


"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class       __A				(       unittest.TestCase			):






 """simple docstring"""




 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     a 		=1
     a 		=3
     a 		=(32, 32)

     a 		=floats_tensor((batch_size, num_channels) + sizes ,    rng=random.Random(0						)						).to(__lowerCamelCase						)
     return image


 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     torch.manual_seed(0						)
     a 		=UNetaDConditionModel(
         block_out_channels=(32, 32, 64) ,    layers_per_block=2 ,    sample_size=32 ,    in_channels=7 ,    out_channels=4 ,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''') ,    up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''') ,    cross_attention_dim=32 ,    attention_head_dim=8 ,    use_linear_projection=__lowerCamelCase ,    only_cross_attention=(True, True, False) ,    num_class_embeds=100 ,    )
     return model


 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     torch.manual_seed(0						)
     a 		=AutoencoderKL(
         block_out_channels=[32, 32, 64] ,    in_channels=3 ,    out_channels=3 ,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,    latent_channels=4 ,    )
     return model


 @property
 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     torch.manual_seed(0						)
     a 		=CLIPTextConfig(
         bos_token_id=0 ,    eos_token_id=2 ,    hidden_size=32 ,    intermediate_size=37 ,    layer_norm_eps=1E-0_5 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    pad_token_id=1 ,    vocab_size=1000 ,    hidden_act='''gelu''' ,    projection_dim=512 ,    )
     return CLIPTextModel(__lowerCamelCase						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     a 		="""cpu"""  # ensure determinism for the device-dependent torch.Generator
     a 		=self.dummy_cond_unet_upscale
     a 		=DDPMScheduler()
     a 		=DDIMScheduler(prediction_type='''v_prediction'''						)
     a 		=self.dummy_vae
     a 		=self.dummy_text_encoder
     a 		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

     a 		=self.dummy_image.cpu().permute(0 ,    2 ,    3 ,    1						)[0]
     a 		=Image.fromarray(np.uinta(__lowerCamelCase						)						).convert('''RGB'''						).resize((64, 64)						)

     # make sure here that pndm scheduler skips prk
     a 		=StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase ,    low_res_scheduler=__lowerCamelCase ,    scheduler=__lowerCamelCase ,    vae=__lowerCamelCase ,    text_encoder=__lowerCamelCase ,    tokenizer=__lowerCamelCase ,    max_noise_level=350 ,    )
     a 		=sd_pipe.to(__lowerCamelCase						)
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

     a 		="""A painting of a squirrel eating a burger"""
     a 		=torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
     a 		=sd_pipe(
         [prompt] ,    image=__lowerCamelCase ,    generator=__lowerCamelCase ,    guidance_scale=6.0 ,    noise_level=20 ,    num_inference_steps=2 ,    output_type='''np''' ,    )

     a 		=output.images

     a 		=torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
     a 		=sd_pipe(
         [prompt] ,    image=__lowerCamelCase ,    generator=__lowerCamelCase ,    guidance_scale=6.0 ,    noise_level=20 ,    num_inference_steps=2 ,    output_type='''np''' ,    return_dict=__lowerCamelCase ,    )[0]

     a 		=image[0, -3:, -3:, -1]
     a 		=image_from_tuple[0, -3:, -3:, -1]

     a 		=low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     a 		=np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]						)

     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		="""cpu"""  # ensure determinism for the device-dependent torch.Generator
     a 		=self.dummy_cond_unet_upscale
     a 		=DDPMScheduler()
     a 		=DDIMScheduler(prediction_type='''v_prediction'''						)
     a 		=self.dummy_vae
     a 		=self.dummy_text_encoder
     a 		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

     a 		=self.dummy_image.cpu().permute(0 ,    2 ,    3 ,    1						)[0]
     a 		=Image.fromarray(np.uinta(__lowerCamelCase						)						).convert('''RGB'''						).resize((64, 64)						)

     # make sure here that pndm scheduler skips prk
     a 		=StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase ,    low_res_scheduler=__lowerCamelCase ,    scheduler=__lowerCamelCase ,    vae=__lowerCamelCase ,    text_encoder=__lowerCamelCase ,    tokenizer=__lowerCamelCase ,    max_noise_level=350 ,    )
     a 		=sd_pipe.to(__lowerCamelCase						)
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

     a 		="""A painting of a squirrel eating a burger"""
     a 		=sd_pipe(
         2 * [prompt] ,    image=2 * [low_res_image] ,    guidance_scale=6.0 ,    noise_level=20 ,    num_inference_steps=2 ,    output_type='''np''' ,    )
     a 		=output.images
     assert image.shape[0] == 2

     a 		=torch.Generator(device=__lowerCamelCase						).manual_seed(0						)
     a 		=sd_pipe(
         [prompt] ,    image=__lowerCamelCase ,    generator=__lowerCamelCase ,    num_images_per_prompt=2 ,    guidance_scale=6.0 ,    noise_level=20 ,    num_inference_steps=2 ,    output_type='''np''' ,    )
     a 		=output.images
     assert image.shape[0] == 2





