
"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class  A__       (	_lowerCamelCase       ,     unittest.TestCase):
		A_						:       Any				   =	KandinskyVaaInpaintPipeline
		A_						:       Tuple				   =	['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		A_						:       List[str]				   =	[
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		A_						:       List[str]				   =	[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A_						:       Tuple				   =	False

		@property
		def 							__lowerCamelCase							(      self ):
			return 32

		@property
		def 							__lowerCamelCase							(      self ):
			return 32

		@property
		def 							__lowerCamelCase							(      self ):
			return self.time_input_dim

		@property
		def 							__lowerCamelCase							(      self ):
			return self.time_input_dim * 4

		@property
		def 							__lowerCamelCase							(      self ):
			return 1_00

		@property
		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )

			__lowerCAmelCase			:						Optional[Any]									=      {
			    'in_channels': 9,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 8,
			    'addition_embed_type': 'image',
			    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
			    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
			    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
			    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
			    'layers_per_block': 1,
			    'encoder_hid_dim': self.text_embedder_hidden_size,
			    'encoder_hid_dim_type': 'image_proj',
			    'cross_attention_dim': self.cross_attention_dim,
			    'attention_head_dim': 4,
			    'resnet_time_scale_shift': 'scale_shift',
			    'class_embed_type': None,
			}

			__lowerCAmelCase			:						Optional[Any]									=      UNetaDConditionModel(**_SCREAMING_SNAKE_CASE )
			return model

		@property
		def 							__lowerCamelCase							(      self ):
			return {
			    "block_out_channels": [32, 64],
			    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
			    "in_channels": 3,
			    "latent_channels": 4,
			    "layers_per_block": 1,
			    "norm_num_groups": 8,
			    "norm_type": "spatial",
			    "num_vq_embeddings": 12,
			    "out_channels": 3,
			    "up_block_types": [
			        "AttnUpDecoderBlock2D",
			        "UpDecoderBlock2D",
			    ],
			    "vq_embed_dim": 4,
			}

		@property
		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						str									=      VQModel(**self.dummy_movq_kwargs )
			return model

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      self.dummy_unet
			__lowerCAmelCase			:						List[str]									=      self.dummy_movq

			__lowerCAmelCase			:						Union[str, Any]									=      DDIMScheduler(
			    num_train_timesteps=10_00       ,		beta_schedule='linear'       ,		beta_start=0.0_0085       ,		beta_end=0.012       ,		clip_sample=_SCREAMING_SNAKE_CASE       ,		set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,		steps_offset=1       ,		prediction_type='epsilon'       ,		thresholding=_SCREAMING_SNAKE_CASE       ,		)

			__lowerCAmelCase			:						str									=      {
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=0 ):
			__lowerCAmelCase			:						List[Any]									=      floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(seed + 1 ) ).to(
			    _SCREAMING_SNAKE_CASE )
			# create init_image
			__lowerCAmelCase			:						Tuple									=      floats_tensor((1, 3, 64, 64)       ,		rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      image.cpu().permute(0       ,		2       ,		3       ,		1 )[0]
			__lowerCAmelCase			:						List[Any]									=      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((2_56, 2_56) )
			# create mask
			__lowerCAmelCase			:						Optional[Any]									=      np.ones((64, 64)       ,		dtype=np.floataa )
			__lowerCAmelCase			:						Dict									=      0

			if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
				__lowerCAmelCase			:						Dict									=      torch.manual_seed(_SCREAMING_SNAKE_CASE )
			else:
				__lowerCAmelCase			:						Optional[int]									=      torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      {
			    'image': init_image,
			    'mask_image': mask,
			    'image_embeds': image_embeds,
			    'negative_image_embeds': negative_image_embeds,
			    'generator': generator,
			    'height': 64,
			    'width': 64,
			    'num_inference_steps': 2,
			    'guidance_scale': 4.0,
			    'output_type': 'np',
			}
			return inputs

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      'cpu'

			__lowerCAmelCase			:						Union[str, Any]									=      self.get_dummy_components()

			__lowerCAmelCase			:						List[Any]									=      self.pipeline_class(**_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      pipe.to(_SCREAMING_SNAKE_CASE )

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[str]									=      pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
			__lowerCAmelCase			:						Union[str, Any]									=      output.images

			__lowerCAmelCase			:						Optional[Any]									=      pipe(
			    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )       ,		return_dict=_SCREAMING_SNAKE_CASE       ,		)[0]

			__lowerCAmelCase			:						str									=      image[0, -3:, -3:, -1]
			__lowerCAmelCase			:						int									=      image_from_tuple[0, -3:, -3:, -1]

			print(f"image.shape {image.shape}" )

			assert image.shape == (1, 64, 64, 3)

			__lowerCAmelCase			:						List[Any]									=      np.array(
			    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848] )

			assert (
			    np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
			), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
			assert (
			    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
			), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







		def 							__lowerCamelCase							(      self ):
			super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




