





'''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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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					    =	16
__UpperCamelCase					    =	32







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = 16 ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     AutoTokenizer.from_pretrained("""bert-base-cased""" )
			__snake_case					:      Dict							      =     load_dataset("""glue"""    ,	"""mrpc""" )

			def tokenize_function(_lowerCamelCase ):
						# max_length=None => use the model max length (it's actually the default)
						__snake_case					:      List[Any]							      =     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():
						__snake_case					:      Optional[Any]							      =     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
			__snake_case					:      int							      =     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.
						__snake_case					:      Optional[int]							      =     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":
									__snake_case					:      int							      =     16
						elif accelerator.mixed_precision != "no":
									__snake_case					:      Any							      =     8
						else:
									__snake_case					:      List[Any]							      =     None

						return tokenizer.pad(
						    _lowerCamelCase    ,	padding="""longest"""    ,	max_length=_lowerCamelCase    ,	pad_to_multiple_of=_lowerCamelCase    ,	return_tensors="""pt"""    ,	)

			# Instantiate dataloaders.
			__snake_case					:      List[str]							      =     DataLoader(
			    tokenized_datasets["""train"""]    ,	shuffle=_lowerCamelCase    ,	collate_fn=_lowerCamelCase    ,	batch_size=_lowerCamelCase )
			__snake_case					:      Dict							      =     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					    =	mocked_dataloaders  # noqa: F811







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Tuple:



			"""simple docstring"""


			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,	_lowerCamelCase ) == "1":
						__snake_case					:      Optional[int]							      =     2
			# Initialize accelerator
			__snake_case					:      List[Any]							      =     Accelerator(cpu=args.cpu    ,	mixed_precision=args.mixed_precision )
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__snake_case					:      Union[str, Any]							      =     config["""lr"""]
			__snake_case					:      Optional[int]							      =     int(config["""num_epochs"""] )
			__snake_case					:      Optional[int]							      =     int(config["""seed"""] )
			__snake_case					:      List[Any]							      =     int(config["""batch_size"""] )

			__snake_case					:      int							      =     evaluate.load("""glue"""    ,	"""mrpc""" )

			# New Code #
			# We now can define an inner training loop function. It should take a batch size as the only parameter,
			# and build the dataloaders in there.
			# It also gets our decorator
			@find_executable_batch_size(starting_batch_size=_lowerCamelCase )
			def inner_training_loop(_lowerCamelCase ):
						# And now just move everything below under this function
						# We need to bring in the Accelerator object from earlier
						nonlocal accelerator
						# And reset all of its attributes that could hold onto any memory:
						accelerator.free_memory()

						# Then we can declare the model, optimizer, and everything else:
						set_seed(_lowerCamelCase )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__snake_case					:      str							      =     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).
						__snake_case					:      Union[str, Any]							      =     model.to(accelerator.device )

						# Instantiate optimizer
						__snake_case					:      int							      =     AdamW(params=model.parameters()    ,	lr=_lowerCamelCase )
						__snake_case      ,     __snake_case					:      Optional[int]							      =     get_dataloaders(_lowerCamelCase    ,	_lowerCamelCase )

						# Instantiate scheduler
						__snake_case					:      Optional[Any]							      =     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.
						__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      int							      =     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 )
												__snake_case					:      Optional[int]							      =     model(**_lowerCamelCase )
												__snake_case					:      Optional[Any]							      =     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():
															__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
												__snake_case					:      List[Any]							      =     outputs.logits.argmax(dim=-1 )
												__snake_case      ,     __snake_case					:      Optional[int]							      =     accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=_lowerCamelCase    ,	references=_lowerCamelCase    ,	)

									__snake_case					:      Any							      =     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 					_a						(       ) ->				Optional[int]:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     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.""" )
			__snake_case					:      Any							      =     parser.parse_args()
			__snake_case					:      Any							      =     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(_lowerCamelCase    ,	_lowerCamelCase )


if __name__ == "__main__":
	main()









