

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_mbartaa import MBartaaTokenizer
else:
					SCREAMING_SNAKE_CASE_						=					None


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE_						=					{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE_						=					{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE_						=					['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', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']

class  a     (		UpperCAmelCase ):
						_lowercase			  =	VOCAB_FILES_NAMES
						_lowercase			  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase			  =	PRETRAINED_VOCAB_FILES_MAP
						_lowercase			  =	["input_ids", "attention_mask"]
						_lowercase			  =	MBartaaTokenizer

						_lowercase			  =	[]
						_lowercase			  =	[]


						def __init__(				self  ,    A_=None  ,    A_=None  ,    A_=None  ,    A_=None  ,    A_="</s>"  ,    A_="</s>"  ,    A_="<s>"  ,    A_="<unk>"  ,    A_="<pad>"  ,    A_="<mask>"  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			AddedToken(A_  ,    lstrip=A_  ,    rstrip=A_		) if isinstance(A_  ,    A_		) else mask_token

										_UpperCAmelCase				:						List[Any]       						=			kwargs.get("additional_special_tokens"  ,    []		)
										kwargs["additional_special_tokens"] += [
										    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    A_  ,    src_lang=A_  ,    tgt_lang=A_  ,    tokenizer_file=A_  ,    eos_token=A_  ,    sep_token=A_  ,    cls_token=A_  ,    unk_token=A_  ,    pad_token=A_  ,    mask_token=A_  ,    **A_  ,    )

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

										_UpperCAmelCase				:						Union[str, Any]       						=			{
										    lang_code: self.convert_tokens_to_ids(A_		) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										_UpperCAmelCase				:						Any       						=			src_lang if src_lang is not None else "en_XX"
										_UpperCAmelCase				:						Tuple       						=			tgt_lang
										_UpperCAmelCase				:						Union[str, Any]       						=			self.lang_code_to_id[self._src_lang]
										self.set_src_lang_special_tokens(self._src_lang		)


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self._src_lang


						@src_lang.setter
						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[str]       						=			new_src_lang
										self.set_src_lang_special_tokens(self._src_lang		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None		):







										'''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  ,    A_  ,    A_ = "en_XX"  ,    A_ = None  ,    A_ = "ro_RO"  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			src_lang
										_UpperCAmelCase				:						Optional[Any]       						=			tgt_lang
										return super().prepare_seqaseq_batch(A_  ,    A_  ,    **A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.set_src_lang_special_tokens(self.src_lang		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.set_tgt_lang_special_tokens(self.tgt_lang		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			self.convert_tokens_to_ids(A_		)
										_UpperCAmelCase				:						str       						=			[self.cur_lang_code_id]
										_UpperCAmelCase				:						Tuple       						=			[self.eos_token_id]

										_UpperCAmelCase				:						int       						=			self.convert_ids_to_tokens(self.prefix_tokens		)
										_UpperCAmelCase				:						int       						=			self.convert_ids_to_tokens(self.suffix_tokens		)

										_UpperCAmelCase				:						List[Any]       						=			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  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			self.convert_tokens_to_ids(A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			[self.cur_lang_code_id]
										_UpperCAmelCase				:						Optional[int]       						=			[self.eos_token_id]

										_UpperCAmelCase				:						Dict       						=			self.convert_ids_to_tokens(self.prefix_tokens		)
										_UpperCAmelCase				:						int       						=			self.convert_ids_to_tokens(self.suffix_tokens		)

										_UpperCAmelCase				:						Union[str, Any]       						=			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  ,    A_  ,    A_  ,    A_  ,    A_  ,    **A_		):







										'''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"		)
										_UpperCAmelCase				:						Union[str, Any]       						=			src_lang
										_UpperCAmelCase				:						Dict       						=			self(A_  ,    add_special_tokens=A_  ,    return_tensors=A_  ,    **A_		)
										_UpperCAmelCase				:						List[str]       						=			self.convert_tokens_to_ids(A_		)
										_UpperCAmelCase				:						Dict       						=			tgt_lang_id
										return inputs





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None		):







										'''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
										_UpperCAmelCase				:						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,)





import math

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				list:
				if n <= 0:
								raise ValueError("Only positive integers have prime factors"       )
				_UpperCAmelCase				:						Union[str, Any]       						=			[]
				while n % 2 == 0:
								pf.append(2       )
								_UpperCAmelCase				:						Tuple       						=			int(n / 2       )
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								while n % i == 0:
												pf.append(lowerCAmelCase       )
												_UpperCAmelCase				:						Optional[int]       						=			int(n / i       )
				if n > 2:
								pf.append(lowerCAmelCase       )
				return pf

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Dict       						=			1
				_UpperCAmelCase				:						str       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						List[str]       						=			int(n / 2       )
				div *= temp
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						Any       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						Any       						=			int(n / i       )
								div *= temp
				if n > 1:
								div *= 2
				return div

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				if n <= 0:
								raise ValueError("Only positive numbers are accepted"       )
				_UpperCAmelCase				:						Optional[Any]       						=			1
				_UpperCAmelCase				:						Union[str, Any]       						=			1
				while n % 2 == 0:
								temp += 1
								_UpperCAmelCase				:						Optional[Any]       						=			int(n / 2       )
				if temp > 1:
								s *= (2**temp - 1) / (2 - 1)
				for i in range(3     ,					int(math.sqrt(lowerCAmelCase       )       ) + 1     ,					2       ):
								_UpperCAmelCase				:						List[str]       						=			1
								while n % i == 0:
												temp += 1
												_UpperCAmelCase				:						List[Any]       						=			int(n / i       )
								if temp > 1:
												s *= (i**temp - 1) / (i - 1)
				return int(lowerCAmelCase       )

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:
				_UpperCAmelCase				:						Any       						=			n
				for x in set(prime_factors(lowerCAmelCase       )       ):
								s *= (x - 1) / x
				return int(lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


