




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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


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







class 		__UpperCAmelCase(		__A			):





     """simple docstring"""



     __UpperCAmelCase						:			List[str]							=     ['pixel_values']

     def __init__(   self			:							int    ,					A			:							Optional[Any] = True    ,					A			:							Dict = None    ,					A			:							Union[str, Any] = True    ,					A			:							List[Any] = None    ,					A			:							Any = PILImageResampling.BICUBIC    ,					A			:							Optional[int] = True    ,					A			:							Dict = 1 / 2_55    ,					A			:							str = True    ,					A			:							Dict = None    ,					A			:							Tuple = None    ,					**A			:							Optional[Any]    ,					)      ->      Union[str, Any]:






         """simple docstring"""
         super().__init__(**__lowercase     )
         a__  			=							crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
         a__  			=							get_size_dict(__lowercase    ,					param_name="""crop_size"""     )
         a__  			=							size if size is not None else {"""height""": 2_24, """width""": 2_24}
         a__  			=							get_size_dict(__lowercase     )

         a__  			=							do_center_crop
         a__  			=							crop_size
         a__  			=							do_resize
         a__  			=							size
         a__  			=							resample
         a__  			=							do_rescale
         a__  			=							rescale_factor
         a__  			=							do_normalize
         a__  			=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         a__  			=							image_std if image_std is not None else IMAGENET_DEFAULT_STD

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							List[str]    ,					A			:							str    ,					A			:							int = None    ,					A			:							Optional[Any] = None    ,					**A			:							str    ,					)      ->      Any:






         """simple docstring"""
         a__  			=							self.size if size is None else size
         a__  			=							get_size_dict(__lowercase     )
         a__  			=							get_size_dict(__lowercase    ,					param_name="""crop_size"""     )

         a__			,		a__  			=							get_image_size(__lowercase     )
         a__  			=							min(__lowercase    ,					__lowercase     )
         a__  			=							(size["""height"""] / crop_size["""height"""]) * min_dim
         a__  			=							(size["""width"""] / crop_size["""width"""]) * min_dim
         return center_crop(__lowercase    ,					size=(cropped_height, cropped_width)    ,					data_format=__lowercase    ,					**__lowercase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Optional[int]    ,					A			:							str    ,					A			:							List[str] = PIL.Image.BICUBIC    ,					A			:							Dict = None    ,					**A			:							str    ,					)      ->      Tuple:






         """simple docstring"""
         a__  			=							get_size_dict(__lowercase     )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""     )
         return resize(
             __lowercase    ,					size=(size["""height"""], size["""width"""])    ,					resample=__lowercase    ,					data_format=__lowercase    ,					**__lowercase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Optional[int]    ,					A			:							List[Any]    ,					A			:							Optional[int] = None    ,					**A			:							List[str]    ,					)      ->      Union[str, Any]:






         """simple docstring"""
         return rescale(__lowercase    ,					scale=__lowercase    ,					data_format=__lowercase    ,					**__lowercase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]    ,					A			:							Dict    ,					A			:							str    ,					A			:							Union[str, Any]    ,					A			:							str = None    ,					**A			:							Optional[int]    ,					)      ->      str:






         """simple docstring"""
         return normalize(__lowercase    ,					mean=__lowercase    ,					std=__lowercase    ,					data_format=__lowercase    ,					**__lowercase     )






     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							Dict    ,					A			:							List[Any] = None    ,					A			:							List[Any] = None    ,					A			:							Dict = None    ,					A			:							Dict = None    ,					A			:							Tuple = None    ,					A			:							List[Any] = None    ,					A			:							List[Any] = None    ,					A			:							Optional[Any] = None    ,					A			:							Any = None    ,					A			:							List[Any] = None    ,					A			:							Any = None    ,					A			:							Optional[int] = ChannelDimension.FIRST    ,					**A			:							Optional[Any]    ,					)      ->      int:






