





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


UpperCamelCase_												=	16
UpperCamelCase_												=	32






def 				_UpperCAmelCase			(    A				,							A = 16				,							A = "bert-base-cased"     ):




   '''simple docstring'''

   UpperCAmelCase__						=AutoTokenizer.from_pretrained(A     )
   UpperCAmelCase__						=load_dataset("glue"				,							"mrpc"     )

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

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

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

   def collate_fn(A     ):
      # 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(A				,							padding="max_length"				,							max_length=128				,							return_tensors="pt"     )
      return tokenizer.pad(A				,							padding="longest"				,							return_tensors="pt"     )

   # Instantiate dataloaders.
   UpperCAmelCase__						=DataLoader(
       tokenized_datasets["train"]				,							shuffle=A				,							collate_fn=A				,							batch_size=A     )
   UpperCAmelCase__						=DataLoader(
       tokenized_datasets["validation"]				,							shuffle=A				,							collate_fn=A				,							batch_size=A     )

   return train_dataloader, eval_dataloader






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCAmelCase__						=config["lr"]
   UpperCAmelCase__						=int(config["num_epochs"]     )
   UpperCAmelCase__						=int(config["seed"]     )
   UpperCAmelCase__						=int(config["batch_size"]     )
   UpperCAmelCase__						=args.model_name_or_path

   set_seed(A     )
   UpperCAmelCase__			,							UpperCAmelCase__						=get_dataloaders(A				,							A				,							A     )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCAmelCase__						=AutoModelForSequenceClassification.from_pretrained(A				,							return_dict=A     )

   # Instantiate optimizer
   UpperCAmelCase__						=(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   UpperCAmelCase__						=optimizer_cls(params=model.parameters()				,							lr=A     )

   if accelerator.state.deepspeed_plugin is not None:
      UpperCAmelCase__						=accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      UpperCAmelCase__						=1
   UpperCAmelCase__						=(len(A     ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      UpperCAmelCase__						=get_linear_schedule_with_warmup(
          optimizer=A				,							num_warmup_steps=0				,							num_training_steps=A				,							)
   else:
      UpperCAmelCase__						=DummyScheduler(A				,							total_num_steps=A				,							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.
   UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=accelerator.prepare(
       A				,							A				,							A				,							A				,							A     )

   # We need to keep track of how many total steps we have iterated over
   UpperCAmelCase__						=0
   # We also need to keep track of the stating epoch so files are named properly
   UpperCAmelCase__						=0

   # Now we train the model
   UpperCAmelCase__						=evaluate.load("glue"				,							"mrpc"     )
   UpperCAmelCase__						=0
   UpperCAmelCase__						={}
   for epoch in range(A				,							A     ):
      model.train()
      for step, batch in enumerate(A     ):
         UpperCAmelCase__						=model(**A     )
         UpperCAmelCase__						=outputs.loss
         UpperCAmelCase__						=loss / gradient_accumulation_steps
         accelerator.backward(A     )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

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

      UpperCAmelCase__						=metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""				,							A     )
      UpperCAmelCase__						=eval_metric["accuracy"]

      if best_performance < eval_metric["accuracy"]:
         UpperCAmelCase__						=eval_metric["accuracy"]

   if args.performance_lower_bound is not None:
      assert (
          args.performance_lower_bound <= best_performance
      ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir				,							"all_results.json"     )				,							"w"     ) as f:
         json.dump(A				,							A     )






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."     )
   parser.add_argument(
       "--model_name_or_path"				,							type=A				,							default="bert-base-cased"				,							help="Path to pretrained model or model identifier from huggingface.co/models."				,							required=A				,							)
   parser.add_argument(
       "--output_dir"				,							type=A				,							default="."				,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."				,							)
   parser.add_argument(
       "--performance_lower_bound"				,							type=A				,							default=A				,							help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."				,							)
   parser.add_argument(
       "--num_epochs"				,							type=A				,							default=3				,							help="Number of train epochs."				,							)
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						={"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
   training_function(A				,							A     )


if __name__ == "__main__":
    main()










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     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



