



'''simple docstring'''

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_A				:    Union[str, Any]     						=logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_A				:    List[Any]     						={
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_A				:    Optional[int]     						={
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_A				:    Any     						=sorted(arg_to_scheduler.keys())
_A				:    Dict     						='''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class 		lowerCamelCase__      (						pl.LightningModule     ):




					'''simple docstring'''



					def __init__(  self				:	Dict     ,  UpperCamelCase_				:	argparse.Namespace     ,  UpperCamelCase_				:	List[str]=None     ,  UpperCamelCase_				:	List[str]="base"     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	Optional[int]=None     ,  **UpperCamelCase_				:	Tuple     ,  )  ->       Optional[int]:



						'''simple docstring'''





						super().__init__()
						# TODO: move to self.save_hyperparameters()
						# self.save_hyperparameters()
						# can also expand arguments into trainer signature for easier reading

						self.save_hyperparameters(UpperCamelCase_					)
						_lowercase					:       List[Any]       =		0
						_lowercase					:       Any       =		Path(self.hparams.output_dir					)
						_lowercase					:       str       =		self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
							_lowercase					:       List[Any]       =		AutoConfig.from_pretrained(
							    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,  **({'num_labels': num_labels} if num_labels is not None else {})     ,  cache_dir=UpperCamelCase_     ,  **UpperCamelCase_     ,  )
						else:
							_lowercase					:       PretrainedConfig       =		config

						_lowercase					:       Dict       =		('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
							if getattr(self.hparams     ,  UpperCamelCase_     ,  UpperCamelCase_					):
								assert hasattr(self.config     ,  UpperCamelCase_					), F'''model config doesn\'t have a `{p}` attribute'''
								setattr(self.config     ,  UpperCamelCase_     ,  getattr(self.hparams     ,  UpperCamelCase_					)					)

						if tokenizer is None:
							_lowercase					:       Union[str, Any]       =		AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,  cache_dir=UpperCamelCase_     ,  )
						else:
							_lowercase					:       PreTrainedTokenizer       =		tokenizer
						_lowercase					:       Any       =		MODEL_MODES[mode]
						if model is None:
							_lowercase					:       Union[str, Any]       =		self.model_type.from_pretrained(
							    self.hparams.model_name_or_path     ,  from_tf=bool('.ckpt' in self.hparams.model_name_or_path					)     ,  config=self.config     ,  cache_dir=UpperCamelCase_     ,  )
						else:
							_lowercase					:       str       =		model




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  *UpperCamelCase_				:	int     ,  **UpperCamelCase_				:	str					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.model_type.from_pretrained(*UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		arg_to_scheduler[self.hparams.lr_scheduler]
						_lowercase					:       List[str]       =		get_schedule_func(
						    self.opt     ,  num_warmup_steps=self.hparams.warmup_steps     ,  num_training_steps=self.total_steps()					)
						_lowercase					:       Optional[int]       =		{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
						return scheduler




					def 						__UpperCAmelCase     (  self				:	Any					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.model
						_lowercase					:       int       =		['bias', 'LayerNorm.weight']
						_lowercase					:       int       =		[
						    {
						        'params': [
						            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)
						        ],  # check this named paramters
						        'weight_decay': self.hparams.weight_decay,
						    },
						    {
						        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
						        'weight_decay': 0.0,
						    },
						]
						if self.hparams.adafactor:
							_lowercase					:       Dict       =		Adafactor(
							    UpperCamelCase_     ,  lr=self.hparams.learning_rate     ,  scale_parameter=UpperCamelCase_     ,  relative_step=UpperCamelCase_					)

						else:
							_lowercase					:       str       =		AdamW(
							    UpperCamelCase_     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon					)
						_lowercase					:       Any       =		optimizer

						_lowercase					:       str       =		self.get_lr_scheduler()

						return [optimizer], [scheduler]




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Optional[int]					)  ->       int:



						'''simple docstring'''





						return self.validation_step(UpperCamelCase_     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	str					)  ->       str:



						'''simple docstring'''





						return self.validation_end(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		max(1     ,  self.hparams.gpus					)  # TODO: consider num_tpu_cores
						_lowercase					:       Dict       =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
						return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	List[Any]					)  ->       str:



						'''simple docstring'''





						if stage == "test":
							_lowercase					:       str       =		len(self.test_dataloader().dataset					)
						else:
							_lowercase					:       Union[str, Any]       =		self.get_dataloader('train'     ,  self.hparams.train_batch_size     ,  shuffle=UpperCamelCase_					)
							_lowercase					:       str       =		len(self.train_dataloader().dataset					)




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	bool = False					)  ->       List[Any]:



						'''simple docstring'''





						raise NotImplementedError('You must implement this for your task'					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Union[str, Any]:



						'''simple docstring'''





						return self.train_loader




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Tuple:



						'''simple docstring'''





						return self.get_dataloader('dev'     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





						return self.get_dataloader('test'     ,  self.hparams.eval_batch_size     ,  shuffle=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





						return os.path.join(
						    self.hparams.data_dir     ,  'cached_{}_{}_{}'.format(
						        UpperCamelCase_     ,  list(filter(UpperCamelCase_     ,  self.hparams.model_name_or_path.split('/'					)					)					).pop()     ,  str(self.hparams.max_seq_length					)     ,  )     ,  )




					@pl.utilities.rank_zero_only
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Dict[str, Any]					)  ->       None:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.output_dir.joinpath('best_tfmr'					)
						_lowercase					:       List[str]       =		self.step_count
						self.model.save_pretrained(UpperCamelCase_					)
						self.tokenizer.save_pretrained(UpperCamelCase_					)




					@staticmethod
					def 						__UpperCAmelCase     (  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	List[Any]					)  ->       Optional[int]:



						'''simple docstring'''





						parser.add_argument(
						    '--model_name_or_path'     ,  default=UpperCamelCase_     ,  type=UpperCamelCase_     ,  required=UpperCamelCase_     ,  help='Path to pretrained model or model identifier from huggingface.co/models'     ,  )
						parser.add_argument(
						    '--config_name'     ,  default=''     ,  type=UpperCamelCase_     ,  help='Pretrained config name or path if not the same as model_name'					)
						parser.add_argument(
						    '--tokenizer_name'     ,  default=UpperCamelCase_     ,  type=UpperCamelCase_     ,  help='Pretrained tokenizer name or path if not the same as model_name'     ,  )
						parser.add_argument(
						    '--cache_dir'     ,  default=str(Path(UpperCamelCase_					).parent / 'test_run' / 'cache'					)     ,  type=UpperCamelCase_     ,  help='Where do you want to store the pre-trained models downloaded from huggingface.co'     ,  )
						parser.add_argument(
						    '--encoder_layerdrop'     ,  type=UpperCamelCase_     ,  help='Encoder layer dropout probability (Optional). Goes into model.config'     ,  )
						parser.add_argument(
						    '--decoder_layerdrop'     ,  type=UpperCamelCase_     ,  help='Decoder layer dropout probability (Optional). Goes into model.config'     ,  )
						parser.add_argument(
						    '--dropout'     ,  type=UpperCamelCase_     ,  help='Dropout probability (Optional). Goes into model.config'     ,  )
						parser.add_argument(
						    '--attention_dropout'     ,  type=UpperCamelCase_     ,  help='Attention dropout probability (Optional). Goes into model.config'     ,  )
						parser.add_argument('--learning_rate'     ,  default=5E-5     ,  type=UpperCamelCase_     ,  help='The initial learning rate for Adam.'					)
						parser.add_argument(
						    '--lr_scheduler'     ,  default='linear'     ,  choices=UpperCamelCase_     ,  metavar=UpperCamelCase_     ,  type=UpperCamelCase_     ,  help='Learning rate scheduler'     ,  )
						parser.add_argument('--weight_decay'     ,  default=0.0     ,  type=UpperCamelCase_     ,  help='Weight decay if we apply some.'					)
						parser.add_argument('--adam_epsilon'     ,  default=1E-8     ,  type=UpperCamelCase_     ,  help='Epsilon for Adam optimizer.'					)
						parser.add_argument('--warmup_steps'     ,  default=0     ,  type=UpperCamelCase_     ,  help='Linear warmup over warmup_steps.'					)
						parser.add_argument('--num_workers'     ,  default=4     ,  type=UpperCamelCase_     ,  help='kwarg passed to DataLoader'					)
						parser.add_argument('--num_train_epochs'     ,  dest='max_epochs'     ,  default=3     ,  type=UpperCamelCase_					)
						parser.add_argument('--train_batch_size'     ,  default=32     ,  type=UpperCamelCase_					)
						parser.add_argument('--eval_batch_size'     ,  default=32     ,  type=UpperCamelCase_					)
						parser.add_argument('--adafactor'     ,  action='store_true'					)






class 		lowerCamelCase__      (						pl.Callback     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	List[Any]					)  ->       str:



