




import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


UpperCAmelCase__				     = [
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]







def 		_A(       UpperCamelCase__							:			List[Any]=True				)    -> Optional[Any]:
							'''simple docstring'''


							if with_config:
														return [
														    {
														        "testcase_name": d["dataset"] + "/" + d["config_name"],
														        "dataset": d["dataset"],
														        "config_name": d["config_name"],
														    }
														    for d in DATASETS_ON_HF_GCP
														]
							else:
														return [
														    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
														]







@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__SCREAMING_SNAKE_CASE  )  )
class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       Dict       	=		None
						UpperCamelCase_	:       List[str]       	=		None






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     Optional[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													with TemporaryDirectory() as tmp_dir:
																				__lowercase      =    dataset_module_factory(lowerCamelCase__				,							cache_dir=lowerCamelCase__				)

																				__lowercase      =    import_main_class(dataset_module.module_path				,							dataset=lowerCamelCase__				)

																				__lowercase      =    builder_cls(
																				    cache_dir=lowerCamelCase__				,							config_name=lowerCamelCase__				,							hash=dataset_module.hash				,							)

																				__lowercase      =    '''/'''.join(
																				    [
																				        HF_GCP_BASE_URL,
																				        builder_instance._relative_data_dir(with_hash=lowerCamelCase__				).replace(os.sep				,							'''/'''				),
																				        config.DATASET_INFO_FILENAME,
																				    ]				)
																				__lowercase      =    cached_path(lowerCamelCase__				,							cache_dir=lowerCamelCase__				)
																				self.assertTrue(os.path.exists(lowerCamelCase__				)				)







@pytest.mark.integration
def 		_A(       UpperCamelCase__							:			Any				)    -> int:
							'''simple docstring'''


							__lowercase      =    tmp_path_factory.mktemp('''test_hf_gcp'''				) / '''test_wikipedia_simple'''
							__lowercase      =    dataset_module_factory('''wikipedia'''				,       cache_dir=UpperCamelCase__				)
							__lowercase      =    import_main_class(dataset_module.module_path				)
							__lowercase      =    builder_cls(
							    cache_dir=UpperCamelCase__				,       config_name='''20220301.frr'''				,       hash=dataset_module.hash				,       )
							# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
							__lowercase      =    None
							builder_instance.download_and_prepare()
							__lowercase      =    builder_instance.as_dataset()
							assert ds







@pytest.mark.integration
def 		_A(       UpperCamelCase__							:			List[Any]				)    -> str:
							'''simple docstring'''


							__lowercase      =    dataset_module_factory('''wikipedia'''				,       cache_dir=UpperCamelCase__				)
							__lowercase      =    import_main_class(dataset_module.module_path				,       dataset=UpperCamelCase__				)
							__lowercase      =    builder_cls(
							    cache_dir=UpperCamelCase__				,       config_name='''20220301.frr'''				,       hash=dataset_module.hash				,       )
							__lowercase      =    builder_instance.as_streaming_dataset()
							assert ds
							assert isinstance(UpperCamelCase__				,       UpperCamelCase__				)
							assert "train" in ds
							assert isinstance(ds['''train''']				,       UpperCamelCase__				)
							assert next(iter(ds['''train''']				)				)












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:
			UpperCAmelCase__				     = None


UpperCAmelCase__				     = logging.get_logger(__name__)


UpperCAmelCase__				     = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				     = {
    "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",
    },
}

UpperCAmelCase__				     = {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
UpperCAmelCase__				     = ["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 a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		VOCAB_FILES_NAMES
						UpperCamelCase_	:       List[str]       	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	:       int       	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	:       Optional[int]       	=		['input_ids', 'attention_mask']
						UpperCamelCase_	:       Optional[int]       	=		MBartTokenizer

						UpperCamelCase_	:       List[int]       	=		[]
						UpperCamelCase_	:       List[int]       	=		[]






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


													__lowercase      =    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__				,							)

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

													__lowercase      =    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}				)
													__lowercase      =    {
													    lang_code: self.convert_tokens_to_ids(lowerCamelCase__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
													}

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






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													return self._src_lang






						@src_lang.setter
						def     UpperCAmelCase_      ( self :     Optional[Any]				,							lowerCamelCase__ :     str				)    ->						None:
													"""simple docstring"""


													__lowercase      =    new_src_lang
													self.set_src_lang_special_tokens(self._src_lang				)






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     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 :     Optional[int]				,							lowerCamelCase__ :     List[int]				,							lowerCamelCase__ :     Optional[List[int]] = None				)    ->						List[int]:
													"""simple docstring"""


													__lowercase      =    [self.sep_token_id]
													__lowercase      =    [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]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str]				,							lowerCamelCase__ :     Optional[str]				,							**lowerCamelCase__ :     List[str]				)    ->						Optional[int]:
													"""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'''				)
													__lowercase      =    src_lang
													__lowercase      =    self(lowerCamelCase__				,							add_special_tokens=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)
													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    tgt_lang_id
													return inputs






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     str = "en_XX"				,							lowerCamelCase__ :     Optional[List[str]] = None				,							lowerCamelCase__ :     str = "ro_RO"				,							**lowerCamelCase__ :     Union[str, Any]				,							)    ->						BatchEncoding:
													"""simple docstring"""


													__lowercase      =    src_lang
													__lowercase      =    tgt_lang
													return super().prepare_seqaseq_batch(lowerCamelCase__				,							lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													return self.set_src_lang_special_tokens(self.src_lang				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													return self.set_tgt_lang_special_tokens(self.tgt_lang				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     Optional[Any]				)    ->						None:
													"""simple docstring"""


													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    []
													__lowercase      =    [self.eos_token_id, self.cur_lang_code]

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

													__lowercase      =    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 :     Tuple				,							lowerCamelCase__ :     str				)    ->						None:
													"""simple docstring"""


													__lowercase      =    self.convert_tokens_to_ids(lowerCamelCase__				)
													__lowercase      =    []
													__lowercase      =    [self.eos_token_id, self.cur_lang_code]

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

													__lowercase      =    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 :     Optional[int]				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     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(lowerCamelCase__				):
																				logger.error(f'Vocabulary path ({save_directory}) should be a directory.'				)
																				return
													__lowercase      =    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,)






