
"""simple docstring"""





import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_a														=		logging.getLogger(__name__)



def       lowerCamelCase__		(			__snake_case=2,     __snake_case=3,     __snake_case=16,     __snake_case = 10,     __snake_case = 2	)							->		str:


					"""simple docstring"""





					def get_dataset(__snake_case	):
										_UpperCamelCase							 =  torch.randn(batch_size * n_batches,     1	)
										return TensorDataset(__snake_case,     a * x + b + 0.1 * torch.randn(batch_size * n_batches,     1	)	)

					_UpperCamelCase							 =  get_dataset(__snake_case	)
					_UpperCamelCase							 =  get_dataset(__snake_case	)
					_UpperCamelCase							 =  DataLoader(__snake_case,     shuffle=__snake_case,     batch_size=__snake_case,     num_workers=4	)
					_UpperCamelCase							 =  DataLoader(__snake_case,     shuffle=__snake_case,     batch_size=__snake_case,     num_workers=4	)
					return (train_dataloader, valid_dataloader)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case=None	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  []
					for epoch in range(__snake_case	):
										# Train quickly
										model.train()
										for batch in dataloader:
															_UpperCamelCase ,  _UpperCamelCase							 =  batch
															_UpperCamelCase							 =  model(__snake_case	)
															_UpperCamelCase							 =  torch.nn.functional.mse_loss(__snake_case,     __snake_case	)
															accelerator.backward(__snake_case	)
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()	)  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands






class     _UpperCAmelCase(    nn.Module	):



		def __init__(					self)       ->       Dict:






							'''simple docstring'''
							super().__init__()
							_UpperCamelCase							 =  nn.Parameter(torch.randn(1))
							_UpperCamelCase							 =  nn.Parameter(torch.randn(1))




		def 						UpperCAmelCase							(					self				,		__a)       ->       Tuple:






							'''simple docstring'''
							return x * self.a + self.b






class     _UpperCAmelCase(    unittest.TestCase	):



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(42)
												_UpperCamelCase							 =  DummyModel()
												_UpperCamelCase							 =  torch.optim.Adam(params=model.parameters()				,		lr=1e-3)
												_UpperCamelCase ,  _UpperCamelCase							 =  dummy_dataloaders()
												_UpperCamelCase							 =  ProjectConfiguration(total_limit=1				,		project_dir=__a				,		automatic_checkpoint_naming=__a)
												# Train baseline
												_UpperCamelCase							 =  Accelerator(project_config=__a)
												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  accelerator.prepare(
												    __a				,		__a				,		__a				,		__a)
												# Save initial
												accelerator.save_state()

												# Save second state
												accelerator.save_state()
												self.assertEqual(len(os.listdir(accelerator.project_dir))				,		1)



		def 						UpperCAmelCase							(					self)       ->       Union[str, Any]:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(42)
												_UpperCamelCase							 =  DummyModel()
												_UpperCamelCase							 =  torch.optim.Adam(params=model.parameters()				,		lr=1e-3)
												_UpperCamelCase ,  _UpperCamelCase							 =  dummy_dataloaders()
												# Train baseline
												_UpperCamelCase							 =  Accelerator()
												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  accelerator.prepare(
												    __a				,		__a				,		__a				,		__a)
												# Save initial
												_UpperCamelCase							 =  os.path.join(__a				,		'''initial''')
												accelerator.save_state(__a)
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()
												_UpperCamelCase							 =  train(3				,		__a				,		__a				,		__a				,		__a)
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()

												# Train partially
												set_seed(42)
												_UpperCamelCase							 =  DummyModel()
												_UpperCamelCase							 =  torch.optim.Adam(params=model.parameters()				,		lr=1e-3)
												_UpperCamelCase ,  _UpperCamelCase							 =  dummy_dataloaders()
												_UpperCamelCase							 =  Accelerator()
												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  accelerator.prepare(
												    __a				,		__a				,		__a				,		__a)
												accelerator.load_state(__a)
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)

												_UpperCamelCase							 =  train(2				,		__a				,		__a				,		__a				,		__a)
												# Save everything
												_UpperCamelCase							 =  os.path.join(__a				,		'''checkpoint''')
												accelerator.save_state(__a)

												# Load everything back in and make sure all states work
												accelerator.load_state(__a)
												test_rands += train(1				,		__a				,		__a				,		__a				,		__a)
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(42)
												_UpperCamelCase							 =  DummyModel()
												_UpperCamelCase							 =  torch.optim.Adam(params=model.parameters()				,		lr=1e-3)
												_UpperCamelCase ,  _UpperCamelCase							 =  dummy_dataloaders()
												_UpperCamelCase							 =  ProjectConfiguration(automatic_checkpoint_naming=__a)

