




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
# and perform 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
#
########################################################################


lowerCamelCase    :		str			   =       16
lowerCamelCase    :		str			   =       32

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Accelerator     ,							lowercase     :	int = 16					):


      '''simple docstring'''



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

      def tokenize_function(lowercase     :	Optional[Any]					):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase_							=   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
      # starting with the main process first:
      with accelerator.main_process_first():
            lowerCamelCase_							=   datasets.map(
                lowercase     ,							batched=lowercase     ,							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
      lowerCamelCase_							=   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.
            lowerCamelCase_							=   1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  lowerCamelCase_							=   16
            elif accelerator.mixed_precision != "no":
                  lowerCamelCase_							=   8
            else:
                  lowerCamelCase_							=   None

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

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

      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    :		Any			   =       mocked_dataloaders  # noqa: F811

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,							lowercase					) == "1":
            lowerCamelCase_							=   2
      # New Code #
      lowerCamelCase_							=   int(args.gradient_accumulation_steps					)
      # Initialize accelerator
      lowerCamelCase_							=   Accelerator(
          cpu=args.cpu     ,							mixed_precision=args.mixed_precision     ,							gradient_accumulation_steps=lowercase					)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'					)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase_							=   config['lr']
      lowerCamelCase_							=   int(config['num_epochs']					)
      lowerCamelCase_							=   int(config['seed']					)
      lowerCamelCase_							=   int(config['batch_size']					)

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

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

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

      # Instantiate optimizer
      lowerCamelCase_							=   AdamW(params=model.parameters()     ,							lr=lowercase					)

      # Instantiate scheduler
      lowerCamelCase_							=   get_linear_schedule_with_warmup(
          optimizer=lowercase     ,							num_warmup_steps=1_00     ,							num_training_steps=(len(lowercase					) * 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.
      lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   accelerator.prepare(
          lowercase     ,							lowercase     ,							lowercase     ,							lowercase     ,							lowercase					)

      # Now we train the model
      for epoch in range(lowercase					):
            model.train()
            for step, batch in enumerate(lowercase					):
                  # 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(lowercase					):
                        lowerCamelCase_							=   model(**lowercase					)
                        lowerCamelCase_							=   output.loss
                        accelerator.backward(lowercase					)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            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():
                        lowerCamelCase_							=   model(**lowercase					)
                  lowerCamelCase_							=   outputs.logits.argmax(dim=-1					)
                  lowerCamelCase_					,    lowerCamelCase_							=   accelerator.gather_for_metrics((predictions, batch['labels'])					)
                  metric.add_batch(
                      predictions=lowercase     ,							references=lowercase     ,							)

            lowerCamelCase_							=   metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:"""     ,							lowercase					)

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   argparse.ArgumentParser(description='Simple example of training script.'					)
      parser.add_argument(
          '--mixed_precision'     ,							type=lowercase     ,							default=lowercase     ,							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=lowercase     ,							default=1     ,							help='The number of minibatches to be ran before gradients are accumulated.'     ,							)
      parser.add_argument('--cpu'     ,							action='store_true'     ,							help='If passed, will train on the CPU.'					)
      lowerCamelCase_							=   parser.parse_args()
      lowerCamelCase_							=   {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(lowercase     ,							lowercase					)


if __name__ == "__main__":
 main()









import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''image_processor''', '''tokenizer''']
 UpperCamelCase						       =						'''OwlViTImageProcessor'''
 UpperCamelCase						       =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(    self :  Optional[Any]						,							A_ :  Tuple=None						,							A_ :  Tuple=None						,							**A_ :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'						,							A_						,							)
             lowerCamelCase_							=   kwargs.pop('feature_extractor'  )

       lowerCamelCase_							=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(A_						,							A_  )

 def __call__(    self :  List[str]						,							A_ :  List[str]=None						,							A_ :  List[Any]=None						,							A_ :  Dict=None						,							A_ :  Tuple="max_length"						,							A_ :  int="np"						,							**A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       if text is None and query_images is None and images is None:
             raise ValueError(
                 'You have to specify at least one text or query image or image. All three cannot be none.'  )

       if text is not None:
             if isinstance(A_						,							A_  ) or (isinstance(A_						,							A_  ) and not isinstance(text[0]						,							A_  )):
                   lowerCamelCase_							=   [self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )]

             elif isinstance(A_						,							A_  ) and isinstance(text[0]						,							A_  ):
                   lowerCamelCase_							=   []

                   # Maximum number of queries across batch
                   lowerCamelCase_							=   max([len(A_  ) for t in text]  )

                   # Pad all batch samples to max number of text queries
                   for t in text:
                         if len(A_  ) != max_num_queries:
                               lowerCamelCase_							=   t + [' '] * (max_num_queries - len(A_  ))

                         lowerCamelCase_							=   self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )
                         encodings.append(A_  )
             else:
                   raise TypeError('Input text should be a string, a list of strings or a nested list of strings'  )

             if return_tensors == "np":
                   lowerCamelCase_							=   np.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   np.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "jax" and is_flax_available():
                   import jax.numpy as jnp

                   lowerCamelCase_							=   jnp.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   jnp.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "pt" and is_torch_available():
                   import torch

                   lowerCamelCase_							=   torch.cat([encoding['input_ids'] for encoding in encodings]						,							dim=0  )
                   lowerCamelCase_							=   torch.cat([encoding['attention_mask'] for encoding in encodings]						,							dim=0  )

             elif return_tensors == "tf" and is_tf_available():
                   import tensorflow as tf

                   lowerCamelCase_							=   tf.stack([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   tf.stack([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             else:
                   raise ValueError('Target return tensor type could not be returned'  )

             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   input_ids
             lowerCamelCase_							=   attention_mask

       if query_images is not None:
             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   self.image_processor(
                 A_						,							return_tensors=A_						,							**A_  ).pixel_values
             lowerCamelCase_							=   query_pixel_values

       if images is not None:
             lowerCamelCase_							=   self.image_processor(A_						,							return_tensors=A_						,							**A_  )

       if text is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif query_images is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif text is not None or query_images is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**A_  )						,							tensor_type=A_  )

 def     a__			(    self :  Tuple						,							*A_ :  Dict						,							**A_ :  Dict  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process(*A_						,							**A_  )

 def     a__			(    self :  List[str]						,							*A_ :  Any						,							**A_ :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       return self.image_processor.post_process_object_detection(*A_						,							**A_  )

 def     a__			(    self :  Any						,							*A_ :  str						,							**A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process_image_guided_detection(*A_						,							**A_  )

 def     a__			(    self :  Union[str, Any]						,							*A_ :  Any						,							**A_ :  Union[str, Any]  )       ->     str:

       """simple docstring"""






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

 def     a__			(    self :  Optional[int]						,							*A_ :  List[Any]						,							**A_ :  int  )       ->     int:

       """simple docstring"""






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

 @property
 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,							A_						,							)
       return self.image_processor_class





 @property
 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,							A_						,							)
       return self.image_processor



