

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			Tuple		,__a      :			Any=1_024		,__a      :			List[Any]=1_024		,__a      :			Optional[Any]=False		,**__a      :			int	)			->     List[str]:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		AutoTokenizer.from_pretrained(UpperCamelCase_	)
					_a	:    int         =		SeqaSeqDataset(UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,type_path='''train'''		,**UpperCamelCase_	)
					_a	:    List[Any]         =		tok.pad_token_id

					def get_lens(__a      :			List[Any]	):
										_a	:    Optional[Any]         =		tqdm(
										    DataLoader(UpperCamelCase_		,batch_size=512		,num_workers=8		,shuffle=UpperCamelCase_		,collate_fn=ds.collate_fn	)		,desc=str(ds.len_file	)		,)
										_a	:    str         =		[]
										for batch in dl:
															_a	:    List[Any]         =		batch['''input_ids'''].ne(UpperCamelCase_	).sum(1	).tolist()
															_a	:    Any         =		batch['''labels'''].ne(UpperCamelCase_	).sum(1	).tolist()
															if consider_target:
																				for src, tgt in zip(UpperCamelCase_		,UpperCamelCase_	):
																									max_lens.append(max(UpperCamelCase_		,UpperCamelCase_	)	)
															else:
																				max_lens.extend(UpperCamelCase_	)
										return max_lens

					_a	:    Tuple         =		get_lens(UpperCamelCase_	)
					_a	:    Union[str, Any]         =		SeqaSeqDataset(UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,type_path='''val'''		,**UpperCamelCase_	)
					_a	:    Tuple         =		get_lens(UpperCamelCase_	)
					pickle_save(UpperCamelCase_		,train_ds.len_file	)
					pickle_save(UpperCamelCase_		,val_ds.len_file	)


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





"""simple docstring"""





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  List[Any]						   =		'''BridgeTowerImageProcessor'''
		__lowercase							:  Tuple						   =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				super().__init__(lowerCAmelCase__							,  lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = self.tokenizer(
				    text=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  max_length=lowerCAmelCase__							,  stride=lowerCAmelCase__							,  pad_to_multiple_of=lowerCAmelCase__							,  return_token_type_ids=lowerCAmelCase__							,  return_attention_mask=lowerCAmelCase__							,  return_overflowing_tokens=lowerCAmelCase__							,  return_special_tokens_mask=lowerCAmelCase__							,  return_offsets_mapping=lowerCAmelCase__							,  return_length=lowerCAmelCase__							,  verbose=lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__							,  )
				# add pixel_values + pixel_mask
				__SCREAMING_SNAKE_CASE            = self.image_processor(
				    lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  do_normalize=lowerCAmelCase__							,  do_center_crop=lowerCAmelCase__							,  **lowerCAmelCase__)
				encoding.update(lowerCAmelCase__)

				return encoding




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.tokenizer.model_input_names
				__SCREAMING_SNAKE_CASE            = self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




