





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							:       List[Any]					 =	16
lowerCAmelCase							:       int					 =	32






def 		lowerCAmelCase  (	UpperCamelCase__   :   Accelerator       ,			UpperCamelCase__   :   int = 16       ,			UpperCamelCase__   :   str = "bert-base-cased"					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   AutoTokenizer.from_pretrained(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   load_dataset('''glue'''       ,			'''mrpc'''					)

						def tokenize_function(UpperCamelCase__   :   int					):
												# max_length=None => use the model max length (it's actually the default)
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   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
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   datasets.map(
						    UpperCamelCase__       ,			batched=UpperCamelCase__       ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,			load_from_cache_file=UpperCamelCase__					)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__SCREAMING_SNAKE_CASE:				List[str]           =   tokenized_datasets.rename_column('''label'''       ,			'''labels'''					)

						def collate_fn(UpperCamelCase__   :   str					):
												# 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(UpperCamelCase__       ,			padding='''max_length'''       ,			max_length=128       ,			return_tensors='''pt'''					)
												return tokenizer.pad(UpperCamelCase__       ,			padding='''longest'''       ,			return_tensors='''pt'''					)

						# Instantiate dataloaders.
						__SCREAMING_SNAKE_CASE:				Any           =   DataLoader(
						    tokenized_datasets['''train''']       ,			shuffle=UpperCamelCase__       ,			collate_fn=UpperCamelCase__       ,			batch_size=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[Any]           =   DataLoader(
						    tokenized_datasets['''validation''']       ,			shuffle=UpperCamelCase__       ,			collate_fn=UpperCamelCase__       ,			batch_size=UpperCamelCase__					)

						return train_dataloader, eval_dataloader






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   List[Any]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   config['''lr''']
						__SCREAMING_SNAKE_CASE:				List[str]           =   int(config['''num_epochs''']					)
						__SCREAMING_SNAKE_CASE:				Any           =   int(config['''seed''']					)
						__SCREAMING_SNAKE_CASE:				Dict           =   int(config['''batch_size''']					)
						__SCREAMING_SNAKE_CASE:				str           =   args.model_name_or_path

						set_seed(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   get_dataloaders(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__       ,			return_dict=UpperCamelCase__					)

						# Instantiate optimizer
						__SCREAMING_SNAKE_CASE:				Any           =   (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__SCREAMING_SNAKE_CASE:				int           =   optimizer_cls(params=model.parameters()       ,			lr=UpperCamelCase__					)

						if accelerator.state.deepspeed_plugin is not None:
												__SCREAMING_SNAKE_CASE:				Any           =   accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												__SCREAMING_SNAKE_CASE:				Dict           =   1
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   (len(UpperCamelCase__					) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												__SCREAMING_SNAKE_CASE:				Tuple           =   get_linear_schedule_with_warmup(
												    optimizer=UpperCamelCase__       ,			num_warmup_steps=0       ,			num_training_steps=UpperCamelCase__       ,			)
						else:
												__SCREAMING_SNAKE_CASE:				str           =   DummyScheduler(UpperCamelCase__       ,			total_num_steps=UpperCamelCase__       ,			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.
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   accelerator.prepare(
						    UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)

						# We need to keep track of how many total steps we have iterated over
						__SCREAMING_SNAKE_CASE:				Tuple           =   0
						# We also need to keep track of the stating epoch so files are named properly
						__SCREAMING_SNAKE_CASE:				List[str]           =   0

						# Now we train the model
						__SCREAMING_SNAKE_CASE:				Dict           =   evaluate.load('''glue'''       ,			'''mrpc'''					)
						__SCREAMING_SNAKE_CASE:				str           =   0
						__SCREAMING_SNAKE_CASE:				List[Any]           =   {}
						for epoch in range(UpperCamelCase__       ,			UpperCamelCase__					):
												model.train()
												for step, batch in enumerate(UpperCamelCase__					):
																		__SCREAMING_SNAKE_CASE:				List[str]           =   model(**UpperCamelCase__					)
																		__SCREAMING_SNAKE_CASE:				List[str]           =   outputs.loss
																		__SCREAMING_SNAKE_CASE:				Tuple           =   loss / gradient_accumulation_steps
																		accelerator.backward(UpperCamelCase__					)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   0
												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():
																								__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(**UpperCamelCase__					)
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   outputs.logits.argmax(dim=-1					)
																		# It is slightly faster to call this once, than multiple times
																		__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   accelerator.gather(
																		    (predictions, batch['''labels'''])					)  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(UpperCamelCase__					) - 1:
																														__SCREAMING_SNAKE_CASE:				int           =   predictions[: len(eval_dataloader.dataset					) - samples_seen]
																														__SCREAMING_SNAKE_CASE:				Any           =   references[: len(eval_dataloader.dataset					) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=UpperCamelCase__       ,			references=UpperCamelCase__       ,			)

												__SCREAMING_SNAKE_CASE:				Tuple           =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""       ,			UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   eval_metric['''accuracy''']

												if best_performance < eval_metric["accuracy"]:
																		__SCREAMING_SNAKE_CASE:				Dict           =   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(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''					)
						parser.add_argument(
						    '''--model_name_or_path'''       ,			type=UpperCamelCase__       ,			default='''bert-base-cased'''       ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,			required=UpperCamelCase__       ,			)
						parser.add_argument(
						    '''--output_dir'''       ,			type=UpperCamelCase__       ,			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=UpperCamelCase__       ,			default=UpperCamelCase__       ,			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=UpperCamelCase__       ,			default=3       ,			help='''Number of train epochs.'''       ,			)
						__SCREAMING_SNAKE_CASE:				Dict           =   parser.parse_args()
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
						training_function(UpperCamelCase__       ,			UpperCamelCase__					)


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,
    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


lowerCAmelCase							:       str					 =	logging.get_logger(__name__)





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''pixel_values''']






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

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size if size is not None else {'''shortest_edge''': 384}
										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = PILImageResampling.BICUBIC			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)
										if "shortest_edge" not in size:
																raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
										__SCREAMING_SNAKE_CASE:				str           =   size['''shortest_edge''']

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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = ChannelDimension.FIRST			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				List[Any]           =   size if size is not None else self.size
										__SCREAMING_SNAKE_CASE:				Dict           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   make_list_of_images(_lowerCAmelCase					)

										if not valid_images(_lowerCAmelCase					):
																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.
										__SCREAMING_SNAKE_CASE:				Tuple           =   [to_numpy_array(_lowerCAmelCase					) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE:				str           =   [self.resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, crop_pct=_lowerCAmelCase			, resample=_lowerCAmelCase					) for image in images]

										if do_rescale:
																__SCREAMING_SNAKE_CASE:				Any           =   [self.rescale(image=_lowerCAmelCase			, scale=_lowerCAmelCase					) for image in images]

										if do_normalize:
																__SCREAMING_SNAKE_CASE:				int           =   [self.normalize(image=_lowerCAmelCase			, mean=_lowerCAmelCase			, std=_lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				str           =   [to_channel_dimension_format(_lowerCAmelCase			, _lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				List[str]           =   {'''pixel_values''': images}
										return BatchFeature(data=_lowerCAmelCase			, tensor_type=_lowerCAmelCase					)



