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.convert_ids_to_tokens(self.prefix_tokens		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.convert_ids_to_tokens(self.suffix_tokens		)

				SCREAMING_SNAKE_CASE      :					str    =				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 							__UpperCamelCase       (	self							:       str					,					a							:       str					,					a							:       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(a		):
					logger.error(F"Vocabulary path ({save_directory}) should be a directory."		)
					return
				SCREAMING_SNAKE_CASE      :					str    =				os.path.join(
				    a					,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

				return (out_vocab_file,)
'''simple docstring'''





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : int			)    ->      str:


     '''simple docstring'''




     if a < 0 or b < 0:
          raise ValueError("the value of both inputs must be positive"			)
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]  # remove the leading "0b"
     UpperCAmelCase_           =   str(bin(snake_case_			)			)[2:]
     UpperCAmelCase_           =   max(len(snake_case_			)					,    len(snake_case_			)			)
     return "0b" + "".join(
         str(int("1" in (char_a, char_b)			)			)
         for char_a, char_b in zip(a_binary.zfill(snake_case_			)					,    b_binary.zfill(snake_case_			)			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