												# Train baseline
												_UpperCamelCase							 =  Accelerator(project_dir=__a				,		project_config=__a)
												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  accelerator.prepare(
												    __a				,		__a				,		__a				,		__a)
												# Save initial
												accelerator.save_state()
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()
												_UpperCamelCase							 =  train(3				,		__a				,		__a				,		__a				,		__a)
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()

												# Train partially
												set_seed(42)
												_UpperCamelCase							 =  DummyModel()
												_UpperCamelCase							 =  torch.optim.Adam(params=model.parameters()				,		lr=1e-3)
												_UpperCamelCase ,  _UpperCamelCase							 =  dummy_dataloaders()
												_UpperCamelCase							 =  ProjectConfiguration(iteration=1				,		automatic_checkpoint_naming=__a)
												_UpperCamelCase							 =  Accelerator(project_dir=__a				,		project_config=__a)
												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  accelerator.prepare(
												    __a				,		__a				,		__a				,		__a)
												accelerator.load_state(os.path.join(__a				,		'''checkpoints'''				,		'''checkpoint_0'''))
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)

												_UpperCamelCase							 =  train(2				,		__a				,		__a				,		__a				,		__a)
												# Save everything
												accelerator.save_state()

												# Load everything back in and make sure all states work
												accelerator.load_state(os.path.join(__a				,		'''checkpoints'''				,		'''checkpoint_1'''))
												test_rands += train(1				,		__a				,		__a				,		__a				,		__a)
												((_UpperCamelCase) ,  (_UpperCamelCase))							 =  model.a.item(), model.b.item()
												_UpperCamelCase							 =  optimizer.state_dict()
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)
												self.assertEqual(__a				,		__a)



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  torch.tensor([1, 2, 3])
							_UpperCamelCase							 =  torch.tensor([2, 3, 4])
							_UpperCamelCase							 =  DummyModel()
							_UpperCamelCase							 =  torch.optim.Adam(net.parameters())
							_UpperCamelCase							 =  Accelerator()
							with self.assertRaises(__a) as ve:
												accelerator.register_for_checkpointing(__a				,		__a				,		__a				,		__a)
							_UpperCamelCase							 =  str(ve.exception)
							self.assertTrue('''Item at index 0''' in message)
							self.assertTrue('''Item at index 1''' in message)
							self.assertFalse('''Item at index 2''' in message)
							self.assertFalse('''Item at index 3''' in message)



		def 						UpperCAmelCase							(					self)       ->       List[str]:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(42)
												_UpperCamelCase							 =  DummyModel()
												_UpperCamelCase							 =  torch.optim.Adam(params=model.parameters()				,		lr=1e-3)
												_UpperCamelCase							 =  torch.optim.lr_scheduler.StepLR(__a				,		step_size=1				,		gamma=0.99)
												_UpperCamelCase ,  _UpperCamelCase							 =  dummy_dataloaders()
												_UpperCamelCase							 =  ProjectConfiguration(automatic_checkpoint_naming=__a)
												# Train baseline
												_UpperCamelCase							 =  Accelerator(project_dir=__a				,		project_config=__a)
												_UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase ,  _UpperCamelCase							 =  accelerator.prepare(
												    __a				,		__a				,		__a				,		__a				,		__a)
												# Save initial
												accelerator.save_state()
												_UpperCamelCase							 =  scheduler.state_dict()
												train(3				,		__a				,		__a				,		__a				,		__a				,		__a)
												self.assertNotEqual(__a				,		scheduler.state_dict())

												# Load everything back in and make sure all states work
												accelerator.load_state(os.path.join(__a				,		'''checkpoints'''				,		'''checkpoint_0'''))
												self.assertEqual(__a				,		scheduler.state_dict())



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(42)
												_UpperCamelCase							 =  DummyModel()
												_UpperCamelCase							 =  ProjectConfiguration(automatic_checkpoint_naming=__a				,		total_limit=2)
												# Train baseline
												_UpperCamelCase							 =  Accelerator(project_dir=__a				,		project_config=__a)
												_UpperCamelCase							 =  accelerator.prepare(__a)
												# Save 3 states:
												for _ in range(11):
																	accelerator.save_state()
												self.assertTrue(not os.path.exists(os.path.join(__a				,		'''checkpoints'''				,		'''checkpoint_0''')))
												self.assertTrue(os.path.exists(os.path.join(__a				,		'''checkpoints'''				,		'''checkpoint_9''')))
												self.assertTrue(os.path.exists(os.path.join(__a				,		'''checkpoints'''				,		'''checkpoint_10''')))




