import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      Tuple      							=logging.get_logger(__name__)

_lowercase							:      Any      							={
    '''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json''',
    '''Salesforce/blip-vqa-capfit-large''': (
        '''https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-base''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-large''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'''
    ),
    '''Salesforce/blip-itm-base-coco''': '''https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-coco''': '''https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-base-flikr''': '''https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-flikr''': (
        '''https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'''
    ),
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"blip_text_model"







  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Tuple=3_05_24      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=7_68      ,	SCREAMING_SNAKE_CASE__    :					List[str]=7_68      ,	SCREAMING_SNAKE_CASE__    :					Any=30_72      ,	SCREAMING_SNAKE_CASE__    :					int=7_68      ,	SCREAMING_SNAKE_CASE__    :					List[str]=12      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=8      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=5_12      ,	SCREAMING_SNAKE_CASE__    :					Dict="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=1e-12      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					int=0.0      ,	SCREAMING_SNAKE_CASE__    :					str=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=3_05_22      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=0      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=1_02      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=True      ,	SCREAMING_SNAKE_CASE__    :					Any=True      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	)    ->				Union[str, Any]:
         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	sep_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)

         A						:     Dict         =vocab_size
         A						:     List[str]         =hidden_size
         A						:     str         =encoder_hidden_size
         A						:     int         =intermediate_size
         A						:     Optional[int]         =projection_dim
         A						:     Optional[int]         =hidden_dropout_prob
         A						:     Union[str, Any]         =num_hidden_layers
         A						:     int         =num_attention_heads
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Any         =layer_norm_eps
         A						:     List[Any]         =hidden_act
         A						:     List[Any]         =initializer_range
         A						:     List[Any]         =attention_probs_dropout_prob
         A						:     str         =is_decoder
         A						:     str         =use_cache







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Union[str, os.PathLike]      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]     )    ->				"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__     )

         A  ,  A						:     List[Any]         =cls.get_config_dict(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

         # get the text config dict if we are loading from BlipConfig
         if config_dict.get('model_type'     ) == "blip":
                A						:     List[str]         =config_dict['text_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(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Union[str, Any]            =				"blip_vision_model"







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[str]=7_68      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=30_72      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=5_12      ,	SCREAMING_SNAKE_CASE__    :					Any=12      ,	SCREAMING_SNAKE_CASE__    :					Any=12      ,	SCREAMING_SNAKE_CASE__    :					Dict=3_84      ,	SCREAMING_SNAKE_CASE__    :					Tuple=16      ,	SCREAMING_SNAKE_CASE__    :					Tuple="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Dict=1e-5      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Any=1e-10      ,	**SCREAMING_SNAKE_CASE__    :					List[Any]      ,	)    ->				Dict:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =hidden_size
         A						:     Optional[Any]         =intermediate_size
         A						:     Union[str, Any]         =projection_dim
         A						:     List[Any]         =num_hidden_layers
         A						:     List[Any]         =num_attention_heads
         A						:     Tuple         =patch_size
         A						:     List[Any]         =image_size
         A						:     Optional[Any]         =initializer_range
         A						:     Optional[Any]         =attention_dropout
         A						:     List[Any]         =layer_norm_eps
         A						:     int         =hidden_act







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, os.PathLike]      ,	**SCREAMING_SNAKE_CASE__    :					str     )    ->				"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__     )

         A  ,  A						:     List[Any]         =cls.get_config_dict(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )

         # get the vision config dict if we are loading from BlipConfig
         if config_dict.get('model_type'     ) == "blip":
                A						:     Optional[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(SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Tuple            =				"blip"
  lowercase		:     List[Any]            =				True







  def __init__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=None      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					Tuple=5_12      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=2.6_5_9_2      ,	SCREAMING_SNAKE_CASE__    :					Tuple=2_56      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	)    ->				Tuple:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         if text_config is None:
                A						:     Union[str, Any]         ={}
                logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'     )

         if vision_config is None:
                A						:     Any         ={}
                logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'     )

