





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


__UpperCamelCase:	int = 1_6
__UpperCamelCase:	Optional[Any] = 3_2


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Accelerator   ,    _lowercase     : int = 16   ,    _lowercase     : str = "bert-base-cased"			)   ->							Dict:




		'''simple docstring'''
		lowercase__					:      Dict      =		AutoTokenizer.from_pretrained(_lowercase			)
		lowercase__					:      Union[str, Any]      =		load_dataset('glue'   ,    'mrpc'			)

		def tokenize_function(_lowercase     : int			):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__					:      Union[str, Any]      =		tokenizer(examples['sentence1']   ,    examples['sentence2']   ,    truncation=_lowercase   ,    max_length=_lowercase			)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowercase__					:      List[str]      =		datasets.map(
		    _lowercase   ,    batched=_lowercase   ,    remove_columns=['idx', 'sentence1', 'sentence2']   ,    load_from_cache_file=_lowercase			)

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

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

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

		return train_dataloader, eval_dataloader


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Tuple   ,    _lowercase     : List[str]   ,    _lowercase     : Dict			)   ->							Optional[int]:




		'''simple docstring'''
		model.eval()
		lowercase__					:      Any      =		0
		for step, batch in enumerate(_lowercase			):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device			)
				with torch.no_grad():
						lowercase__					:      Dict      =		model(**_lowercase			)
				lowercase__					:      Union[str, Any]      =		outputs.logits.argmax(dim=-1			)
				# It is slightly faster to call this once, than multiple times
				lowercase__   ,				lowercase__					:      Dict      =		accelerator.gather(
				    (predictions, batch['labels'])			)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(_lowercase			) - 1:
								lowercase__					:      int      =		predictions[: len(eval_dataloader.dataset			) - samples_seen]
								lowercase__					:      List[Any]      =		references[: len(eval_dataloader.dataset			) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=_lowercase   ,    references=_lowercase   ,    )

		lowercase__					:      List[str]      =		metric.compute()
		return eval_metric["accuracy"]


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Dict			)   ->							Dict:




		'''simple docstring'''
		# Initialize accelerator
		lowercase__					:      Dict      =		Accelerator()

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

		set_seed(_lowercase			)
		lowercase__   ,				lowercase__					:      List[str]      =		get_dataloaders(_lowercase   ,    _lowercase   ,    _lowercase			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowercase__					:      Tuple      =		AutoModelForSequenceClassification.from_pretrained(_lowercase   ,    return_dict=_lowercase			)

		# Instantiate optimizer
		lowercase__					:      Optional[int]      =		(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowercase__					:      str      =		optimizer_cls(params=model.parameters()   ,    lr=_lowercase			)

		if accelerator.state.deepspeed_plugin is not None:
				lowercase__					:      Any      =		accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				lowercase__					:      List[Any]      =		1
		lowercase__					:      Optional[int]      =		(len(_lowercase			) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowercase__					:      List[str]      =		get_linear_schedule_with_warmup(
				    optimizer=_lowercase   ,    num_warmup_steps=0   ,    num_training_steps=_lowercase   ,    )
		else:
				lowercase__					:      Optional[int]      =		DummyScheduler(_lowercase   ,    total_num_steps=_lowercase   ,    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.
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      List[Any]      =		accelerator.prepare(
		    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase			)

		# We need to keep track of how many total steps we have iterated over
		lowercase__					:      List[str]      =		0
		# We also need to keep track of the stating epoch so files are named properly
		lowercase__					:      Union[str, Any]      =		0
		lowercase__					:      Dict      =		evaluate.load('glue'   ,    'mrpc'			)
		lowercase__					:      List[Any]      =		num_epochs

		if args.partial_train_epoch is not None:
				lowercase__					:      Tuple      =		args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint			)
				lowercase__					:      Dict      =		args.resume_from_checkpoint.split('epoch_'			)[1]
				lowercase__					:      Optional[int]      =		''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				lowercase__					:      List[Any]      =		int(_lowercase			) + 1
				lowercase__					:      int      =		evaluation_loop(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase			)
				accelerator.print('resumed checkpoint performance:'   ,    _lowercase			)
				accelerator.print('resumed checkpoint\'s scheduler\'s lr:'   ,    lr_scheduler.get_lr()[0]			)
				accelerator.print('resumed optimizers\'s lr:'   ,    optimizer.param_groups[0]['lr']			)
				with open(os.path.join(args.output_dir   ,    f"""state_{starting_epoch-1}.json"""			)   ,    'r'			) as f:
						lowercase__					:      Optional[int]      =		json.load(_lowercase			)
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		lowercase__					:      Optional[int]      =		{}
		for epoch in range(_lowercase   ,    _lowercase			):
				model.train()
				for step, batch in enumerate(_lowercase			):
						lowercase__					:      int      =		model(**_lowercase			)
						lowercase__					:      List[Any]      =		outputs.loss
						lowercase__					:      List[Any]      =		loss / gradient_accumulation_steps
						accelerator.backward(_lowercase			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				lowercase__					:      Tuple      =		f"""epoch_{epoch}"""
				lowercase__					:      List[str]      =		os.path.join(args.output_dir   ,    _lowercase			)
				accelerator.save_state(_lowercase			)
				lowercase__					:      Union[str, Any]      =		evaluation_loop(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase			)
				lowercase__					:      int      =		accuracy
				lowercase__					:      Optional[int]      =		lr_scheduler.get_lr()[0]
				lowercase__					:      Union[str, Any]      =		optimizer.param_groups[0]['lr']
				lowercase__					:      Optional[Any]      =		epoch
				lowercase__					:      List[Any]      =		overall_step
				accelerator.print(f"""epoch {epoch}:"""   ,    _lowercase			)

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir   ,    f"""state_{epoch}.json"""			)   ,    'w'			) as f:
								json.dump(_lowercase   ,    _lowercase			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							str:




		'''simple docstring'''
		lowercase__					:      Dict      =		argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'			)
		parser.add_argument(
		    '--model_name_or_path'   ,    type=_lowercase   ,    default='bert-base-cased'   ,    help='Path to pretrained model or model identifier from huggingface.co/models.'   ,    required=_lowercase   ,    )
		parser.add_argument(
		    '--output_dir'   ,    type=_lowercase   ,    default='.'   ,    help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'   ,    )
		parser.add_argument(
		    '--resume_from_checkpoint'   ,    type=_lowercase   ,    default=_lowercase   ,    help='If the training should continue from a checkpoint folder.'   ,    )
		parser.add_argument(
		    '--partial_train_epoch'   ,    type=_lowercase   ,    default=_lowercase   ,    help='If passed, the training will stop after this number of epochs.'   ,    )
		parser.add_argument(
		    '--num_epochs'   ,    type=_lowercase   ,    default=2   ,    help='Number of train epochs.'   ,    )
		lowercase__					:      List[str]      =		parser.parse_args()
		lowercase__					:      Optional[Any]      =		{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(_lowercase   ,    _lowercase			)


if __name__ == "__main__":
			main()









from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