		@require_cuda
		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__)]
							execute_subprocess_async(__a				,		env=os.environ.copy())


if __name__ == "__main__":
				_a														=		"""/tmp/accelerate/state_checkpointing"""
				_a														=		DummyModel()
				_a														=		torch.optim.Adam(params=model.parameters(), lr=1E-3)
				_a														=		torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
				_a							,							_a														=		dummy_dataloaders()
				_a														=		ProjectConfiguration(automatic_checkpoint_naming=True)
				# Train baseline
				_a														=		Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
				if accelerator.process_index == 0:
								if os.path.exists(savedir):
												shutil.rmtree(savedir)
								os.makedirs(savedir)
				_a							,							_a							,							_a							,							_a							,							_a														=		accelerator.prepare(
				    model, optimizer, train_dataloader, valid_dataloader, scheduler
				)
				_a							,							_a														=		accelerator.prepare(model, optimizer)
				train(3, model, train_dataloader, optimizer, accelerator, scheduler)
				# Check that the intial optimizer is loaded on the GPU
				for group in optimizer.param_groups:
								_a														=		group["""params"""][0].device
								break
				assert param_device.type == accelerator.device.type
				_a														=		model.cpu()
				accelerator.wait_for_everyone()
				accelerator.save_state()
				accelerator.wait_for_everyone()

				# Check CPU state
				accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
				for group in optimizer.param_groups:
								_a														=		group["""params"""][0].device
								break
				assert (
				    param_device.type == torch.device("""cpu""").type
				), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

				# Check device state
				model.to(accelerator.device)
				accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
				for group in optimizer.param_groups:
								_a														=		group["""params"""][0].device
								break
				assert (
				    param_device.type == accelerator.device.type
				), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

				# Check error
				with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
								accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
				accelerator.wait_for_everyone()
				if accelerator.process_index == 0:
								shutil.rmtree(savedir)
				accelerator.wait_for_everyone()








"""simple docstring"""





import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					if isinstance(__snake_case,     torch.Tensor	):
										return image
					elif isinstance(__snake_case,     PIL.Image.Image	):
										_UpperCamelCase							 =  [image]

					if isinstance(image[0],     PIL.Image.Image	):
										_UpperCamelCase							 =  [np.array(i.resize((w, h),     resample=PIL_INTERPOLATION['''lanczos''']	)	)[None, :] for i in image]
										_UpperCamelCase							 =  np.concatenate(__snake_case,     axis=0	)
										_UpperCamelCase							 =  np.array(__snake_case	).astype(np.floataa	) / 255.0
										_UpperCamelCase							 =  image.transpose(0,     3,     1,     2	)
										_UpperCamelCase							 =  2.0 * image - 1.0
										_UpperCamelCase							 =  torch.from_numpy(__snake_case	)
					elif isinstance(image[0],     torch.Tensor	):
										_UpperCamelCase							 =  torch.cat(__snake_case,     dim=0	)
					return image



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=0.9995	)							->		List[Any]:


					"""simple docstring"""





					if not isinstance(__snake_case,     np.ndarray	):
										_UpperCamelCase							 =  True
										_UpperCamelCase							 =  va.device
										_UpperCamelCase							 =  va.cpu().numpy()
										_UpperCamelCase							 =  va.cpu().numpy()

					_UpperCamelCase							 =  np.sum(va * va / (np.linalg.norm(__snake_case	) * np.linalg.norm(__snake_case	))	)
					if np.abs(__snake_case	) > DOT_THRESHOLD:
										_UpperCamelCase							 =  (1 - t) * va + t * va
					else:
										_UpperCamelCase							 =  np.arccos(__snake_case	)
										_UpperCamelCase							 =  np.sin(__snake_case	)
										_UpperCamelCase							 =  theta_a * t
										_UpperCamelCase							 =  np.sin(__snake_case	)
										_UpperCamelCase							 =  np.sin(theta_a - theta_t	) / sin_theta_a
										_UpperCamelCase							 =  sin_theta_t / sin_theta_a
										_UpperCamelCase							 =  sa * va + sa * va

					if inputs_are_torch:
										_UpperCamelCase							 =  torch.from_numpy(__snake_case	).to(__snake_case	)

					return va



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  F.normalize(__snake_case,     dim=-1	)
					_UpperCamelCase							 =  F.normalize(__snake_case,     dim=-1	)
					return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Optional[int]:


					"""simple docstring"""





					for param in model.parameters():
										_UpperCamelCase							 =  value






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a=None				,		__a=None				,		__a=None				,		)       ->       List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(
							    vae=__a				,		text_encoder=__a				,		clip_model=__a				,		tokenizer=__a				,		unet=__a				,		scheduler=__a				,		feature_extractor=__a				,		coca_model=__a				,		coca_tokenizer=__a				,		coca_transform=__a				,		)
							_UpperCamelCase							 =  (
							    feature_extractor.size
							    if isinstance(feature_extractor.size				,		__a)
							    else feature_extractor.size['''shortest_edge''']
							)
							_UpperCamelCase							 =  transforms.Normalize(mean=feature_extractor.image_mean				,		std=feature_extractor.image_std)
							set_requires_grad(self.text_encoder				,		__a)
							set_requires_grad(self.clip_model				,		__a)



		def 						UpperCAmelCase							(					self				,		__a = "auto")       ->       Union[str, Any]:






							'''simple docstring'''
							if slice_size == "auto":
												# half the attention head size is usually a good trade-off between
												# speed and memory
												_UpperCamelCase							 =  self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__a)



		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							self.enable_attention_slicing(__a)



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							set_requires_grad(self.vae				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Union[str, Any]:






							'''simple docstring'''
							set_requires_grad(self.vae				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Tuple:






							'''simple docstring'''
							set_requires_grad(self.unet				,		__a)



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							set_requires_grad(self.unet				,		__a)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       Any:






							'''simple docstring'''
							# get the original timestep using init_timestep
							_UpperCamelCase							 =  min(int(num_inference_steps * strength)				,		__a)

							_UpperCamelCase							 =  max(num_inference_steps - init_timestep				,		0)
							_UpperCamelCase							 =  self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a=None)       ->       Tuple:






							'''simple docstring'''
							if not isinstance(__a				,		torch.Tensor):
												raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(__a)}''')

							_UpperCamelCase							 =  image.to(device=__a				,		dtype=__a)

							if isinstance(__a				,		__a):
												_UpperCamelCase							 =  [
												    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(__a)
												]
												_UpperCamelCase							 =  torch.cat(__a				,		dim=0)
							else:
												_UpperCamelCase							 =  self.vae.encode(__a).latent_dist.sample(__a)

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							_UpperCamelCase							 =  0.1_8215 * init_latents
							_UpperCamelCase							 =  init_latents.repeat_interleave(__a				,		dim=0)

							_UpperCamelCase							 =  randn_tensor(init_latents.shape				,		generator=__a				,		device=__a				,		dtype=__a)

							# get latents
							_UpperCamelCase							 =  self.scheduler.add_noise(__a				,		__a				,		__a)
							_UpperCamelCase							 =  init_latents

							return latents



		def 						UpperCAmelCase							(					self				,		__a)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  self.coca_transform(__a).unsqueeze(0)
							with torch.no_grad(), torch.cuda.amp.autocast():
												_UpperCamelCase							 =  self.coca_model.generate(transformed_image.to(device=self.device				,		dtype=self.coca_model.dtype))
							_UpperCamelCase							 =  self.coca_tokenizer.decode(generated[0].cpu().numpy())
							return generated.split('''<end_of_text>''')[0].replace('''<start_of_text>'''				,		'''''').rstrip(''' .,''')



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  self.feature_extractor.preprocess(__a)
							_UpperCamelCase							 =  torch.from_numpy(clip_image_input['''pixel_values'''][0]).unsqueeze(0).to(self.device).half()
							_UpperCamelCase							 =  self.clip_model.get_image_features(__a)
							_UpperCamelCase							 =  image_embeddings_clip / image_embeddings_clip.norm(p=2				,		dim=-1				,		keepdim=__a)
							_UpperCamelCase							 =  image_embeddings_clip.repeat_interleave(__a				,		dim=0)
							return image_embeddings_clip



		@torch.enable_grad()
		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		)       ->       Tuple:






							'''simple docstring'''
							_UpperCamelCase							 =  latents.detach().requires_grad_()

							_UpperCamelCase							 =  self.scheduler.scale_model_input(__a				,		__a)