"""simple docstring"""
import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def 		_SCREAMING_SNAKE_CASE			():
 lowerCAmelCase    				= argparse.ArgumentParser()
 parser.add_argument(
     '-m'  ,     '--pretrained_model_name_or_path'  ,     type=_UpperCAmelCase  ,     default=_UpperCAmelCase  ,     required=_UpperCAmelCase  ,     help='Path to pretrained model or model identifier from huggingface.co/models.'  ,     )
 parser.add_argument(
     '-c'  ,     '--caption'  ,     type=_UpperCAmelCase  ,     default='robotic cat with wings'  ,     help='Text used to generate images.'  ,     )
 parser.add_argument(
     '-n'  ,     '--images_num'  ,     type=_UpperCAmelCase  ,     default=4  ,     help='How much images to generate.'  ,     )
 parser.add_argument(
     '-s'  ,     '--seed'  ,     type=_UpperCAmelCase  ,     default=42  ,     help='Seed for random process.'  ,     )
 parser.add_argument(
     '-ci'  ,     '--cuda_id'  ,     type=_UpperCAmelCase  ,     default=0  ,     help='cuda_id.'  ,     )
 lowerCAmelCase    				= parser.parse_args()
 return args

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Optional[int]			):
 if not len(_UpperCAmelCase			) == rows * cols:
  raise ValueError('The specified number of rows and columns are not correct.'			)

 lowerCAmelCase				,lowerCAmelCase    				= imgs[0].size
 lowerCAmelCase    				= Image.new('RGB'  ,     size=(cols * w, rows * h)			)
 lowerCAmelCase				,lowerCAmelCase    				= grid.size

 for i, img in enumerate(_UpperCAmelCase			):
  grid.paste(_UpperCAmelCase  ,     box=(i % cols * w, i // cols * h)			)
 return grid

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Union[str, Any]="robotic cat with wings"  ,     _UpperCAmelCase					:    Optional[int]=7.5  ,     _UpperCAmelCase					:    Dict=50  ,     _UpperCAmelCase					:    Tuple=1  ,     _UpperCAmelCase					:    int=42  ,     ):
 lowerCAmelCase    				= torch.Generator(pipeline.device			).manual_seed(_UpperCAmelCase			)
 lowerCAmelCase    				= pipeline(
     _UpperCAmelCase  ,     guidance_scale=_UpperCAmelCase  ,     num_inference_steps=_UpperCAmelCase  ,     generator=_UpperCAmelCase  ,     num_images_per_prompt=_UpperCAmelCase  ,     ).images
 lowerCAmelCase    				= int(math.sqrt(_UpperCAmelCase			)			)
 lowerCAmelCase    				= image_grid(_UpperCAmelCase  ,     rows=_rows  ,     cols=num_images_per_prompt // _rows			)
 return grid, images


__UpperCamelCase     :						Optional[Any]						       =  parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase     :						List[Any]						       =  CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__UpperCamelCase     :						str						       =  CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__UpperCamelCase     :						Optional[int]						       =  AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
__UpperCamelCase     :						List[str]						       =  UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

__UpperCamelCase     :						Tuple						       =  StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase     :						Union[str, Any]						       =  lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
       __UpperCamelCase     :						Dict						       =  load(args.pretrained_model_name_or_path, model=unet)
       unet.eval()
       setattr(pipeline, '''unet''', unet)
else:
       __UpperCamelCase     :						Dict						       =  unet.to(torch.device('''cuda''', args.cuda_id))
__UpperCamelCase     :						Optional[Any]						       =  pipeline.to(unet.device)
__UpperCamelCase       ,__UpperCamelCase     :						List[Any]						       =  generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '''{}.png'''.format('''_'''.join(args.caption.split()))))
__UpperCamelCase     :						int						       =  os.path.join(args.pretrained_model_name_or_path, '''_'''.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
       image.save(os.path.join(dirname, '''{}.png'''.format(idx + 1)))