         A						:     List[Any]         =BlipTextConfig(**SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =BlipVisionConfig(**SCREAMING_SNAKE_CASE__     )

         A						:     Union[str, Any]         =self.vision_config.hidden_size

         A						:     Dict         =projection_dim
         A						:     List[str]         =logit_scale_init_value
         A						:     int         =1.0
         A						:     List[Any]         =0.0_2
         A						:     List[str]         =image_text_hidden_size







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					BlipTextConfig      ,	SCREAMING_SNAKE_CASE__    :					BlipVisionConfig      ,	**SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Optional[Any]:
         return cls(text_config=text_config.to_dict()      ,	vision_config=vision_config.to_dict()      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				List[str]:
         A						:     List[Any]         =copy.deepcopy(self.__dict__     )
         A						:     Any         =self.text_config.to_dict()
         A						:     int         =self.vision_config.to_dict()
         A						:     str         =self.__class__.model_type
         return output







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase							:      List[Any]      							=1_6
_lowercase							:      Union[str, Any]      							=3_2



def A__  (			lowercase:     Accelerator,		lowercase:     int = 16,		lowercase:     str = "bert-base-cased"					)				->		Optional[int]:
       A						:     List[Any]         =AutoTokenizer.from_pretrained(lowercase					)
       A						:     Any         =load_dataset('glue',		'mrpc'					)

       def tokenize_function(lowercase:     Any					):
              # max_length=None => use the model max length (it's actually the default)
              A						:     List[str]         =tokenizer(examples['sentence1'],		examples['sentence2'],		truncation=lowercase,		max_length=lowercase					)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       A						:     Any         =datasets.map(
           lowercase,		batched=lowercase,		remove_columns=['idx', 'sentence1', 'sentence2'],		load_from_cache_file=lowercase					)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       A						:     Dict         =tokenized_datasets.rename_column('label',		'labels'					)

       def collate_fn(lowercase:     Optional[int]					):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(lowercase,		padding='max_length',		max_length=128,		return_tensors='pt'					)
              return tokenizer.pad(lowercase,		padding='longest',		return_tensors='pt'					)

       # Instantiate dataloaders.
       A						:     Union[str, Any]         =DataLoader(
           tokenized_datasets['train'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)
       A						:     str         =DataLoader(
           tokenized_datasets['validation'],		shuffle=lowercase,		collate_fn=lowercase,		batch_size=lowercase					)

       return train_dataloader, eval_dataloader



def A__  (			lowercase:     Dict,		lowercase:     Optional[int],		lowercase:     Any,		lowercase:     str					)				->		Tuple:
       model.eval()
       A						:     Tuple         =0
       for step, batch in enumerate(lowercase					):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device					)
              with torch.no_grad():
                     A						:     Tuple         =model(**lowercase					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              # It is slightly faster to call this once, than multiple times
              A  ,  A						:     Union[str, Any]         =accelerator.gather(
                  (predictions, batch['labels'])					)  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(lowercase					) - 1:
                            A						:     List[Any]         =predictions[: len(eval_dataloader.dataset					) - samples_seen]
                            A						:     Optional[int]         =references[: len(eval_dataloader.dataset					) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase,		references=lowercase,		)

       A						:     Union[str, Any]         =metric.compute()
       return eval_metric["accuracy"]



def A__  (			lowercase:     Union[str, Any],		lowercase:     Dict					)				->		List[str]:
       # Initialize accelerator
       A						:     Optional[int]         =Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A						:     int         =config['lr']
       A						:     Optional[Any]         =int(config['num_epochs']					)
       A						:     Union[str, Any]         =int(config['seed']					)
       A						:     List[str]         =int(config['batch_size']					)
       A						:     Optional[Any]         =args.model_name_or_path

       set_seed(lowercase					)
       A  ,  A						:     str         =get_dataloaders(lowercase,		lowercase,		lowercase					)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A						:     List[str]         =AutoModelForSequenceClassification.from_pretrained(lowercase,		return_dict=lowercase					)