						'''simple docstring'''





						if (
						    trainer.is_global_zero and trainer.global_rank == 0
						):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 		lowerCamelCase__      (						pl.Callback     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						for name, param in pl_module.model.rag.named_parameters():
							if param.grad is None:
								print(UpperCamelCase_					)






class 		lowerCamelCase__      (						pl.Callback     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	List[str]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		trainer.lr_schedulers[0]['scheduler']
						_lowercase					:       Union[str, Any]       =		{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
						pl_module.logger.log_metrics(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	pl.Trainer     ,  UpperCamelCase_				:	pl.LightningModule					)  ->       Any:



						'''simple docstring'''





						rank_zero_info('***** Validation results *****'					)
						_lowercase					:       Optional[int]       =		trainer.callback_metrics
						# Log results
						for key in sorted(UpperCamelCase_					):
							if key not in ["log", "progress_bar"]:
								rank_zero_info('{} = {}\n'.format(UpperCamelCase_     ,  str(metrics[key]					)					)					)




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	pl.Trainer     ,  UpperCamelCase_				:	pl.LightningModule					)  ->       List[str]:



						'''simple docstring'''





						rank_zero_info('***** Test results *****'					)
						_lowercase					:       int       =		trainer.callback_metrics
						# Log and save results to file
						_lowercase					:       List[Any]       =		os.path.join(pl_module.hparams.output_dir     ,  'test_results.txt'					)
						with open(UpperCamelCase_     ,  'w'					) as writer:
							for key in sorted(UpperCamelCase_					):
								if key not in ["log", "progress_bar"]:
									rank_zero_info('{} = {}\n'.format(UpperCamelCase_     ,  str(metrics[key]					)					)					)
									writer.write('{} = {}\n'.format(UpperCamelCase_     ,  str(metrics[key]					)					)					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			None:
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    '--output_dir',				default=str(Path(_lowercase					).parent / 'test_run' / 'model_checkpoints'					),				type=_lowercase,				help='The output directory where the model predictions and checkpoints will be written.',				)
	parser.add_argument(
	    '--fp16',				action='store_true',				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit',				)

	parser.add_argument(
	    '--fp16_opt_level',				type=_lowercase,				default='O2',				help=(
	        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
	        'See details at https://nvidia.github.io/apex/amp.html'
	    ),				)
	parser.add_argument('--n_tpu_cores',				dest='tpu_cores',				type=_lowercase					)
	parser.add_argument('--max_grad_norm',				dest='gradient_clip_val',				default=1.0,				type=_lowercase,				help='Max gradient norm'					)
	parser.add_argument('--do_train',				action='store_true',				help='Whether to run training.'					)
	parser.add_argument('--do_predict',				action='store_true',				help='Whether to run predictions on the test set.'					)
	parser.add_argument(
	    '--gradient_accumulation_steps',				dest='accumulate_grad_batches',				type=_lowercase,				default=1,				help='Number of updates steps to accumulate before performing a backward/update pass.',				)
	parser.add_argument('--seed',				type=_lowercase,				default=42,				help='random seed for initialization'					)
	parser.add_argument(
	    '--data_dir',				default=str(Path(_lowercase					).parent / 'test_run' / 'dummy-train-data'					),				type=_lowercase,				help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.',				)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=None,				_lowercase=True,				_lowercase=[],				_lowercase=None,				_lowercase=None,				**_lowercase,				) ->			int:
	pl.seed_everything(args.seed					)

	# init model
	_lowercase					:       Any       =		Path(model.hparams.output_dir					)
	odir.mkdir(exist_ok=_lowercase					)

	# add custom checkpoints
	if checkpoint_callback is None:
		_lowercase					:       str       =		pl.callbacks.ModelCheckpoint(
		    filepath=args.output_dir,				prefix='checkpoint',				monitor='val_loss',				mode='min',				save_top_k=1					)
	if early_stopping_callback:
		extra_callbacks.append(_lowercase					)
	if logging_callback is None:
		_lowercase					:       str       =		LoggingCallback()

