


'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							      =     {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     ['''DistilBertTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DistilBertForMaskedLM''',
		    '''DistilBertForMultipleChoice''',
		    '''DistilBertForQuestionAnswering''',
		    '''DistilBertForSequenceClassification''',
		    '''DistilBertForTokenClassification''',
		    '''DistilBertModel''',
		    '''DistilBertPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDistilBertForMaskedLM''',
		    '''TFDistilBertForMultipleChoice''',
		    '''TFDistilBertForQuestionAnswering''',
		    '''TFDistilBertForSequenceClassification''',
		    '''TFDistilBertForTokenClassification''',
		    '''TFDistilBertMainLayer''',
		    '''TFDistilBertModel''',
		    '''TFDistilBertPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''FlaxDistilBertForMaskedLM''',
		    '''FlaxDistilBertForMultipleChoice''',
		    '''FlaxDistilBertForQuestionAnswering''',
		    '''FlaxDistilBertForSequenceClassification''',
		    '''FlaxDistilBertForTokenClassification''',
		    '''FlaxDistilBertModel''',
		    '''FlaxDistilBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_distilbert import (
		    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DistilBertConfig,
		    DistilBertOnnxConfig,
		)
		from .tokenization_distilbert import DistilBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_distilbert_fast import DistilBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_distilbert import (
				    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DistilBertForMaskedLM,
				    DistilBertForMultipleChoice,
				    DistilBertForQuestionAnswering,
				    DistilBertForSequenceClassification,
				    DistilBertForTokenClassification,
				    DistilBertModel,
				    DistilBertPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_distilbert import (
				    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDistilBertForMaskedLM,
				    TFDistilBertForMultipleChoice,
				    TFDistilBertForQuestionAnswering,
				    TFDistilBertForSequenceClassification,
				    TFDistilBertForTokenClassification,
				    TFDistilBertMainLayer,
				    TFDistilBertModel,
				    TFDistilBertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				    FlaxDistilBertPreTrainedModel,
				)

else:
		import sys

		__snake_case							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					for split in ds.keys():
										print(f'''Splitting {split} with {ds[split].num_rows} records'''   )

										# to save to val.source, val.target like summary datasets
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= tgt_path.open('''w+'''   )

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]   ):
															UpperCamelCase__   :Union[str, Any]												= x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''   )
															tgt_fp.write(ex[tgt_lang] + '''\n'''   )

					print(f'''Saved {dataset} dataset to {save_dir}'''   )


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)