       # Instantiate optimizer
       A						:     Any         =(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       A						:     List[str]         =optimizer_cls(params=model.parameters(),		lr=lowercase					)

       if accelerator.state.deepspeed_plugin is not None:
              A						:     Optional[int]         =accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              A						:     Dict         =1
       A						:     Union[str, Any]         =(len(lowercase					) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              A						:     List[Any]         =get_linear_schedule_with_warmup(
                  optimizer=lowercase,		num_warmup_steps=0,		num_training_steps=lowercase,		)
       else:
              A						:     List[str]         =DummyScheduler(lowercase,		total_num_steps=lowercase,		warmup_num_steps=0					)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       A  ,  A  ,  A  ,  A  ,  A						:     Optional[int]         =accelerator.prepare(
           lowercase,		lowercase,		lowercase,		lowercase,		lowercase					)

       # We need to keep track of how many total steps we have iterated over
       A						:     Tuple         =0
       # We also need to keep track of the stating epoch so files are named properly
       A						:     List[str]         =0
       A						:     Tuple         =evaluate.load('glue',		'mrpc'					)
       A						:     Optional[int]         =num_epochs

       if args.partial_train_epoch is not None:
              A						:     Dict         =args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint					)
              A						:     List[Any]         =args.resume_from_checkpoint.split('epoch_'					)[1]
              A						:     List[Any]         =''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              A						:     Union[str, Any]         =int(lowercase					) + 1
              A						:     List[str]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              accelerator.print('resumed checkpoint performance:',		lowercase					)
              accelerator.print('resumed checkpoint\'s scheduler\'s lr:',		lr_scheduler.get_lr()[0]					)
              accelerator.print('resumed optimizers\'s lr:',		optimizer.param_groups[0]['lr']					)
              with open(os.path.join(args.output_dir,		F'state_{starting_epoch-1}.json'					),		'r'					) as f:
                     A						:     Union[str, Any]         =json.load(lowercase					)
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       A						:     str         ={}
       for epoch in range(lowercase,		lowercase					):
              model.train()
              for step, batch in enumerate(lowercase					):
                     A						:     Tuple         =model(**lowercase					)
                     A						:     List[Any]         =outputs.loss
                     A						:     Any         =loss / gradient_accumulation_steps
                     accelerator.backward(lowercase					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              A						:     Union[str, Any]         =F'epoch_{epoch}'
              A						:     Optional[Any]         =os.path.join(args.output_dir,		lowercase					)
              accelerator.save_state(lowercase					)
              A						:     Optional[Any]         =evaluation_loop(lowercase,		lowercase,		lowercase,		lowercase					)
              A						:     Dict         =accuracy
              A						:     Optional[Any]         =lr_scheduler.get_lr()[0]
              A						:     Any         =optimizer.param_groups[0]['lr']
              A						:     str         =epoch
              A						:     Dict         =overall_step
              accelerator.print(F'epoch {epoch}:',		lowercase					)

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir,		F'state_{epoch}.json'					),		'w'					) as f:
                            json.dump(lowercase,		lowercase					)



def A__  (			)				->		Optional[int]:
       A						:     Optional[int]         =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
       parser.add_argument(
           '--model_name_or_path',		type=lowercase,		default='bert-base-cased',		help='Path to pretrained model or model identifier from huggingface.co/models.',		required=lowercase,		)
       parser.add_argument(
           '--output_dir',		type=lowercase,		default='.',		help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',		)
       parser.add_argument(
           '--resume_from_checkpoint',		type=lowercase,		default=lowercase,		help='If the training should continue from a checkpoint folder.',		)
       parser.add_argument(
           '--partial_train_epoch',		type=lowercase,		default=lowercase,		help='If passed, the training will stop after this number of epochs.',		)
       parser.add_argument(
           '--num_epochs',		type=lowercase,		default=2,		help='Number of train epochs.',		)
       A						:     str         =parser.parse_args()
       A						:     Optional[int]         ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

       training_function(lowercase,		lowercase					)


if __name__ == "__main__":
    main()