	_lowercase					:       Optional[int]       =		{}

	if args.fpaa:
		_lowercase					:       Any       =		16

	if args.gpus > 1:
		_lowercase					:       List[Any]       =		'auto'
		_lowercase					:       List[Any]       =		'ddp'

	_lowercase					:       Dict       =		args.accumulate_grad_batches
	_lowercase					:       List[str]       =		None
	_lowercase					:       List[str]       =		'auto'

	_lowercase					:       Any       =		pl.Trainer.from_argparse_args(
	    _lowercase,				weights_summary=_lowercase,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback],				logger=_lowercase,				val_check_interval=1,				num_sanity_val_steps=2,				**_lowercase,				)

	if args.do_train:
		trainer.fit(_lowercase					)

	else:
		print('RAG modeling tests with new set functions successfuly executed!'					)
	return trainer





'''simple docstring'''

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				:    int     						=logging.get_logger(__name__)

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



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_vision_model"""




					def __init__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	str=1408     ,  UpperCamelCase_				:	Tuple=6144     ,  UpperCamelCase_				:	Union[str, Any]=39     ,  UpperCamelCase_				:	Optional[Any]=16     ,  UpperCamelCase_				:	str=224     ,  UpperCamelCase_				:	Dict=14     ,  UpperCamelCase_				:	Dict="gelu"     ,  UpperCamelCase_				:	int=1E-6     ,  UpperCamelCase_				:	int=0.0     ,  UpperCamelCase_				:	List[str]=1E-10     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		num_hidden_layers
						_lowercase					:       str       =		num_attention_heads
						_lowercase					:       Tuple       =		patch_size
						_lowercase					:       Dict       =		image_size
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       List[Any]       =		attention_dropout
						_lowercase					:       int       =		layer_norm_eps
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       str       =		qkv_bias




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Tuple       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Any       =		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(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_qformer"""




					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]=3_0522     ,  UpperCamelCase_				:	Union[str, Any]=768     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	Optional[Any]=12     ,  UpperCamelCase_				:	List[str]=3072     ,  UpperCamelCase_				:	List[str]="gelu"     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	List[str]=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	List[Any]=1E-12     ,  UpperCamelCase_				:	Optional[Any]=0     ,  UpperCamelCase_				:	str="absolute"     ,  UpperCamelCase_				:	List[Any]=2     ,  UpperCamelCase_				:	Any=1408     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





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

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       List[Any]       =		num_attention_heads
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       Union[str, Any]       =		intermediate_size
						_lowercase					:       List[Any]       =		hidden_dropout_prob
						_lowercase					:       Dict       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       Tuple       =		layer_norm_eps
						_lowercase					:       List[str]       =		position_embedding_type
						_lowercase					:       str       =		cross_attention_frequency
						_lowercase					:       int       =		encoder_hidden_size




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       List[str]       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Optional[int]       =		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(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip"""
					A_						    =						True




					def __init__(  self				:	Any     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	Dict=None     ,  UpperCamelCase_				:	Union[str, Any]=32     ,  **UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

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

						if qformer_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

						if text_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

						_lowercase					:       List[Any]       =		InstructBlipVisionConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		InstructBlipQFormerConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		text_config['model_type'] if 'model_type' in text_config else 'opt'
						_lowercase					:       int       =		CONFIG_MAPPING[text_model_type](**UpperCamelCase_					)

						_lowercase					:       str       =		self.text_config.tie_word_embeddings
						_lowercase					:       int       =		self.text_config.is_encoder_decoder

						_lowercase					:       Tuple       =		num_query_tokens
						_lowercase					:       str       =		self.vision_config.hidden_size
						_lowercase					:       Union[str, Any]       =		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						_lowercase					:       List[Any]       =		1.0
						_lowercase					:       int       =		0.02




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Tuple     ,  UpperCamelCase_				:	InstructBlipVisionConfig     ,  UpperCamelCase_				:	InstructBlipQFormerConfig     ,  UpperCamelCase_				:	PretrainedConfig     ,  **UpperCamelCase_				:	Dict     ,  )  ->       List[str]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		copy.deepcopy(self.__dict__					)
						_lowercase					:       Optional[int]       =		self.vision_config.to_dict()
						_lowercase					:       Optional[Any]       =		self.qformer_config.to_dict()
						_lowercase					:       Tuple       =		self.text_config.to_dict()
						_lowercase					:       Dict       =		self.__class__.model_type
						return output