							# predict the noise residual
							_UpperCamelCase							 =  self.unet(__a				,		__a				,		encoder_hidden_states=__a).sample

							if isinstance(self.scheduler				,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
												_UpperCamelCase							 =  self.scheduler.alphas_cumprod[timestep]
												_UpperCamelCase							 =  1 - alpha_prod_t
												# compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												_UpperCamelCase							 =  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

												_UpperCamelCase							 =  torch.sqrt(__a)
												_UpperCamelCase							 =  pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler				,		__a):
												_UpperCamelCase							 =  self.scheduler.sigmas[index]
												_UpperCamelCase							 =  latents - sigma * noise_pred
							else:
												raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							_UpperCamelCase							 =  1 / 0.1_8215 * sample
							_UpperCamelCase							 =  self.vae.decode(__a).sample
							_UpperCamelCase							 =  (image / 2 + 0.5).clamp(0				,		1)

							_UpperCamelCase							 =  transforms.Resize(self.feature_extractor_size)(__a)
							_UpperCamelCase							 =  self.normalize(__a).to(latents.dtype)

							_UpperCamelCase							 =  self.clip_model.get_image_features(__a)
							_UpperCamelCase							 =  image_embeddings_clip / image_embeddings_clip.norm(p=2				,		dim=-1				,		keepdim=__a)

							_UpperCamelCase							 =  spherical_dist_loss(__a				,		__a).mean() * clip_guidance_scale

							_UpperCamelCase							 =  -torch.autograd.grad(__a				,		__a)[0]

							if isinstance(self.scheduler				,		__a):
												_UpperCamelCase							 =  latents.detach() + grads * (sigma**2)
												_UpperCamelCase							 =  noise_pred_original
							else:
												_UpperCamelCase							 =  noise_pred_original - torch.sqrt(__a) * grads
							return noise_pred, latents




		@torch.no_grad()
		def __call__(					self				,		__a				,		__a				,		__a = None				,		__a = None				,		__a = 5_12				,		__a = 5_12				,		__a = 0.6				,		__a = 50				,		__a = 7.5				,		__a = 1				,		__a = 0.0				,		__a = 1_00				,		__a = None				,		__a = "pil"				,		__a = True				,		__a = 0.8				,		__a = 0.1				,		__a = 0.1				,		)       ->       Dict:






							'''simple docstring'''
							if isinstance(__a				,		__a) and len(__a) != batch_size:
												raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(__a)} generators.''')

							if height % 8 != 0 or width % 8 != 0:
												raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

							if isinstance(__a				,		torch.Generator) and batch_size > 1:
												_UpperCamelCase							 =  [generator] + [None] * (batch_size - 1)

							_UpperCamelCase							 =  [
							    ('''model''', self.coca_model is None),
							    ('''tokenizer''', self.coca_tokenizer is None),
							    ('''transform''', self.coca_transform is None),
							]
							_UpperCamelCase							 =  [x[0] for x in coca_is_none if x[1]]
							_UpperCamelCase							 =  ''', '''.join(__a)
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
												if len(__a):
																	raise ValueError(
																	    F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
																	    F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
												_UpperCamelCase							 =  self.get_image_description(__a)
							if style_prompt is None:
												if len(__a):
																	raise ValueError(
																	    F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
																	    F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
												_UpperCamelCase							 =  self.get_image_description(__a)

							# get prompt text embeddings for content and style
							_UpperCamelCase							 =  self.tokenizer(
							    __a				,		padding='''max_length'''				,		max_length=self.tokenizer.model_max_length				,		truncation=__a				,		return_tensors='''pt'''				,		)
							_UpperCamelCase							 =  self.text_encoder(content_text_input.input_ids.to(self.device))[0]

							_UpperCamelCase							 =  self.tokenizer(
							    __a				,		padding='''max_length'''				,		max_length=self.tokenizer.model_max_length				,		truncation=__a				,		return_tensors='''pt'''				,		)
							_UpperCamelCase							 =  self.text_encoder(style_text_input.input_ids.to(self.device))[0]

							_UpperCamelCase							 =  slerp(__a				,		__a				,		__a)

							# duplicate text embeddings for each generation per prompt
							_UpperCamelCase							 =  text_embeddings.repeat_interleave(__a				,		dim=0)

							# set timesteps
							_UpperCamelCase							 =  '''offset''' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
							_UpperCamelCase							 =  {}
							if accepts_offset:
												_UpperCamelCase							 =  1

							self.scheduler.set_timesteps(__a				,		**__a)
							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							self.scheduler.timesteps.to(self.device)

