
"""simple docstring"""





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		"""▁"""

_a														=		{"""vocab_file""": """sentencepiece.bpe.model"""}

_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"""
        ),
    }
}

_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(   lowerCamelCase     ):
		lowercase__      =				VOCAB_FILES_NAMES
		lowercase__      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase__      =				PRETRAINED_VOCAB_FILES_MAP
		lowercase__      =				['input_ids', 'attention_mask']

		lowercase__      =				[]
		lowercase__      =				[]



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






							'''simple docstring'''
							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else mask_token

							_UpperCamelCase										=  {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__a    ,		eos_token=__a    ,		unk_token=__a    ,		sep_token=__a    ,		cls_token=__a    ,		pad_token=__a    ,		mask_token=__a    ,		tokenizer_file=__a    ,		src_lang=__a    ,		tgt_lang=__a    ,		additional_special_tokens=__a    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**__a    ,		)

							_UpperCamelCase										=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(str(__a))
							_UpperCamelCase										=  vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_UpperCamelCase										=  {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_UpperCamelCase										=  1

							_UpperCamelCase										=  len(self.sp_model)
							_UpperCamelCase										=  {
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a)
							}
							_UpperCamelCase										=  {v: k for k, v in self.lang_code_to_id.items()}
							_UpperCamelCase										=  len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

							self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
							_UpperCamelCase										=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							_UpperCamelCase										=  list(self.lang_code_to_id.keys())

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

							_UpperCamelCase										=  src_lang if src_lang is not None else '''en_XX'''
							_UpperCamelCase										=  self.lang_code_to_id[self._src_lang]
							_UpperCamelCase										=  tgt_lang
							self.set_src_lang_special_tokens(self._src_lang)



		def __getstate__( self)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.__dict__.copy()
							_UpperCamelCase										=  None
							_UpperCamelCase										=  self.sp_model.serialized_model_proto()
							return state



		def __setstate__( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  d

							# for backward compatibility
							if not hasattr(self    ,		'''sp_model_kwargs'''):
												_UpperCamelCase										=  {}

							_UpperCamelCase										=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



		@property
		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token



		@property
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							return self._src_lang



		@src_lang.setter
		def 						UpperCAmelCase							( self    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None    ,		__a = False)	->	List[int]:






							'''simple docstring'''

							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__a    ,		token_ids_a=__a    ,		already_has_special_tokens=__a)

							_UpperCamelCase										=  [1] * len(self.prefix_tokens)
							_UpperCamelCase										=  [1] * len(self.suffix_tokens)
							if token_ids_a is None:
												return prefix_ones + ([0] * len(__a)) + suffix_ones
							return prefix_ones + ([0] * len(__a)) + ([0] * len(__a)) + suffix_ones



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = 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    ,		__a    ,		__a = None)	->	List[int]:






							'''simple docstring'''
							_UpperCamelCase										=  [self.sep_token_id]
							_UpperCamelCase										=  [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    ,		__a    ,		__a    ,		__a    ,		__a    ,		**__a)	->	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''')
							_UpperCamelCase										=  src_lang
							_UpperCamelCase										=  self(__a    ,		add_special_tokens=__a    ,		return_tensors=__a    ,		**__a)
							_UpperCamelCase										=  self.convert_tokens_to_ids(__a)
							_UpperCamelCase										=  tgt_lang_id
							return inputs



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  {self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)}
							vocab.update(self.added_tokens_encoder)
							return vocab



		def 						UpperCAmelCase							( self    ,		__a)	->	List[str]:






							'''simple docstring'''
							return self.sp_model.encode(__a    ,		out_type=__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							_UpperCamelCase										=  self.sp_model.PieceToId(__a)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



		def 						UpperCAmelCase							( self    ,		__a)	->	List[str]:






							'''simple docstring'''
							if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset)



		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  ''''''.join(__a).replace(__a    ,		''' ''').strip()
							return out_string



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None)	->	Tuple[str]:






							'''simple docstring'''
							if not os.path.isdir(__a):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
												return
							_UpperCamelCase										=  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) and os.path.isfile(self.vocab_file):
												copyfile(self.vocab_file    ,		__a)
							elif not os.path.isfile(self.vocab_file):
												with open(__a    ,		'''wb''') as fi:
																	_UpperCamelCase										=  self.sp_model.serialized_model_proto()
																	fi.write(__a)

							return (out_vocab_file,)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = "en_XX"    ,		__a = None    ,		__a = "ro_RO"    ,		**__a    ,		)	->	BatchEncoding:






							'''simple docstring'''
							_UpperCamelCase										=  src_lang
							_UpperCamelCase										=  tgt_lang
							return super().prepare_seqaseq_batch(__a    ,		__a    ,		**__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							return self.set_src_lang_special_tokens(self.src_lang)



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							return self.set_tgt_lang_special_tokens(self.tgt_lang)



		def 						UpperCAmelCase							( self    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  self.lang_code_to_id[src_lang]
							_UpperCamelCase										=  []
							_UpperCamelCase										=  [self.eos_token_id, self.cur_lang_code]




		def 						UpperCAmelCase							( self    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  self.lang_code_to_id[lang]
							_UpperCamelCase										=  []
							_UpperCamelCase										=  [self.eos_token_id, self.cur_lang_code]








"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a														=		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					return (abs(source - target     ) / target) < 0.01



@pytest.mark.integration
def 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  _TestCommandArgs(dataset=__snake_case,					all_configs=__snake_case,					save_infos=__snake_case     )
					_UpperCamelCase										=  TestCommand(*__snake_case     )
					test_command.run()
					_UpperCamelCase										=  os.path.join(__snake_case,					'''README.md'''     )
					assert os.path.exists(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict.from_directory(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict(
					    {
					        '''default''': DatasetInfo(
					            features=Features(
					                {
					                    '''tokens''': Sequence(Value('''string'''     )     ),
					                    '''ner_tags''': Sequence(
					                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']     )     ),
					                    '''langs''': Sequence(Value('''string'''     )     ),
					                    '''spans''': Sequence(Value('''string'''     )     ),
					                }     ),					splits=[
					                {
					                    '''name''': '''train''',
					                    '''num_bytes''': 2_35_15_63,
					                    '''num_examples''': 1_00_00,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 23_84_18,
					                    '''num_examples''': 10_00,
					                },
					            ],					download_size=3_94_06_80,					dataset_size=2_58_99_81,					)
					    }     )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_UpperCamelCase			,  _UpperCamelCase										=  getattr(dataset_infos['''default'''],					__snake_case     ), getattr(expected_dataset_infos['''default'''],					__snake_case     )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case,					__snake_case     )
										elif key == "splits":
															assert list(__snake_case     ) == list(__snake_case     )
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes,					expected[split].num_bytes     )
										else:
															result == expected






