





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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save




def  __UpperCAmelCase(   lowercase_			,     lowercase_			,     lowercase_=10_24			,     lowercase_=10_24			,     lowercase_=False			,     **lowercase_   ):
       _lowerCamelCase				:  List[str]						=		AutoTokenizer.from_pretrained(lowercase_   )
       _lowerCamelCase				:  Tuple						=		SeqaSeqDataset(lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     type_path='''train'''			,     **lowercase_   )
       _lowerCamelCase				:  Optional[int]						=		tok.pad_token_id

       def get_lens(lowercase_   ):
              _lowerCamelCase				:  int						=		tqdm(
                  DataLoader(lowercase_			,     batch_size=5_12			,     num_workers=8			,     shuffle=lowercase_			,     collate_fn=ds.collate_fn   )			,     desc=str(ds.len_file   )			,     )
              _lowerCamelCase				:  List[Any]						=		[]
              for batch in dl:
                     _lowerCamelCase				:  Optional[Any]						=		batch['''input_ids'''].ne(lowercase_   ).sum(1   ).tolist()
                     _lowerCamelCase				:  Optional[Any]						=		batch['''labels'''].ne(lowercase_   ).sum(1   ).tolist()
                     if consider_target:
                            for src, tgt in zip(lowercase_			,     lowercase_   ):
                                   max_lens.append(max(lowercase_			,     lowercase_   )   )
                     else:
                            max_lens.extend(lowercase_   )
              return max_lens

       _lowerCamelCase				:  Optional[Any]						=		get_lens(lowercase_   )
       _lowerCamelCase				:  Optional[int]						=		SeqaSeqDataset(lowercase_			,     lowercase_			,     lowercase_			,     lowercase_			,     type_path='''val'''			,     **lowercase_   )
       _lowerCamelCase				:  Dict						=		get_lens(lowercase_   )
       pickle_save(lowercase_			,     train_ds.len_file   )
       pickle_save(lowercase_			,     val_ds.len_file   )


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








import copy
import random

from transformers import CLIPTokenizer







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             super().__init__(*a__   ,				**a__)
             _lowerCamelCase				:  Optional[Any]						=		{}


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		super().add_tokens(a__   ,				*a__   ,				**a__)
             if num_added_tokens == 0:
                    raise ValueError(
                        F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                        ''' `placeholder_token` that is not already in the tokenizer.''')


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=1   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                    output.append(a__)
             else:
                    _lowerCamelCase				:  Dict						=		[]
                    for i in range(a__):
                           _lowerCamelCase				:  Optional[Any]						=		placeholder_token + F"""_{i}"""
                           self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                           output.append(a__)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F"""The tokenizer already has placeholder token {token} that can get confused with"""
                               F""" {placeholder_token}keep placeholder tokens independent""")
             _lowerCamelCase				:  int						=		output


      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=1.0):



             """simple docstring"""





             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Tuple						=		[]
                    for i in range(len(a__)):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]   ,				vector_shuffle=a__))
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           _lowerCamelCase				:  Union[str, Any]						=		self.token_map[placeholder_token]
                           _lowerCamelCase				:  Optional[Any]						=		tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                           if vector_shuffle:
                                  _lowerCamelCase				:  int						=		copy.copy(a__)
                                  random.shuffle(a__)
                           _lowerCamelCase				:  List[str]						=		text.replace(a__   ,				''' '''.join(a__))
             return text


      def __call__(		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)



      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)

