
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


A     :	Optional[int]    = logging.get_logger(__name__)

A     :	List[Any]    = {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}







class 			__A( a			):
				snake_case_						   =    '''instructblip_vision_model'''




				def __init__(     self		,     _snake_case=1_408		,     _snake_case=6_144		,     _snake_case=39		,     _snake_case=16		,     _snake_case=224		,     _snake_case=14		,     _snake_case="gelu"		,     _snake_case=1E-6		,     _snake_case=0.0		,     _snake_case=1E-10		,     _snake_case=True		,     **_snake_case		,     )    ->      List[str]:


								'''simple docstring'''




								super().__init__(**_snake_case				)

								__a										=    hidden_size
								__a										=    intermediate_size
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    patch_size
								__a										=    image_size
								__a										=    initializer_range
								__a										=    attention_dropout
								__a										=    layer_norm_eps
								__a										=    hidden_act
								__a										=    qkv_bias




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     **_snake_case				)    ->      "PretrainedConfig":


								'''simple docstring'''




								cls._set_token_in_kwargs(_snake_case				)

								__a							,     __a										=    cls.get_config_dict(_snake_case		,     **_snake_case				)

								# get the vision config dict if we are loading from InstructBlipConfig
								if config_dict.get('''model_type'''				) == "instructblip":
												__a										=    config_dict['''vision_config''']

								if "model_type" in config_dict and hasattr(cls		,     '''model_type'''				) and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
												    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

								return cls.from_dict(_snake_case		,     **_snake_case				)







class 			__A( a			):
				snake_case_						   =    '''instructblip_qformer'''




				def __init__(     self		,     _snake_case=30_522		,     _snake_case=768		,     _snake_case=12		,     _snake_case=12		,     _snake_case=3_072		,     _snake_case="gelu"		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=512		,     _snake_case=0.02		,     _snake_case=1E-12		,     _snake_case=0		,     _snake_case="absolute"		,     _snake_case=2		,     _snake_case=1_408		,     **_snake_case		,     )    ->      Dict:


								'''simple docstring'''




								super().__init__(pad_token_id=_snake_case		,     **_snake_case				)

								__a										=    vocab_size
								__a										=    hidden_size
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    hidden_act
								__a										=    intermediate_size
								__a										=    hidden_dropout_prob
								__a										=    attention_probs_dropout_prob
								__a										=    max_position_embeddings
								__a										=    initializer_range
								__a										=    layer_norm_eps
								__a										=    position_embedding_type
								__a										=    cross_attention_frequency
								__a										=    encoder_hidden_size




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     **_snake_case				)    ->      "PretrainedConfig":


								'''simple docstring'''




								cls._set_token_in_kwargs(_snake_case				)

								__a							,     __a										=    cls.get_config_dict(_snake_case		,     **_snake_case				)

								# get the qformer config dict if we are loading from InstructBlipConfig
								if config_dict.get('''model_type'''				) == "instructblip":
												__a										=    config_dict['''qformer_config''']

								if "model_type" in config_dict and hasattr(cls		,     '''model_type'''				) and config_dict["model_type"] != cls.model_type:
												logger.warning(
												    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
												    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

								return cls.from_dict(_snake_case		,     **_snake_case				)







class 			__A( a			):
				snake_case_						   =    '''instructblip'''
				snake_case_						   =    True




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case=32		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)

								if vision_config is None:
												__a										=    {}
												logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''				)

								if qformer_config is None:
												__a										=    {}
												logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''				)

								if text_config is None:
												__a										=    {}
												logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''				)

								__a										=    InstructBlipVisionConfig(**_snake_case				)
								__a										=    InstructBlipQFormerConfig(**_snake_case				)
								__a										=    text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
								__a										=    CONFIG_MAPPING[text_model_type](**_snake_case				)

								__a										=    self.text_config.tie_word_embeddings
								__a										=    self.text_config.is_encoder_decoder

								__a										=    num_query_tokens
								__a										=    self.vision_config.hidden_size
								__a										=    self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
								__a										=    1.0
								__a										=    0.02




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								return cls(
								    vision_config=vision_config.to_dict()		,     qformer_config=qformer_config.to_dict()		,     text_config=text_config.to_dict()		,     **_snake_case		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    copy.deepcopy(self.__dict__				)
								__a										=    self.vision_config.to_dict()
								__a										=    self.qformer_config.to_dict()
								__a										=    self.text_config.to_dict()
								__a										=    self.__class__.model_type
								return output

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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

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

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


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