							_UpperCamelCase ,  _UpperCamelCase							 =  self.get_timesteps(__a				,		__a				,		self.device)
							_UpperCamelCase							 =  timesteps[:1].repeat(__a)

							# Preprocess image
							_UpperCamelCase							 =  preprocess(__a				,		__a				,		__a)
							_UpperCamelCase							 =  self.prepare_latents(
							    __a				,		__a				,		__a				,		text_embeddings.dtype				,		self.device				,		__a)

							_UpperCamelCase							 =  preprocess(__a				,		__a				,		__a)
							_UpperCamelCase							 =  self.prepare_latents(
							    __a				,		__a				,		__a				,		text_embeddings.dtype				,		self.device				,		__a)

							_UpperCamelCase							 =  slerp(__a				,		__a				,		__a)

							if clip_guidance_scale > 0:
												_UpperCamelCase							 =  self.get_clip_image_embeddings(__a				,		__a)
												_UpperCamelCase							 =  self.get_clip_image_embeddings(__a				,		__a)
												_UpperCamelCase							 =  slerp(
												    __a				,		__a				,		__a)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							_UpperCamelCase							 =  guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
												_UpperCamelCase							 =  content_text_input.input_ids.shape[-1]
												_UpperCamelCase							 =  self.tokenizer(['''''']				,		padding='''max_length'''				,		max_length=__a				,		return_tensors='''pt''')
												_UpperCamelCase							 =  self.text_encoder(uncond_input.input_ids.to(self.device))[0]
												# duplicate unconditional embeddings for each generation per prompt
												_UpperCamelCase							 =  uncond_embeddings.repeat_interleave(__a				,		dim=0)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												_UpperCamelCase							 =  torch.cat([uncond_embeddings, text_embeddings])

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							_UpperCamelCase							 =  (batch_size, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCamelCase							 =  text_embeddings.dtype
							if latents is None:
												if self.device.type == "mps":
																	# randn does not work reproducibly on mps
																	_UpperCamelCase							 =  torch.randn(__a				,		generator=__a				,		device='''cpu'''				,		dtype=__a).to(
																	    self.device)
												else:
																	_UpperCamelCase							 =  torch.randn(__a				,		generator=__a				,		device=self.device				,		dtype=__a)
							else:
												if latents.shape != latents_shape:
																	raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
												_UpperCamelCase							 =  latents.to(self.device)

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCamelCase							 =  latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							_UpperCamelCase							 =  '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
							_UpperCamelCase							 =  {}
							if accepts_eta:
												_UpperCamelCase							 =  eta

							# check if the scheduler accepts generator
							_UpperCamelCase							 =  '''generator''' in set(inspect.signature(self.scheduler.step).parameters.keys())
							if accepts_generator:
												_UpperCamelCase							 =  generator

							with self.progress_bar(total=__a):
												for i, t in enumerate(__a):
																	# expand the latents if we are doing classifier free guidance
																	_UpperCamelCase							 =  torch.cat([latents] * 2) if do_classifier_free_guidance else latents
																	_UpperCamelCase							 =  self.scheduler.scale_model_input(__a				,		__a)

																	# predict the noise residual
																	_UpperCamelCase							 =  self.unet(__a				,		__a				,		encoder_hidden_states=__a).sample

																	# perform classifier free guidance
																	if do_classifier_free_guidance:
																						_UpperCamelCase ,  _UpperCamelCase							 =  noise_pred.chunk(2)
																						_UpperCamelCase							 =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# perform clip guidance
																	if clip_guidance_scale > 0:
																						_UpperCamelCase							 =  (
																						    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
																						)
																						_UpperCamelCase ,  _UpperCamelCase							 =  self.cond_fn(
																						    __a				,		__a				,		__a				,		__a				,		__a				,		__a				,		__a				,		)

																	# compute the previous noisy sample x_t -> x_t-1
																	_UpperCamelCase							 =  self.scheduler.step(__a				,		__a				,		__a				,		**__a).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							_UpperCamelCase							 =  1 / 0.1_8215 * latents
							_UpperCamelCase							 =  self.vae.decode(__a).sample

							_UpperCamelCase							 =  (image / 2 + 0.5).clamp(0				,		1)
							_UpperCamelCase							 =  image.cpu().permute(0				,		2				,		3				,		1).numpy()

							if output_type == "pil":
												_UpperCamelCase							 =  self.numpy_to_pil(__a)

							if not return_dict:
												return (image, None)

							return StableDiffusionPipelineOutput(images=__a				,		nsfw_content_detected=__a)