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

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







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )

			__lowerCAmelCase			:						Optional[int]									=      load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
			__lowerCAmelCase			:						Union[str, Any]									=      np.ones((7_68, 7_68)       ,		dtype=np.floataa )
			__lowerCAmelCase			:						List[str]									=      0

			__lowerCAmelCase			:						Dict									=      'a hat'

			__lowerCAmelCase			:						Optional[int]									=      KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'       ,		torch_dtype=torch.floataa )
			pipe_prior.to(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						str									=      KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,		torch_dtype=torch.floataa )
			__lowerCAmelCase			:						str									=      pipeline.to(_SCREAMING_SNAKE_CASE )
			pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      torch.Generator(device='cpu' ).manual_seed(0 )
			__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      pipe_prior(
			    _SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=5       ,		negative_prompt=''       ,		).to_tuple()

			__lowerCAmelCase			:						int									=      pipeline(
			    image=_SCREAMING_SNAKE_CASE       ,		mask_image=_SCREAMING_SNAKE_CASE       ,		image_embeds=_SCREAMING_SNAKE_CASE       ,		negative_image_embeds=_SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=1_00       ,		height=7_68       ,		width=7_68       ,		output_type='np'       ,		)

			__lowerCAmelCase			:						Optional[int]									=      output.images[0]

			assert image.shape == (7_68, 7_68, 3)

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )





from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed




def 			__magic_name__				( A		:					Tuple,  A		:					List[Any],  A		:					List[Any],  A		:					Dict   ):






			'''simple docstring'''







			for param, grad_param in zip(model_a.parameters(),  model_b.parameters()   ):
						if not param.requires_grad:
									continue
						if not did_step:
									# Grads should not be in sync
									assert (
									    torch.allclose(param.grad,  grad_param.grad   ) is False
									), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
						else:
									# Grads should be in sync
									assert (
									    torch.allclose(param.grad,  grad_param.grad   ) is True
									), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""




def 			__magic_name__				( A		:					List[Any],  A		:					int,  A		:					Optional[Any],  A		:					Optional[int],  A		:					Any=True   ):






			'''simple docstring'''







			model.train()
			a    =			model(A   )
			a    =			F.mse_loss(A,  target.to(output.device   )   )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(A   )




def 			__magic_name__				( A		:					Any,  A		:					Any=False   ):






			'''simple docstring'''







			set_seed(42   )
			a    =			RegressionModel()
			a    =			deepcopy(A   )
			a    =			RegressionDataset(length=80   )
			a    =			DataLoader(A,  batch_size=16   )
			model.to(accelerator.device   )
			if sched:
						a    =			AdamW(params=model.parameters(),  lr=1E-3   )
						a    =			AdamW(params=ddp_model.parameters(),  lr=1E-3   )
						a    =			LambdaLR(A,  lr_lambda=lambda A   : epoch**0.65   )
						a    =			LambdaLR(A,  lr_lambda=lambda A   : epoch**0.65   )
			# Make a copy of `model`
			if sched:
						a    ,		a    ,		a    ,		a    =			accelerator.prepare(A,  A,  A,  A   )
			else:
						a    ,		a    =			accelerator.prepare(A,  A   )
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader




def 			__magic_name__				( A		:					List[Any]   ):






			'''simple docstring'''







			a    ,		a    ,		a    =			get_training_setup(A   )
			# Use a single batch
			a    ,		a    =			next(iter(A   )   ).values()
			for iteration in range(3   ):
						# Gather the distributed inputs and targs for the base model
						a    ,		a    =			accelerator.gather((ddp_input, ddp_target)   )
						a    ,		a    =			input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						step_model(A,  A,  A,  A   )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(A   ):
												step_model(A,  A,  A,  A   )
						else:
									# Sync grads
									step_model(A,  A,  A,  A   )

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(A,  A,  A,  A   )
						for param, ddp_param in zip(model.parameters(),  ddp_model.parameters()   ):
									if not param.requires_grad:
												continue
									assert torch.allclose(
									    param.grad,  ddp_param.grad   ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration   )
						a    =			ddp_input[torch.randperm(len(A   )   )]




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			a    ,		a    ,		a    =			get_training_setup(A   )
			# Use a single batch
			a    ,		a    =			next(iter(A   )   ).values()
			for iteration in range(3   ):
						# Gather the distributed inputs and targs for the base model
						a    ,		a    =			accelerator.gather((ddp_input, ddp_target)   )
						a    ,		a    =			input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						step_model(A,  A,  A,  A   )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(A   ):
												step_model(A,  A,  A,  A   )
						else:
									# Sync grads
									step_model(A,  A,  A,  A   )

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters(),  ddp_model.parameters()   ):
									if not param.requires_grad:
												continue
									if iteration % 2 == 0:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad,  ddp_param.grad   ) is False
												), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
									else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad,  ddp_param.grad   ) is True
												), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration   )
						a    =			ddp_input[torch.randperm(len(A   )   )]




def 			__magic_name__				( A		:					List[Any]=False,  A		:					List[Any]=False   ):