 @unittest.skipIf(torch_device != '''cuda''' ,    '''This test requires a GPU'''						)
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		=self.dummy_cond_unet_upscale
     a 		=DDPMScheduler()
     a 		=DDIMScheduler(prediction_type='''v_prediction'''						)
     a 		=self.dummy_vae
     a 		=self.dummy_text_encoder
     a 		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

     a 		=self.dummy_image.cpu().permute(0 ,    2 ,    3 ,    1						)[0]
     a 		=Image.fromarray(np.uinta(__lowerCamelCase						)						).convert('''RGB'''						).resize((64, 64)						)

     # put models in fp16, except vae as it overflows in fp16
     a 		=unet.half()
     a 		=text_encoder.half()

     # make sure here that pndm scheduler skips prk
     a 		=StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase ,    low_res_scheduler=__lowerCamelCase ,    scheduler=__lowerCamelCase ,    vae=__lowerCamelCase ,    text_encoder=__lowerCamelCase ,    tokenizer=__lowerCamelCase ,    max_noise_level=350 ,    )
     a 		=sd_pipe.to(__lowerCamelCase						)
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase						)

     a 		="""A painting of a squirrel eating a burger"""
     a 		=torch.manual_seed(0						)
     a 		=sd_pipe(
         [prompt] ,    image=__lowerCamelCase ,    generator=__lowerCamelCase ,    num_inference_steps=2 ,    output_type='''np''' ,    ).images

     a 		=low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)






@slow
@require_torch_gpu
class       __A				(       unittest.TestCase			):






 """simple docstring"""




 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     a 		=load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''						)
     a 		=load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat.npy'''						)

     a 		="""stabilityai/stable-diffusion-x4-upscaler"""
     a 		=StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase						)
     pipe.to(__lowerCamelCase						)
     pipe.set_progress_bar_config(disable=__lowerCamelCase						)
     pipe.enable_attention_slicing()

     a 		="""a cat sitting on a park bench"""

     a 		=torch.manual_seed(0						)
     a 		=pipe(
         prompt=__lowerCamelCase ,    image=__lowerCamelCase ,    generator=__lowerCamelCase ,    output_type='''np''' ,    )
     a 		=output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image						).max() < 1E-3


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''						)
     a 		=load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat_fp16.npy'''						)

     a 		="""stabilityai/stable-diffusion-x4-upscaler"""
     a 		=StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase ,    torch_dtype=torch.floataa ,    )
     pipe.to(__lowerCamelCase						)
     pipe.set_progress_bar_config(disable=__lowerCamelCase						)
     pipe.enable_attention_slicing()

     a 		="""a cat sitting on a park bench"""

     a 		=torch.manual_seed(0						)
     a 		=pipe(
         prompt=__lowerCamelCase ,    image=__lowerCamelCase ,    generator=__lowerCamelCase ,    output_type='''np''' ,    )
     a 		=output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image						).max() < 5E-1





 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     a 		=load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''						)

     a 		="""stabilityai/stable-diffusion-x4-upscaler"""
     a 		=StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase ,    torch_dtype=torch.floataa ,    )
     pipe.to(__lowerCamelCase						)
     pipe.set_progress_bar_config(disable=__lowerCamelCase						)
     pipe.enable_attention_slicing(1						)
     pipe.enable_sequential_cpu_offload()

     a 		="""a cat sitting on a park bench"""

     a 		=torch.manual_seed(0						)
     a 		=pipe(
         prompt=__lowerCamelCase ,    image=__lowerCamelCase ,    generator=__lowerCamelCase ,    num_inference_steps=5 ,    output_type='''np''' ,    )

     a 		=torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 10**9







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_ :						Union[str, Any]      =      16
UpperCAmelCase_ :						int      =      32

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






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	AutoTokenizer.from_pretrained(__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	load_dataset("""glue"""	,			"""mrpc"""					)

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

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

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

	def collate_fn(__magic_name__					:  Any					):
		# 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(__magic_name__	,			padding="""max_length"""	,			max_length=128	,			return_tensors="""pt"""					)
		return tokenizer.pad(__magic_name__	,			padding="""longest"""	,			return_tensors="""pt"""					)

	# Instantiate dataloaders.
	UpperCamelCase     :List[str]          =	DataLoader(
	    tokenized_datasets["""train"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)
	UpperCamelCase     :List[Any]          =	DataLoader(
	    tokenized_datasets["""validation"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)

	return train_dataloader, eval_dataloader

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Optional[Any]					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	Accelerator()

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

	set_seed(__magic_name__					)
	UpperCamelCase     ,      UpperCamelCase     :Dict          =	get_dataloaders(__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCamelCase     :List[str]          =	AutoModelForSequenceClassification.from_pretrained(__magic_name__	,			return_dict=__magic_name__					)

	# Instantiate optimizer
	UpperCamelCase     :Union[str, Any]          =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	UpperCamelCase     :Optional[Any]          =	optimizer_cls(params=model.parameters()	,			lr=__magic_name__					)

	if accelerator.state.deepspeed_plugin is not None:
		UpperCamelCase     :Any          =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		UpperCamelCase     :Any          =	1
	UpperCamelCase     :Dict          =	(len(__magic_name__					) * num_epochs) // gradient_accumulation_steps

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

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

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

			overall_step += 1

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

		UpperCamelCase     :List[str]          =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,			__magic_name__					)
		UpperCamelCase     :Dict          =	eval_metric["""accuracy"""]

		if best_performance < eval_metric["accuracy"]:
			UpperCamelCase     :str          =	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(__magic_name__	,			__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



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


if __name__ == "__main__":
	main()
