



import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =["""image_processor""", """tokenizer"""]
		snake_case_     ="""BlipImageProcessor"""
		snake_case_     ="""AutoTokenizer"""



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						super().__init__(lowerCamelCase_		,lowerCamelCase_			)

						# add QFormer tokenizer
						lowerCAmelCase__					:    int												=					qformer_tokenizer



		def __call__(self		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = True		,__lowerCamelCase = False		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = 0		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = False		,__lowerCamelCase = True		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      BatchFeature:

						"""simple docstring"""






						if images is None and text is None:
										raise ValueError('''You have to specify at least images or text.'''			)

						lowerCAmelCase__					:    int												=					BatchFeature()

						if text is not None:
										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_		,)
										encoding.update(lowerCamelCase_			)
										lowerCAmelCase__					:    Any												=					self.qformer_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_		,)
										lowerCAmelCase__					:    List[Any]												=					qformer_text_encoding.pop('''input_ids'''			)
										lowerCAmelCase__					:    Optional[int]												=					qformer_text_encoding.pop('''attention_mask'''			)

						if images is not None:
										lowerCAmelCase__					:    str												=					self.image_processor(lowerCamelCase_		,return_tensors=lowerCamelCase_			)
										encoding.update(lowerCamelCase_			)

						return encoding



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						return self.tokenizer.batch_decode(*lowerCamelCase_		,**lowerCamelCase_			)



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						return self.tokenizer.decode(*lowerCamelCase_		,**lowerCamelCase_			)



		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					self.tokenizer.model_input_names
						lowerCAmelCase__					:    Any												=					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,**__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						if os.path.isfile(lowerCamelCase_			):
										raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""			)
						os.makedirs(lowerCamelCase_		,exist_ok=lowerCamelCase_			)
						lowerCAmelCase__					:    Optional[Any]												=					os.path.join(lowerCamelCase_		,'''qformer_tokenizer'''			)
						self.qformer_tokenizer.save_pretrained(lowerCamelCase_			)
						return super().save_pretrained(lowerCamelCase_		,**lowerCamelCase_			)



		@classmethod
		def 		lowerCAmelCase__				(cls		,__lowerCamelCase		,**__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					AutoTokenizer.from_pretrained(lowerCamelCase_		,subfolder='''qformer_tokenizer'''			)
						lowerCAmelCase__					:    str												=					cls._get_arguments_from_pretrained(lowerCamelCase_		,**lowerCamelCase_			)
						args.append(lowerCamelCase_			)
						return cls(*lowerCamelCase_			)






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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

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

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					get_lens(lowerCamelCase_)
				pickle_save(lowerCamelCase_	,train_ds.len_file)
				pickle_save(lowerCamelCase_	,val_ds.len_file)


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

