"""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:
				__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        _snake_case   (    _UpperCamelCase						):
						snake_case__	     =	VOCAB_FILES_NAMES
						snake_case__	     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__	     =	PRETRAINED_VOCAB_FILES_MAP
						snake_case__	     =	["""input_ids""", """attention_mask"""]
						snake_case__	     =	MBartTokenizer

						snake_case__	     =	[]
						snake_case__	     =	[]






						def __init__(				self					:	str							,    UpperCAmelCase					:	List[str]=None							,    UpperCAmelCase					:	Union[str, Any]=None							,    UpperCAmelCase					:	List[Any]="<s>"							,    UpperCAmelCase					:	List[Any]="</s>"							,    UpperCAmelCase					:	Tuple="</s>"							,    UpperCAmelCase					:	Optional[int]="<s>"							,    UpperCAmelCase					:	str="<unk>"							,    UpperCAmelCase					:	Union[str, Any]="<pad>"							,    UpperCAmelCase					:	List[str]="<mask>"							,    UpperCAmelCase					:	Dict=None							,    UpperCAmelCase					:	Any=None							,    UpperCAmelCase					:	Optional[int]=None							,    **UpperCAmelCase					:	Dict							,    ):
													# Mask token behave like a normal word, i.e. include the space before it
													__lowerCamelCase    :						Any						      =   AddedToken(__lowerCamelCase							,    lstrip=__lowerCamelCase							,    rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase							,    __lowerCamelCase			) else mask_token

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

													__lowerCamelCase    :						int						      =   vocab_file
													__lowerCamelCase    :						Union[str, Any]						      =   False if not self.vocab_file else True

													__lowerCamelCase    :						Optional[int]						      =   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}			)
													__lowerCamelCase    :						int						      =   {
													    lang_code: self.convert_tokens_to_ids(__lowerCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
													}

													__lowerCamelCase    :						Dict						      =   src_lang if src_lang is not None else "en_XX"
													__lowerCamelCase    :						Any						      =   self.convert_tokens_to_ids(self._src_lang			)
													__lowerCamelCase    :						Dict						      =   tgt_lang
													self.set_src_lang_special_tokens(self._src_lang			)






						@property
						def 					lowerCamelCase__   (				self					:	Dict			):
													return self._src_lang






						@src_lang.setter
						def 					lowerCamelCase__   (				self					:	Tuple							,    UpperCAmelCase					:	str			):
													__lowerCamelCase    :						Optional[int]						      =   new_src_lang
													self.set_src_lang_special_tokens(self._src_lang			)






						def 					lowerCamelCase__   (				self					:	Tuple							,    UpperCAmelCase					:	List[int]							,    UpperCAmelCase					:	Optional[List[int]] = 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 					lowerCamelCase__   (				self					:	Optional[Any]							,    UpperCAmelCase					:	List[int]							,    UpperCAmelCase					:	Optional[List[int]] = None			):
													__lowerCamelCase    :						List[str]						      =   [self.sep_token_id]
													__lowerCamelCase    :						Dict						      =   [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 					lowerCamelCase__   (				self					:	str							,    UpperCAmelCase					:	Optional[int]							,    UpperCAmelCase					:	str							,    UpperCAmelCase					:	Optional[str]							,    UpperCAmelCase					:	Optional[str]							,    **UpperCAmelCase					:	List[Any]			):
													if src_lang is None or tgt_lang is None:
																				raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"			)
													__lowerCamelCase    :						str						      =   src_lang
													__lowerCamelCase    :						Union[str, Any]						      =   self(__lowerCamelCase							,    add_special_tokens=__lowerCamelCase							,    return_tensors=__lowerCamelCase							,    **__lowerCamelCase			)
													__lowerCamelCase    :						Dict						      =   self.convert_tokens_to_ids(__lowerCamelCase			)
													__lowerCamelCase    :						Any						      =   tgt_lang_id
													return inputs






						def 					lowerCamelCase__   (				self					:	str							,    UpperCAmelCase					:	List[str]							,    UpperCAmelCase					:	str = "en_XX"							,    UpperCAmelCase					:	Optional[List[str]] = None							,    UpperCAmelCase					:	str = "ro_RO"							,    **UpperCAmelCase					:	str							,    ):
													__lowerCamelCase    :						Any						      =   src_lang
													__lowerCamelCase    :						Dict						      =   tgt_lang
													return super().prepare_seqaseq_batch(__lowerCamelCase							,    __lowerCamelCase							,    **__lowerCamelCase			)






						def 					lowerCamelCase__   (				self					:	List[Any]			):
													return self.set_src_lang_special_tokens(self.src_lang			)






						def 					lowerCamelCase__   (				self					:	Dict			):
													return self.set_tgt_lang_special_tokens(self.tgt_lang			)






						def 					lowerCamelCase__   (				self					:	Union[str, Any]							,    UpperCAmelCase					:	List[str]			):
													__lowerCamelCase    :						str						      =   self.convert_tokens_to_ids(__lowerCamelCase			)
													__lowerCamelCase    :						Optional[int]						      =   []
													__lowerCamelCase    :						List[Any]						      =   [self.eos_token_id, self.cur_lang_code]

													__lowerCamelCase    :						Tuple						      =   self.convert_ids_to_tokens(self.prefix_tokens			)
													__lowerCamelCase    :						Optional[int]						      =   self.convert_ids_to_tokens(self.suffix_tokens			)

													__lowerCamelCase    :						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 					lowerCamelCase__   (				self					:	List[str]							,    UpperCAmelCase					:	str			):
													__lowerCamelCase    :						int						      =   self.convert_tokens_to_ids(__lowerCamelCase			)
													__lowerCamelCase    :						Optional[int]						      =   []
													__lowerCamelCase    :						Optional[int]						      =   [self.eos_token_id, self.cur_lang_code]

													__lowerCamelCase    :						Any						      =   self.convert_ids_to_tokens(self.prefix_tokens			)
													__lowerCamelCase    :						Optional[Any]						      =   self.convert_ids_to_tokens(self.suffix_tokens			)

													__lowerCamelCase    :						Tuple						      =   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 					lowerCamelCase__   (				self					:	str							,    UpperCAmelCase					:	str							,    UpperCAmelCase					:	Optional[str] = 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(__lowerCamelCase			):
																				logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""			)
																				return
													__lowerCamelCase    :						List[Any]						      =   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,)





from __future__ import annotations




def 			__magic_name__				( A		:					list   ):






			'''simple docstring'''







			if len(A   ) == 0:
						return []
			a    ,		a    =			min(A   ), max(A   )
			a    =			int(max_value - min_value   ) + 1
			a    =			[[] for _ in range(A   )]

			for i in my_list:
						buckets[int(i - min_value   )].append(A   )

			return [v for bucket in buckets for v in sorted(A   )]


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
		assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]






