



"""simple docstring"""






from pathlib import Path

import fire
from tqdm import tqdm






def      snake_case__   (		__lowerCamelCase				:    Dict="ro"							,		__lowerCamelCase				:    Union[str, Any]="en"							,		__lowerCamelCase				:    Tuple="wmt16"							,		__lowerCamelCase				:    List[str]=None	):
			"""simple docstring"""
			try:
						import datasets
			except (ModuleNotFoundError, ImportError):
						raise ImportError('''run pip install datasets'''	)
			lowerCamelCase__		:		int			=f'''{src_lang}-{tgt_lang}'''
			print(f'''Converting {dataset}-{pair}'''	)
			lowerCamelCase__		:		List[str]			=datasets.load_dataset(__lowerCamelCase							,		__lowerCamelCase	)
			if save_dir is None:
						lowerCamelCase__		:		Optional[int]			=f'''{dataset}-{pair}'''
			lowerCamelCase__		:		Tuple			=Path(__lowerCamelCase	)
			save_dir.mkdir(exist_ok=__lowerCamelCase	)

			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
						lowerCamelCase__		:		Optional[Any]			='''val''' if split == '''validation''' else split
						lowerCamelCase__		:		Optional[Any]			=save_dir.joinpath(f'''{fn}.source'''	)
						lowerCamelCase__		:		Dict			=save_dir.joinpath(f'''{fn}.target'''	)
						lowerCamelCase__		:		Optional[Any]			=src_path.open('''w+'''	)
						lowerCamelCase__		:		Any			=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]	):
									lowerCamelCase__		:		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)









"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['image_processor', 'tokenizer']
					_a   			=							'BlipImageProcessor'
					_a   			=							'AutoTokenizer'



					def __init__(       self							:  Tuple,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict     )->					str:
								lowerCamelCase__		:		Any			=False
								super().__init__(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.image_processor



					def __call__(       self							:  Union[str, Any],  lowerCamelCase							:  ImageInput = None,  lowerCamelCase							:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[bool, str, PaddingStrategy] = False,  lowerCamelCase							:  Union[bool, str, TruncationStrategy] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int = 0,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  **lowerCamelCase							:  List[str],  )->					BatchEncoding:
								if images is None and text is None:
											raise ValueError('''You have to specify either images or text.'''     )

								# Get only text
								if images is None:
											lowerCamelCase__		:		str			=self.tokenizer
											lowerCamelCase__		:		str			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
											return text_encoding

								# add pixel_values
								lowerCamelCase__		:		Optional[int]			=self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase     )

								if text is not None:
											lowerCamelCase__		:		Union[str, Any]			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
											lowerCamelCase__		:		Optional[Any]			=None

								if text_encoding is not None:
											encoding_image_processor.update(lowerCamelCase     )

								return encoding_image_processor



					def 		snake_case       (       self							:  str,  *lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  *lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase     )




					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=self.tokenizer.model_input_names
								lowerCamelCase__		:		List[str]			=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