         """simple docstring"""
         a__  			=							do_center_crop if do_center_crop is not None else self.do_center_crop
         a__  			=							crop_size if crop_size is not None else self.crop_size
         a__  			=							get_size_dict(__lowercase    ,					param_name="""crop_size"""     )
         a__  			=							do_resize if do_resize is not None else self.do_resize
         a__  			=							size if size is not None else self.size
         a__  			=							get_size_dict(__lowercase     )
         a__  			=							resample if resample is not None else self.resample
         a__  			=							do_rescale if do_rescale is not None else self.do_rescale
         a__  			=							rescale_factor if rescale_factor is not None else self.rescale_factor
         a__  			=							do_normalize if do_normalize is not None else self.do_normalize
         a__  			=							image_mean if image_mean is not None else self.image_mean
         a__  			=							image_std if image_std is not None else self.image_std

         a__  			=							make_list_of_images(__lowercase     )

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

         if do_center_crop and crop_size is None:
             raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""     )

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

         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 image standard deviation must be specified if do_normalize is True."""     )

         # All transformations expect numpy arrays.
         a__  			=							[to_numpy_array(__lowercase     ) for image in images]

         if do_center_crop:
             a__  			=							[self.center_crop(__lowercase    ,					__lowercase    ,					size=__lowercase     ) for image in images]

         if do_resize:
             a__  			=							[self.resize(image=__lowercase    ,					size=__lowercase    ,					resample=__lowercase     ) for image in images]

         if do_rescale:
             a__  			=							[self.rescale(image=__lowercase    ,					scale=__lowercase     ) for image in images]

         if do_normalize:
             a__  			=							[self.normalize(image=__lowercase    ,					mean=__lowercase    ,					std=__lowercase     ) for image in images]

         a__  			=							[to_channel_dimension_format(__lowercase    ,					__lowercase     ) for image in images]

         a__  			=							{"""pixel_values""": images}
         return BatchFeature(data=__lowercase    ,					tensor_type=__lowercase     )










'''simple docstring'''

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

_UpperCAmelCase					:			List[Any]				     =					3_2

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase = 1_6):
						__lowerCAmelCase											=					AutoTokenizer.from_pretrained('''bert-base-cased''')
						__lowerCAmelCase											=					load_dataset('''glue''',					'''mrpc''')

						def tokenize_function(lowerCamelCase):
												# max_length=None => use the model max length (it's actually the default)
												__lowerCAmelCase											=					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():
												__lowerCAmelCase											=					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
						__lowerCAmelCase											=					tokenized_datasets.rename_column('''label''',					'''labels''')

						def collate_fn(lowerCamelCase):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase											=					1_6
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase											=					8
												else:
																		__lowerCAmelCase											=					None

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

						# Instantiate dataloaders.
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''train'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)
						__lowerCAmelCase											=					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

	_UpperCAmelCase					:			Optional[int]				     =					mocked_dataloaders  # noqa: F811

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS''',					lowerCamelCase) == "1":
												__lowerCAmelCase											=					2
						# Initialize accelerator
						__lowerCAmelCase											=					Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision)
						# 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''')

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(lowerCamelCase))
						def inner_training_loop(lowerCamelCase):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												__lowerCAmelCase											=					observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												__lowerCAmelCase											=					num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(lowerCamelCase)

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

												# Instantiate optimizer
												__lowerCAmelCase											=					AdamW(params=model.parameters(),					lr=lowerCamelCase)
												__lowerCAmelCase							,       __lowerCAmelCase											=					get_dataloaders(lowerCamelCase,					lowerCamelCase)

												# Instantiate scheduler
												__lowerCAmelCase											=					get_linear_schedule_with_warmup(
												    optimizer=lowerCamelCase,					num_warmup_steps=1_0_0,					num_training_steps=(len(lowerCamelCase) * 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(
												    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

												# Now we train the model
												for epoch in range(lowerCamelCase):
																		model.train()
																		for step, batch in enumerate(lowerCamelCase):
																								# And perform gradient accumulation
																								with accelerator.accumulate(lowerCamelCase):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device)
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																														__lowerCAmelCase											=					outputs.loss
																														accelerator.backward(lowerCamelCase)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()

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

																		__lowerCAmelCase											=					metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F"""epoch {epoch}:""",					lowerCamelCase)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					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.''')
						__lowerCAmelCase											=					parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						__lowerCAmelCase											=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
						training_function(lowerCamelCase,					lowerCamelCase)


if __name__ == "__main__":
	main()

