
'''simple docstring'''




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
#
########################################################################


lowercase			:				Union[str, Any]    		=			16
lowercase			:				Union[str, Any]    		=			32







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__ = 16						):




    '''simple docstring'''



    A       :   str					=						AutoTokenizer.from_pretrained('''bert-base-cased'''						)
    A       :   Optional[Any]					=						load_dataset('''glue'''		,				'''mrpc'''						)

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

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

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

    # Instantiate dataloaders.
    A       :   int					=						DataLoader(
        tokenized_datasets['''train''']		,				shuffle=snake_case__		,				collate_fn=snake_case__		,				batch_size=snake_case__						)
    A       :   Optional[int]					=						DataLoader(
        tokenized_datasets['''validation''']		,				shuffle=snake_case__		,				collate_fn=snake_case__		,				batch_size=snake_case__						)

    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

      lowercase			:				List[Any]    		=			mocked_dataloaders  # noqa: F811







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,				snake_case__						) == "1":
        A       :   int					=						2
    # New Code #
    A       :   Any					=						int(args.gradient_accumulation_steps						)
    A       :   Any					=						int(args.local_sgd_steps						)
    # Initialize accelerator
    A       :   List[str]					=						Accelerator(
        cpu=args.cpu		,				mixed_precision=args.mixed_precision		,				gradient_accumulation_steps=snake_case__						)
    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       :   Union[str, Any]					=						config['''lr''']
    A       :   Union[str, Any]					=						int(config['''num_epochs''']						)
    A       :   List[Any]					=						int(config['''seed''']						)
    A       :   Tuple					=						int(config['''batch_size''']						)

    A       :   Dict					=						evaluate.load('''glue'''		,				'''mrpc'''						)

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

    # 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       :   str					=						model.to(accelerator.device						)

    # Instantiate optimizer
    A       :   List[Any]					=						AdamW(params=model.parameters()		,				lr=snake_case__						)

    # Instantiate scheduler
    A       :   Any					=						get_linear_schedule_with_warmup(
        optimizer=snake_case__		,				num_warmup_steps=100		,				num_training_steps=(len(snake_case__						) * 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       :   List[Any]					=						accelerator.prepare(
        snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)

    # Now we train the model
    for epoch in range(snake_case__						):
        model.train()
        with LocalSGD(
            accelerator=snake_case__		,				model=snake_case__		,				local_sgd_steps=snake_case__		,				enabled=local_sgd_steps is not None						) as local_sgd:
            for step, batch in enumerate(snake_case__						):
                # 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(snake_case__						):
                    A       :   List[str]					=						model(**snake_case__						)
                    A       :   Union[str, Any]					=						output.loss
                    accelerator.backward(snake_case__						)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

        A       :   Union[str, Any]					=						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,				snake_case__						)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   Optional[int]					=						argparse.ArgumentParser(description='''Simple example of training script.'''						)
    parser.add_argument(
        '''--mixed_precision'''		,				type=snake_case__		,				default=snake_case__		,				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=snake_case__		,				default=1		,				help='''The number of minibatches to be ran before gradients are accumulated.'''		,				)
    parser.add_argument(
        '''--local_sgd_steps'''		,				type=snake_case__		,				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       :   Dict					=						parser.parse_args()
    A       :   Dict					=						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(snake_case__		,				snake_case__						)


if __name__ == "__main__":
      main()






'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		['''pixel_values''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 1 / 255						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				None:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE	)
         A       :   str					=						size if size is not None else {'''shortest_edge''': 384}
         A       :   Tuple					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   str					=						do_resize
         A       :   List[Any]					=						size
         # Default value set here for backwards compatibility where the value in config is None
         A       :   List[Any]					=						crop_pct if crop_pct is not None else 224 / 256
         A       :   Optional[int]					=						resample
         A       :   Union[str, Any]					=						do_rescale
         A       :   List[str]					=						rescale_factor
         A       :   Union[str, Any]					=						do_normalize
         A       :   Any					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A       :   Optional[int]					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         A       :   str					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
         if "shortest_edge" not in size:
             raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'	)
         A       :   Any					=						size['''shortest_edge''']

         if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             A       :   Dict					=						int(shortest_edge / crop_pct	)
             A       :   str					=						get_resize_output_image_size(SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
             A       :   int					=						resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         return rescale(SCREAMING_SNAKE_CASE						,			scale=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         return normalize(SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,			**SCREAMING_SNAKE_CASE						,			) ->				PIL.Image.Image:






         """simple docstring"""






         A       :   int					=						do_resize if do_resize is not None else self.do_resize
         A       :   Tuple					=						crop_pct if crop_pct is not None else self.crop_pct
         A       :   Optional[Any]					=						resample if resample is not None else self.resample
         A       :   List[Any]					=						do_rescale if do_rescale is not None else self.do_rescale
         A       :   List[Any]					=						rescale_factor if rescale_factor is not None else self.rescale_factor
         A       :   Union[str, Any]					=						do_normalize if do_normalize is not None else self.do_normalize
         A       :   Union[str, Any]					=						image_mean if image_mean is not None else self.image_mean
         A       :   List[str]					=						image_std if image_std is not None else self.image_std

         A       :   Union[str, Any]					=						size if size is not None else self.size
         A       :   List[Any]					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   Any					=						make_list_of_images(SCREAMING_SNAKE_CASE	)

         if not valid_images(SCREAMING_SNAKE_CASE	):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

         if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

         if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('''crop_pct must be specified if size < 384.'''	)

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

         # All transformations expect numpy arrays.
         A       :   Optional[int]					=						[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

         if do_resize:
             A       :   Any					=						[self.resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			crop_pct=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE	) for image in images]

         if do_rescale:
             A       :   str					=						[self.rescale(image=SCREAMING_SNAKE_CASE						,			scale=SCREAMING_SNAKE_CASE	) for image in images]

         if do_normalize:
             A       :   Dict					=						[self.normalize(image=SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Any					=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Optional[int]					=						{'''pixel_values''': images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE						,			tensor_type=SCREAMING_SNAKE_CASE	)




