
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class     lowercase		:




							def __init__(    self       ,A__       ,A__=sys.maxsize):
												lowercase						     =						'''bilinear'''
												lowercase						     =						max_size
												lowercase						     =						short_edge_length





							def __call__(    self       ,A__):
												lowercase						     =						[]
												for img in imgs:
																	lowercase     ,						lowercase						     =						img.shape[:2]
																	# later: provide list and randomly choose index for resize
																	lowercase						     =						np.random.randint(self.short_edge_length[0]       ,self.short_edge_length[1] + 1)
																	if size == 0:
																						return img
																	lowercase						     =						size * 1.0 / min(A__       ,A__)
																	if h < w:
																						lowercase     ,						lowercase						     =						size, scale * w
																	else:
																						lowercase     ,						lowercase						     =						scale * h, size
																	if max(A__       ,A__) > self.max_size:
																						lowercase						     =						self.max_size * 1.0 / max(A__       ,A__)
																						lowercase						     =						newh * scale
																						lowercase						     =						neww * scale
																	lowercase						     =						int(neww + 0.5)
																	lowercase						     =						int(newh + 0.5)

																	if img.dtype == np.uinta:
																						lowercase						     =						Image.fromarray(A__)
																						lowercase						     =						pil_image.resize((neww, newh)       ,PILImageResampling.BILINEAR)
																						lowercase						     =						np.asarray(A__)
																	else:
																						lowercase						     =						img.permute(2       ,0       ,1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
																						lowercase						     =						nn.functional.interpolate(
																						    A__       ,(newh, neww)       ,mode=self.interp_method       ,align_corners=A__).squeeze(0)
																	img_augs.append(A__)

												return img_augs







class     lowercase		:




							def __init__(    self       ,A__):
												lowercase						     =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,cfg.INPUT.MAX_SIZE_TEST)
												lowercase						     =						cfg.INPUT.FORMAT
												lowercase						     =						cfg.SIZE_DIVISIBILITY
												lowercase						     =						cfg.PAD_VALUE
												lowercase						     =						cfg.INPUT.MAX_SIZE_TEST
												lowercase						     =						cfg.MODEL.DEVICE
												lowercase						     =						torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)       ,1       ,1)
												lowercase						     =						torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)       ,1       ,1)
												lowercase						     =						lambda A__: (x - self.pixel_mean) / self.pixel_std




							def 			A__				(    self       ,A__):
												lowercase						     =						tuple(max(A__) for s in zip(*[img.shape for img in images]))
												lowercase						     =						[im.shape[-2:] for im in images]
												lowercase						     =						[
												    nn.functional.pad(
												        A__       ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]       ,value=self.pad_value       ,)
												    for size, im in zip(A__       ,A__)
												]

												return torch.stack(A__), torch.tensor(A__)





							def __call__(    self       ,A__       ,A__=False):
												with torch.no_grad():
																	if not isinstance(A__       ,A__):
																						lowercase						     =						[images]
																	if single_image:
																						assert len(A__) == 1
																	for i in range(len(A__)):
																						if isinstance(images[i]       ,torch.Tensor):
																											images.insert(A__       ,images.pop(A__).to(self.device).float())
																						elif not isinstance(images[i]       ,torch.Tensor):
																											images.insert(
																											    A__       ,torch.as_tensor(img_tensorize(images.pop(A__)       ,input_format=self.input_format))
																											    .to(self.device)
																											    .float()       ,)
            # resize smallest edge
																	lowercase						     =						torch.tensor([im.shape[:2] for im in images])
																	lowercase						     =						self.aug(A__)
																	# transpose images and convert to torch tensors
																	# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																	# now normalize before pad to avoid useless arithmetic
																	lowercase						     =						[self.normalizer(A__) for x in images]
																	# now pad them to do the following operations
																	lowercase     ,						lowercase						     =						self.pad(A__)
																	# Normalize

																	if self.size_divisibility > 0:
																						raise NotImplementedError()
																	# pad
																	lowercase						     =						torch.true_divide(A__       ,A__)
																	if single_image:
																						return images[0], sizes[0], scales_yx[0]
																	else:
																						return images, sizes, scales_yx



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					assert torch.isfinite(lowerCAmelCase__		).all(), "Box tensor contains infinite or NaN!"
					lowercase     ,						lowercase						     =						box_size
					tensor[:, 0].clamp_(min=0			,      max=lowerCAmelCase__		)
					tensor[:, 1].clamp_(min=0			,      max=lowerCAmelCase__		)
					tensor[:, 2].clamp_(min=0			,      max=lowerCAmelCase__		)
					tensor[:, 3].clamp_(min=0			,      max=lowerCAmelCase__		)






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


lowercase__		:Union[str, Any]			  =   16
lowercase__		:Optional[Any]			  =   32



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = 16		):
					'''simple docstring'''
					lowercase						     =						AutoTokenizer.from_pretrained('''bert-base-cased'''		)
					lowercase						     =						load_dataset('''glue'''			,      '''mrpc'''		)

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

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

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

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

							lowercase__		:List[str]			  =   mocked_dataloaders  # noqa: F811



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,      lowerCAmelCase__		) == "1":
										lowercase						     =						2
					# New Code #
					lowercase						     =						int(args.gradient_accumulation_steps		)
					# Initialize accelerator
					lowercase						     =						Accelerator(
					    cpu=args.cpu			,      mixed_precision=args.mixed_precision			,      gradient_accumulation_steps=lowerCAmelCase__		)
					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
					lowercase						     =						config['''lr''']
					lowercase						     =						int(config['''num_epochs''']		)
					lowercase						     =						int(config['''seed''']		)
					lowercase						     =						int(config['''batch_size''']		)

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

					set_seed(lowerCAmelCase__		)
					lowercase     ,						lowercase						     =						get_dataloaders(lowerCAmelCase__			,      lowerCAmelCase__		)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowercase						     =						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).
					lowercase						     =						model.to(accelerator.device		)

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

					# Instantiate scheduler
					lowercase						     =						get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase__			,      num_warmup_steps=100			,      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.
					lowercase     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase						     =						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__		):
															# 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(lowerCAmelCase__		):
																				lowercase						     =						model(**lowerCAmelCase__		)
																				lowercase						     =						output.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():
																				lowercase						     =						model(**lowerCAmelCase__		)
															lowercase						     =						outputs.logits.argmax(dim=-1		)
															lowercase     ,						lowercase						     =						accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
															metric.add_batch(
															    predictions=lowerCAmelCase__			,      references=lowerCAmelCase__			,      )

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



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						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.'''			,      )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps'''			,      type=lowerCAmelCase__			,      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.'''		)
					lowercase						     =						parser.parse_args()
					lowercase						     =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCAmelCase__			,      lowerCAmelCase__		)


if __name__ == "__main__":
							main()




