



"""simple docstring"""




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'		,					[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]		,					)
def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	Union[str, Any]:






      '''simple docstring'''


      lowercase     :					Union[str, Any] 		=      tmp_path_factory.mktemp('dset_infos_dir' )
      if "full:README.md" in files:
            with open(dataset_infos_dir / 'README.md'		,					'w' ) as f:
                  f.write('---\ndataset_info:\n  dataset_size: 42\n---' )
      if "empty:README.md" in files:
            with open(dataset_infos_dir / 'README.md'		,					'w' ) as f:
                  f.write('' )
    # we want to support dataset_infos.json for backward compatibility
      if "dataset_infos.json" in files:
            with open(dataset_infos_dir / 'dataset_infos.json'		,					'w' ) as f:
                  f.write('{\"default\": {\"dataset_size\": 42}}' )
      lowercase     :					Tuple 		=      DatasetInfosDict.from_directory(lowerCamelCase__ )
      assert dataset_infos
      assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'		,					[
        DatasetInfo(),
        DatasetInfo(
            description='foo'		,					features=Features({'a': Value('int32' )} )		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train'}]		,					download_size=42		,					),
    ]		,					)
def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	Any:






      '''simple docstring'''


      lowercase     :					Optional[int] 		=      str(lowerCamelCase__ )
      dataset_info.write_to_directory(lowerCamelCase__ )
      lowercase     :					Dict 		=      DatasetInfo.from_directory(lowerCamelCase__ )
      assert dataset_info == reloaded
      assert os.path.exists(os.path.join(lowerCamelCase__		,					'dataset_info.json' ) )


def      lowercase__   (	)   ->	List[Any]:






      '''simple docstring'''


      lowercase     :					List[str] 		=      DatasetInfo(
          description='foo'		,					citation='bar'		,					homepage='https://foo.bar'		,					license='CC0'		,					features=Features({'a': Value('int32' )} )		,					post_processed={}		,					supervised_keys=()		,					task_templates=[]		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train', 'num_examples': 42}]		,					download_checksums={}		,					download_size=13_37		,					post_processing_size=4_42		,					dataset_size=12_34		,					size_in_bytes=13_37 + 4_42 + 12_34		,					)
      lowercase     :					Dict 		=      dataset_info._to_yaml_dict()
      assert sorted(lowerCamelCase__ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            assert key in dataset_info_yaml_dict
            assert isinstance(dataset_info_yaml_dict[key]		,					(list, dict, int, str) )
      lowercase     :					Optional[int] 		=      yaml.safe_dump(lowerCamelCase__ )
      lowercase     :					List[str] 		=      yaml.safe_load(lowerCamelCase__ )
      assert dataset_info_yaml_dict == reloaded


def      lowercase__   (	)   ->	int:






      '''simple docstring'''


      lowercase     :					Optional[Any] 		=      DatasetInfo()
      lowercase     :					Tuple 		=      dataset_info._to_yaml_dict()
      assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    'dataset_infos_dict'		,					[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()} ),
        DatasetInfosDict({'my_config_name': DatasetInfo()} ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'		,					features=Features({'a': Value('int32' )} )		,					builder_name='builder'		,					config_name='config'		,					version='1.0.0'		,					splits=[{'name': 'train'}]		,					download_size=42		,					)
            } ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42 ),
                'v2': DatasetInfo(dataset_size=13_37 ),
            } ),
    ]		,					)
def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	Tuple:






      '''simple docstring'''


      lowercase     :					Union[str, Any] 		=      str(lowerCamelCase__ )
      dataset_infos_dict.write_to_directory(lowerCamelCase__ )
      lowercase     :					Optional[Any] 		=      DatasetInfosDict.from_directory(lowerCamelCase__ )

      # the config_name of the dataset_infos_dict take over the attribute
      for config_name, dataset_info in dataset_infos_dict.items():
            lowercase     :					int 		=      config_name
            # the yaml representation doesn't include fields like description or citation
            # so we just test that we can recover what we can from the yaml
            lowercase     :					List[Any] 		=      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
      assert dataset_infos_dict == reloaded

      if dataset_infos_dict:
            assert os.path.exists(os.path.join(lowerCamelCase__		,					'README.md' ) )


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''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

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

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

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

					lowerCamelCase_						=			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_						=			{
					    lang_code: self.convert_tokens_to_ids(lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

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



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self._src_lang



	@src_lang.setter
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			new_src_lang
					self.set_src_lang_special_tokens(self._src_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       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_						=			src_lang
					lowerCamelCase_						=			self(lowercase						,  add_special_tokens=lowercase						,  return_tensors=lowercase						,  **lowercase )
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			tgt_lang_id
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = "en_XX"						,  lowercase = None						,  lowercase = "ro_RO"						,  **lowercase						,  )				->       BatchEncoding:
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			tgt_lang
					return super().prepare_seqaseq_batch(lowercase						,  lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return self.set_src_lang_special_tokens(self.src_lang )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					return self.set_tgt_lang_special_tokens(self.tgt_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					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(lowercase ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

					return (out_vocab_file,)


