





import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__lowerCamelCase :  Any										=  16
__lowerCamelCase :  str										=  32



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase = 16  )		->				Any:
 UpperCamelCase			:      int 			=     AutoTokenizer.from_pretrained("bert-base-cased"  )
 UpperCamelCase			:      Optional[int] 			=     load_dataset("glue"							,     "mrpc"  )

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  UpperCamelCase			:      Any 			=     datasets.map(
      _lowerCAmelCase							,     batched=_lowerCAmelCase							,     remove_columns=["idx", "sentence1", "sentence2"]							,     )

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

 def collate_fn(_lowerCAmelCase  ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCamelCase			:      List[str] 			=     128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   UpperCamelCase			:      Dict 			=     16
  elif accelerator.mixed_precision != "no":
   UpperCamelCase			:      List[Any] 			=     8
  else:
   UpperCamelCase			:      Any 			=     None

  return tokenizer.pad(
      _lowerCAmelCase							,     padding="longest"							,     max_length=_lowerCAmelCase							,     pad_to_multiple_of=_lowerCAmelCase							,     return_tensors="pt"							,     )

 # Instantiate dataloaders.
 UpperCamelCase			:      Union[str, Any] 			=     DataLoader(
     tokenized_datasets["train"]							,     shuffle=_lowerCAmelCase							,     collate_fn=_lowerCAmelCase							,     batch_size=_lowerCAmelCase  )
 UpperCamelCase			:      Union[str, Any] 			=     DataLoader(
     tokenized_datasets["validation"]							,     shuffle=_lowerCAmelCase							,     collate_fn=_lowerCAmelCase							,     batch_size=_lowerCAmelCase  )

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    __lowerCamelCase :  str										=  mocked_dataloaders  # noqa: F811



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[int]:
 # For testing only
 if os.environ.get("TESTING_MOCKED_DATALOADERS"							,     _lowerCAmelCase  ) == "1":
  UpperCamelCase			:      List[Any] 			=     2
 # Initialize accelerator
 UpperCamelCase			:      Union[str, Any] 			=     Accelerator(cpu=args.cpu							,     mixed_precision=args.mixed_precision  )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCamelCase			:      Tuple 			=     config["lr"]
 UpperCamelCase			:      Optional[int] 			=     int(config["num_epochs"]  )
 UpperCamelCase			:      Any 			=     int(config["seed"]  )
 UpperCamelCase			:      Dict 			=     int(config["batch_size"]  )

 UpperCamelCase			:      Tuple 			=     evaluate.load("glue"							,     "mrpc"  )

 # If the batch size is too big we use gradient accumulation
 UpperCamelCase			:      int 			=     1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  UpperCamelCase			:      Optional[int] 			=     batch_size // MAX_GPU_BATCH_SIZE
  UpperCamelCase			:      int 			=     MAX_GPU_BATCH_SIZE

 set_seed(_lowerCAmelCase  )
 UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     get_dataloaders(_lowerCAmelCase							,     _lowerCAmelCase  )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase			:      Optional[int] 			=     AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							,     return_dict=_lowerCAmelCase  )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 UpperCamelCase			:      Tuple 			=     model.to(accelerator.device  )

 # Instantiate optimizer
 UpperCamelCase			:      List[str] 			=     AdamW(params=model.parameters()							,     lr=_lowerCAmelCase  )

 # Instantiate scheduler
 UpperCamelCase			:      Any 			=     get_linear_schedule_with_warmup(
     optimizer=_lowerCAmelCase							,     num_warmup_steps=100							,     num_training_steps=(len(_lowerCAmelCase  ) * num_epochs) // gradient_accumulation_steps							,     )

 # 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			:      Optional[Any] 			=     accelerator.prepare(
     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )

 # Now we train the model
 for epoch in range(_lowerCAmelCase  ):
  model.train()
  for step, batch in enumerate(_lowerCAmelCase  ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device  )
   UpperCamelCase			:      Any 			=     model(**_lowerCAmelCase  )
   UpperCamelCase			:      Optional[Any] 			=     outputs.loss
   UpperCamelCase			:      List[Any] 			=     loss / gradient_accumulation_steps
   accelerator.backward(_lowerCAmelCase  )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  UpperCamelCase			:      List[str] 			=     0
  for step, batch in enumerate(_lowerCAmelCase  ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device  )
   with torch.no_grad():
    UpperCamelCase			:      str 			=     model(**_lowerCAmelCase  )
   UpperCamelCase			:      List[str] 			=     outputs.logits.argmax(dim=-1  )
   UpperCamelCase			,					UpperCamelCase			:      Union[str, Any] 			=     accelerator.gather((predictions, batch["labels"])  )
   # New Code #
   # First we check if it's a distributed system
   if accelerator.use_distributed:
    # Then see if we're on the last batch of our eval dataloader
    if step == len(_lowerCAmelCase  ) - 1:
     # Last batch needs to be truncated on distributed systems as it contains additional samples
     UpperCamelCase			:      Union[str, Any] 			=     predictions[: len(eval_dataloader.dataset  ) - samples_seen]
     UpperCamelCase			:      Any 			=     references[: len(eval_dataloader.dataset  ) - samples_seen]
    else:
     # Otherwise we add the number of samples seen
     samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
   metric.add_batch(
       predictions=_lowerCAmelCase							,     references=_lowerCAmelCase							,     )

  UpperCamelCase			:      List[str] 			=     metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""							,     _lowerCAmelCase  )



def 	A_       (  )		->				Any:
 UpperCamelCase			:      Any 			=     argparse.ArgumentParser(description="Simple example of training script."  )
 parser.add_argument(
     "--mixed_precision"							,     type=_lowerCAmelCase							,     default=_lowerCAmelCase							,     choices=["no", "fp16", "bf16", "fp8"]							,     help="Whether to use mixed precision. Choose"
     "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
     "and an Nvidia Ampere GPU."							,     )
 parser.add_argument("--cpu"							,     action="store_true"							,     help="If passed, will train on the CPU."  )
 UpperCamelCase			:      Dict 			=     parser.parse_args()
 UpperCamelCase			:      Any 			=     {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(_lowerCAmelCase							,     _lowerCAmelCase  )


if __name__ == "__main__":
    main()







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[int]							=     ['image_processor', 'tokenizer']
      _UpperCAmelCase			:Tuple							=     'BlipImageProcessor'
      _UpperCAmelCase			:Optional[int]							=     'AutoTokenizer'





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     False
       super().__init__(A_ ,							A_     )
       UpperCamelCase			:      str 			=     self.image_processor





      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_ ,							):
       '''simple docstring'''




       if images is None and text is None:
        raise ValueError("You have to specify either images or text."     )

       # Get only text
       if images is None:
        UpperCamelCase			:      int 			=     self.tokenizer
        UpperCamelCase			:      Optional[int] 			=     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_ ,							)
        return text_encoding

       # add pixel_values
       UpperCamelCase			:      int 			=     self.image_processor(A_ ,							return_tensors=A_     )

       if text is not None:
        UpperCamelCase			:      Dict 			=     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_ ,							)
       else:
        UpperCamelCase			:      Dict 			=     None

       if text_encoding is not None:
        encoding_image_processor.update(A_     )

       return encoding_image_processor





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




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





      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.tokenizer.model_input_names
       UpperCamelCase			:      int 			=     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
