


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       lowercase_					(_SCREAMING_SNAKE_CASE   ):
				snake_case	    =["""image_processor""", """tokenizer"""]
				snake_case	    ="""BlipImageProcessor"""
				snake_case	    =("""BertTokenizer""", """BertTokenizerFast""")





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> List[Any]:
											a__      =False
											super().__init__(__snake_case       ,  __snake_case)
											a__      =self.image_processor





				def __call__(				self       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = True       ,  lowercase_ = False       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = 0       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = False       ,  lowercase_ = False       ,  lowercase_ = False       ,  lowercase_ = False       ,  lowercase_ = False       ,  lowercase_ = True       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> List[str]:





											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:
																		a__      =self.tokenizer
																		a__      =self.tokenizer(
																		    text=__snake_case       ,  add_special_tokens=__snake_case       ,  padding=__snake_case       ,  truncation=__snake_case       ,  max_length=__snake_case       ,  stride=__snake_case       ,  pad_to_multiple_of=__snake_case       ,  return_attention_mask=__snake_case       ,  return_overflowing_tokens=__snake_case       ,  return_special_tokens_mask=__snake_case       ,  return_offsets_mapping=__snake_case       ,  return_token_type_ids=__snake_case       ,  return_length=__snake_case       ,  verbose=__snake_case       ,  return_tensors=__snake_case       ,  **__snake_case       ,  )
																		return text_encoding

											# add pixel_values
											a__      =self.image_processor(__snake_case       ,  return_tensors=__snake_case)

											if text is not None:
																		a__      =self.tokenizer(
																		    text=__snake_case       ,  add_special_tokens=__snake_case       ,  padding=__snake_case       ,  truncation=__snake_case       ,  max_length=__snake_case       ,  stride=__snake_case       ,  pad_to_multiple_of=__snake_case       ,  return_attention_mask=__snake_case       ,  return_overflowing_tokens=__snake_case       ,  return_special_tokens_mask=__snake_case       ,  return_offsets_mapping=__snake_case       ,  return_token_type_ids=__snake_case       ,  return_length=__snake_case       ,  verbose=__snake_case       ,  return_tensors=__snake_case       ,  **__snake_case       ,  )
											else:
																		a__      =None

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

											return encoding_image_processor





				def 						__UpperCamelCase    (				self       ,  *lowercase_       ,  **lowercase_)  -> str:
											return self.tokenizer.batch_decode(*__snake_case       ,  **__snake_case)





				def 						__UpperCamelCase    (				self       ,  *lowercase_       ,  **lowercase_)  -> str:
											return self.tokenizer.decode(*__snake_case       ,  **__snake_case)





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =self.tokenizer.model_input_names
											a__      =self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))








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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a__      :     Any         =		16
a__      :     str         =		32

def      UpperCAmelCase_    (    _UpperCAmelCase							:Accelerator  ,						_UpperCAmelCase							:int = 16				)	->				int:
     '''simple docstring'''


     A_          =       AutoTokenizer.from_pretrained('''bert-base-cased'''				)
     A_          =       load_dataset('''glue'''  ,						'''mrpc'''				)

     def tokenize_function(_UpperCAmelCase							:Optional[Any]				):
          # max_length=None => use the model max length (it's actually the default)
          A_          =       tokenizer(examples['''sentence1''']  ,						examples['''sentence2''']  ,						truncation=_UpperCAmelCase  ,						max_length=_UpperCAmelCase				)
          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():
          A_          =       datasets.map(
              _UpperCAmelCase  ,						batched=_UpperCAmelCase  ,						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
     A_          =       tokenized_datasets.rename_column('''label'''  ,						'''labels'''				)

     def collate_fn(_UpperCAmelCase							:List[Any]				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A_          =       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":
               A_          =       16
          elif accelerator.mixed_precision != "no":
               A_          =       8
          else:
               A_          =       None

          return tokenizer.pad(
              _UpperCAmelCase  ,						padding='''longest'''  ,						max_length=_UpperCAmelCase  ,						pad_to_multiple_of=_UpperCAmelCase  ,						return_tensors='''pt'''  ,						)