			'''simple docstring'''







			a    =			Accelerator(
			    split_batches=A,  dispatch_batches=A,  gradient_accumulation_steps=2   )
			# Test that context manager behaves properly
			a    ,		a    ,		a    =			get_training_setup(A   )
			for iteration, batch in enumerate(A   ):
						a    ,		a    =			batch.values()
						# Gather the distributed inputs and targs for the base model
						a    ,		a    =			accelerator.gather((ddp_input, ddp_target)   )
						a    ,		a    =			input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						step_model(A,  A,  A,  A,  A   )
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(A   ):
									step_model(A,  A,  A,  A   )

						# DDP model and model should only be in sync when not (iteration % 2 == 0)
						for param, ddp_param in zip(model.parameters(),  ddp_model.parameters()   ):
									if not param.requires_grad:
												continue
									if ((iteration + 1) % 2 == 0) or (iteration == len(A   ) - 1):
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad,  ddp_param.grad   ) is True
												), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
									else:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad,  ddp_param.grad   ) is False
												), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration   )
						a    =			ddp_input[torch.randperm(len(A   )   )]
			GradientState._reset_state()




def 			__magic_name__				( A		:					List[Any]=False,  A		:					Any=False   ):






			'''simple docstring'''







			a    =			Accelerator(
			    split_batches=A,  dispatch_batches=A,  gradient_accumulation_steps=2   )
			# Test that context manager behaves properly
			a    ,		a    ,		a    ,		a    ,		a    ,		a    ,		a    =			get_training_setup(A,  A   )
			for iteration, batch in enumerate(A   ):
						a    ,		a    =			batch.values()
						# Gather the distributed inputs and targs for the base model
						a    ,		a    =			accelerator.gather((ddp_input, ddp_target)   )
						a    ,		a    =			input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						model.train()
						ddp_model.train()
						step_model(A,  A,  A,  A,  A   )
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(A   )):
									if split_batches:
												sched.step()
									else:
												for _ in range(accelerator.num_processes   ):
															sched.step()
						opt.zero_grad()
						# Perform gradient accumulation under wrapper
						with accelerator.accumulate(A   ):
									step_model(A,  A,  A,  A   )
									ddp_opt.step()
									ddp_sched.step()
									ddp_opt.zero_grad()

						# Learning rates should be the same
						assert (
						    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
						), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
						a    =			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(A   ))
						if accelerator.num_processes > 1:
									check_model_parameters(A,  A,  A,  A   )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration   )
			GradientState._reset_state()




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Accelerator()

			a    =			RegressionDataset(length=80   )
			a    =			DataLoader(A,  batch_size=16   )
			a    =			RegressionDataset(length=96   )
			a    =			DataLoader(A,  batch_size=16   )
			a    ,		a    =			accelerator.prepare(A,  A   )
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(A   ):
						assert id(accelerator.gradient_state.active_dataloader   ) == id(A   )
						if iteration < len(A   ) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(A   ):
															assert id(accelerator.gradient_state.active_dataloader   ) == id(A   )
															if batch_num < len(A   ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
															else:
																		assert accelerator.gradient_state.end_of_dataloader
						else:
									assert accelerator.gradient_state.end_of_dataloader
			assert accelerator.gradient_state.active_dataloader is None




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Accelerator()
			a    =			accelerator.state
			if state.local_process_index == 0:
						print("**Test `accumulate` gradient accumulation with dataloader break**"   )
			test_dataloader_break()
			if state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print("**Test NOOP `no_sync` context manager**"   )
						test_noop_sync(A   )
			if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
						if state.local_process_index == 0:
									print("**Test Distributed `no_sync` context manager**"   )
						test_distributed_sync(A   )
			if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
									for dispatch_batches in [True, False]:
												if state.local_process_index == 0:
															print(
															    "**Test `accumulate` gradient accumulation, ",  F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""",  )
												test_gradient_accumulation(A,  A   )

    # Currently will break on torch 2.0 +, need to investigate why
			if is_torch_version("<",  "2.0"   ) or state.distributed_type == DistributedType.NO:
						if state.local_process_index == 0:
									print(
									    "**Test `accumulate` gradient accumulation with optimizer and scheduler, ",  "`split_batches=False`, `dispatch_batches=False`**",  )
						test_gradient_accumulation_with_opt_and_scheduler()
						if state.distributed_type == DistributedType.MULTI_GPU:
									for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
															if not split_batch and not dispatch_batches:
																		continue
															if state.local_process_index == 0:
																		print(
																		    "**Test `accumulate` gradient accumulation with optimizer and scheduler, ",  F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""",  )
															test_gradient_accumulation_with_opt_and_scheduler(A,  A   )




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			main()


if __name__ == "__main__":
		main()






