



import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase__			= 16
lowerCamelCase__			= 32

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Accelerator				,     _SCREAMING_SNAKE_CASE       :			int = 16				,     _SCREAMING_SNAKE_CASE       :			str = "bert-base-cased"    ):
				"""simple docstring"""







				__a       =		AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
				__a       =		load_dataset("""glue"""				,     """mrpc"""    )

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				__a       =		datasets.map(
				    _SCREAMING_SNAKE_CASE				,     batched=_SCREAMING_SNAKE_CASE				,     remove_columns=["""idx""", """sentence1""", """sentence2"""]				,     load_from_cache_file=_SCREAMING_SNAKE_CASE    )

				# 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(_SCREAMING_SNAKE_CASE       :			int    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(_SCREAMING_SNAKE_CASE				,     padding="""max_length"""				,     max_length=128				,     return_tensors="""pt"""    )
								return tokenizer.pad(_SCREAMING_SNAKE_CASE				,     padding="""longest"""				,     return_tensors="""pt"""    )

				# Instantiate dataloaders.
				__a       =		DataLoader(
				    tokenized_datasets["""train"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE    )
				__a       =		DataLoader(
				    tokenized_datasets["""validation"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE    )

				return train_dataloader, eval_dataloader

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]    ):
				"""simple docstring"""







				__a       =		Accelerator()

				# 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       =		args.model_name_or_path

				set_seed(_SCREAMING_SNAKE_CASE    )
				__a     ,					__a       =		get_dataloaders(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__a       =		AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE				,     return_dict=_SCREAMING_SNAKE_CASE    )

				# Instantiate optimizer
				__a       =		(
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				__a       =		optimizer_cls(params=model.parameters()				,     lr=_SCREAMING_SNAKE_CASE    )

				if accelerator.state.deepspeed_plugin is not None:
								__a       =		accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								__a       =		1
				__a       =		(len(_SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								__a       =		get_linear_schedule_with_warmup(
								    optimizer=_SCREAMING_SNAKE_CASE				,     num_warmup_steps=0				,     num_training_steps=_SCREAMING_SNAKE_CASE				,     )
				else:
								__a       =		DummyScheduler(_SCREAMING_SNAKE_CASE				,     total_num_steps=_SCREAMING_SNAKE_CASE				,     warmup_num_steps=0    )

				# 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(
				    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# We need to keep track of how many total steps we have iterated over
				__a       =		0
				# We also need to keep track of the stating epoch so files are named properly
				__a       =		0

				# Now we train the model
				__a       =		evaluate.load("""glue"""				,     """mrpc"""    )
				__a       =		0
				__a       =		{}
				for epoch in range(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
								model.train()
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
												__a       =		model(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.loss
												__a       =		loss / gradient_accumulation_steps
												accelerator.backward(_SCREAMING_SNAKE_CASE    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

								model.eval()
								__a       =		0
								for step, batch in enumerate(_SCREAMING_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       =		model(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.logits.argmax(dim=-1    )
												# It is slightly faster to call this once, than multiple times
												__a     ,					__a       =		accelerator.gather(
												    (predictions, batch["""labels"""])    )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																if step == len(_SCREAMING_SNAKE_CASE    ) - 1:
																				__a       =		predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																				__a       =		references[: len(eval_dataloader.dataset    ) - samples_seen]
																else:
																				samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=_SCREAMING_SNAKE_CASE				,     references=_SCREAMING_SNAKE_CASE				,     )

								__a       =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"epoch {epoch}:"				,     _SCREAMING_SNAKE_CASE    )
								__a       =		eval_metric["""accuracy"""]

								if best_performance < eval_metric["accuracy"]:
												__a       =		eval_metric["""accuracy"""]

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir				,     """all_results.json"""    )				,     """w"""    ) as f:
												json.dump(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
				parser.add_argument(
				    """--model_name_or_path"""				,     type=_SCREAMING_SNAKE_CASE				,     default="""bert-base-cased"""				,     help="""Path to pretrained model or model identifier from huggingface.co/models."""				,     required=_SCREAMING_SNAKE_CASE				,     )
				parser.add_argument(
				    """--output_dir"""				,     type=_SCREAMING_SNAKE_CASE				,     default="""."""				,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,     )
				parser.add_argument(
				    """--performance_lower_bound"""				,     type=_SCREAMING_SNAKE_CASE				,     default=_SCREAMING_SNAKE_CASE				,     help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""				,     )
				parser.add_argument(
				    """--num_epochs"""				,     type=_SCREAMING_SNAKE_CASE				,     default=3				,     help="""Number of train epochs."""				,     )
				__a       =		parser.parse_args()
				__a       =		{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
				training_function(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						main()




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_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


lowerCamelCase__			= logging.get_logger(__name__)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    =['pixel_values']





							def __init__(   self		:		str      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Union[int, float] = 1 / 255      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)
											__a       =		size if size is not None else {"""height""": 256, """width""": 256}
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		do_resize
											__a       =		size
											__a       =		resample
											__a       =		do_center_crop
											__a       =		crop_size
											__a       =		do_rescale
											__a       =		rescale_factor
											__a       =		do_normalize
											__a       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__a       =		image_std if image_std is not None else IMAGENET_STANDARD_STD





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		Union[str, Any]      ,				):

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





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											if "height" not in size or "width" not in size:
															raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"	)
											return center_crop(__lowercase      ,				size=(size["""height"""], size["""width"""])      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[int, float]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		List[str]      ,				):

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





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

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







							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		ImageInput      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Dict=None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		float = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[str, TensorType]] = None      ,				__lowercase		:		ChannelDimension = ChannelDimension.FIRST      ,				**__lowercase		:		Optional[Any]      ,				):

											'''simple docstring'''
											__a       =		do_resize if do_resize is not None else self.do_resize
											__a       =		resample if resample is not None else self.resample
											__a       =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__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       =		size if size is not None else self.size
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else self.crop_size
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

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

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True."""	)

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

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

											if do_center_crop:
															__a       =		[self.center_crop(image=__lowercase      ,				size=__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	)