     # Instantiate dataloaders.
     A_          =       DataLoader(
         tokenized_datasets['''train''']  ,						shuffle=_UpperCAmelCase  ,						collate_fn=_UpperCAmelCase  ,						batch_size=_UpperCAmelCase				)
     A_          =       DataLoader(
         tokenized_datasets['''validation''']  ,						shuffle=_UpperCAmelCase  ,						collate_fn=_UpperCAmelCase  ,						batch_size=_UpperCAmelCase				)

     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

      a__      :     Optional[Any]         =		mocked_dataloaders  # noqa: F811

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]  ,						_UpperCAmelCase							:Dict				)	->				Dict:
     '''simple docstring'''


     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,						_UpperCAmelCase				) == "1":
          A_          =       2
     # New Code #
     A_          =       int(args.gradient_accumulation_steps				)
     A_          =       int(args.local_sgd_steps				)
     # Initialize accelerator
     A_          =       Accelerator(
         cpu=args.cpu  ,						mixed_precision=args.mixed_precision  ,						gradient_accumulation_steps=_UpperCAmelCase				)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A_          =       config['''lr''']
     A_          =       int(config['''num_epochs''']				)
     A_          =       int(config['''seed''']				)
     A_          =       int(config['''batch_size''']				)

     A_          =       evaluate.load('''glue'''  ,						'''mrpc'''				)

     set_seed(_UpperCAmelCase				)
     A_   ,    A_          =       get_dataloaders(_UpperCAmelCase  ,						_UpperCAmelCase				)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A_          =       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  ,						return_dict=_UpperCAmelCase				)

     # 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).
     A_          =       model.to(accelerator.device				)

     # Instantiate optimizer
     A_          =       AdamW(params=model.parameters()  ,						lr=_UpperCAmelCase				)

     # Instantiate scheduler
     A_          =       get_linear_schedule_with_warmup(
         optimizer=_UpperCAmelCase  ,						num_warmup_steps=100  ,						num_training_steps=(len(_UpperCAmelCase				) * num_epochs)  ,						)

     # 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_          =       accelerator.prepare(
         _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # Now we train the model
     for epoch in range(_UpperCAmelCase				):
          model.train()
          with LocalSGD(
              accelerator=_UpperCAmelCase  ,						model=_UpperCAmelCase  ,						local_sgd_steps=_UpperCAmelCase  ,						enabled=local_sgd_steps is not None				) as local_sgd:
               for step, batch in enumerate(_UpperCAmelCase				):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device				)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(_UpperCAmelCase				):
                         A_          =       model(**_UpperCAmelCase				)
                         A_          =       output.loss
                         accelerator.backward(_UpperCAmelCase				)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          model.eval()
          for step, batch in enumerate(_UpperCAmelCase				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device				)
               with torch.no_grad():
                    A_          =       model(**_UpperCAmelCase				)
               A_          =       outputs.logits.argmax(dim=-1				)
               A_   ,    A_          =       accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
               metric.add_batch(
                   predictions=_UpperCAmelCase  ,						references=_UpperCAmelCase  ,						)

          A_          =       metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       argparse.ArgumentParser(description='''Simple example of training script.'''				)
     parser.add_argument(
         '''--mixed_precision'''  ,						type=_UpperCAmelCase  ,						default=_UpperCAmelCase  ,						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.'''  ,						)
     # New Code #
     parser.add_argument(
         '''--gradient_accumulation_steps'''  ,						type=_UpperCAmelCase  ,						default=1  ,						help='''The number of minibatches to be ran before gradients are accumulated.'''  ,						)
     parser.add_argument(
         '''--local_sgd_steps'''  ,						type=_UpperCAmelCase  ,						default=8  ,						help='''Number of local SGD steps or None to disable local SGD'''				)
     parser.add_argument('''--cpu'''  ,						action='''store_true'''  ,						help='''If passed, will train on the CPU.'''				)
     A_          =       parser.parse_args()
     A_          =       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_UpperCAmelCase  ,						_UpperCAmelCase				)


if __name__ == "__main__":
      main()



