






'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE       ,unittest.TestCase	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					KandinskyVaaControlnetImgaImgPipeline
			__a							:							int					     =					["image_embeds", "negative_image_embeds", "image", "hint"]
			__a							:							Optional[int]					     =					["image_embeds", "negative_image_embeds", "image", "hint"]
			__a							:							Dict					     =					[
			    "generator",
			    "height",
			    "width",
			    "strength",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			__a							:							Any					     =					False






			@property
			def 				A      (   self							:						List[str]			)							->    Tuple:

									'''simple docstring'''







									return 3_2






			@property
			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







									return 3_2






			@property
			def 				A      (   self							:						int			)							->    Tuple:

									'''simple docstring'''







									return self.time_input_dim






			@property
			def 				A      (   self							:						List[Any]			)							->    int:

									'''simple docstring'''







									return self.time_input_dim * 4






			@property
			def 				A      (   self							:						Any			)							->    Optional[int]:

									'''simple docstring'''







									return 1_0_0






			@property
			def 				A      (   self							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									torch.manual_seed(0			)

									UpperCamelCase__         = {
									    """in_channels""": 8,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 8,
									    """addition_embed_type""": """image_hint""",
									    """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,
									}

									UpperCamelCase__         = UNetaDConditionModel(**lowercase			)
									return model






			@property
			def 				A      (   self							:						Optional[int]			)							->    Any:

									'''simple docstring'''







									return {
									    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
									    "down_block_types": [
									        "DownEncoderBlock2D",
									        "DownEncoderBlock2D",
									        "DownEncoderBlock2D",
									        "AttnDownEncoderBlock2D",
									    ],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 1_2,
									    "out_channels": 3,
									    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "vq_embed_dim": 4,
									}






			@property
			def 				A      (   self							:						Optional[int]			)							->    str:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = VQModel(**self.dummy_movq_kwargs			)
									return model






			def 				A      (   self							:						int			)							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = self.dummy_unet
									UpperCamelCase__         = self.dummy_movq

									UpperCamelCase__         = {
									    """num_train_timesteps""": 1_0_0_0,
									    """beta_schedule""": """linear""",
									    """beta_start""": 0.0_0_0_8_5,
									    """beta_end""": 0.0_1_2,
									    """clip_sample""": False,
									    """set_alpha_to_one""": False,
									    """steps_offset""": 0,
									    """prediction_type""": """epsilon""",
									    """thresholding""": False,
									}

									UpperCamelCase__         = DDIMScheduler(**lowercase			)

									UpperCamelCase__         = {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}

									return components






			def 				A      (   self							:						List[str] ,      lowercase							:						Tuple ,      lowercase							:						List[str]=0			)							->    Union[str, Any]:

									'''simple docstring'''







									UpperCamelCase__         = floats_tensor((1, self.text_embedder_hidden_size) ,      rng=random.Random(lowercase			)			).to(lowercase			)
									UpperCamelCase__         = floats_tensor((1, self.text_embedder_hidden_size) ,      rng=random.Random(seed + 1			)			).to(
									    lowercase			)
									# create init_image
									UpperCamelCase__         = floats_tensor((1, 3, 6_4, 6_4) ,      rng=random.Random(lowercase			)			).to(lowercase			)
									UpperCamelCase__         = image.cpu().permute(0 ,      2 ,      3 ,      1			)[0]
									UpperCamelCase__         = Image.fromarray(np.uinta(lowercase			)			).convert("""RGB"""			).resize((2_5_6, 2_5_6)			)
									# create hint
									UpperCamelCase__         = floats_tensor((1, 3, 6_4, 6_4) ,      rng=random.Random(lowercase			)			).to(lowercase			)

									if str(lowercase			).startswith("""mps"""			):
															UpperCamelCase__         = torch.manual_seed(lowercase			)
									else:
															UpperCamelCase__         = torch.Generator(device=lowercase			).manual_seed(lowercase			)
									UpperCamelCase__         = {
									    """image""": init_image,
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """hint""": hint,
									    """generator""": generator,
									    """height""": 6_4,
									    """width""": 6_4,
									    """num_inference_steps""": 1_0,
									    """guidance_scale""": 7.0,
									    """strength""": 0.2,
									    """output_type""": """np""",
									}
									return inputs







			def 				A      (   self							:						int			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = """cpu"""

									UpperCamelCase__         = self.get_dummy_components()

									UpperCamelCase__         = self.pipeline_class(**lowercase			)
									UpperCamelCase__         = pipe.to(lowercase			)

									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = pipe(**self.get_dummy_inputs(lowercase			)			)
									UpperCamelCase__         = output.images

									UpperCamelCase__         = pipe(
									    **self.get_dummy_inputs(lowercase			) ,      return_dict=lowercase ,      )[0]

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

									assert image.shape == (1, 6_4, 6_4, 3)

									UpperCamelCase__         = np.array(
									    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]			)
									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()}"
@slow
@require_torch_gpu
class    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





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

									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 				A      (   self							:						Dict			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""			)

									UpperCamelCase__         = load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
									UpperCamelCase__         = init_image.resize((5_1_2, 5_1_2)			)

									UpperCamelCase__         = load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinskyv22/hint_image_cat.png"""			)
									UpperCamelCase__         = torch.from_numpy(np.array(lowercase			)			).float() / 2_5_5.0
									UpperCamelCase__         = hint.permute(2 ,      0 ,      1			).unsqueeze(0			)

									UpperCamelCase__         = """A robot, 4k photo"""

									UpperCamelCase__         = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-prior""" ,      torch_dtype=torch.floataa			)
									pipe_prior.to(lowercase			)

									UpperCamelCase__         = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-2-controlnet-depth""" ,      torch_dtype=torch.floataa			)
									UpperCamelCase__         = pipeline.to(lowercase			)

									pipeline.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = torch.Generator(device="""cpu"""			).manual_seed(0			)

									UpperCamelCase__    ,   UpperCamelCase__         = pipe_prior(
									    lowercase ,      image=lowercase ,      strength=0.8_5 ,      generator=lowercase ,      negative_prompt="""""" ,      ).to_tuple()

									UpperCamelCase__         = pipeline(
									    image=lowercase ,      image_embeds=lowercase ,      negative_image_embeds=lowercase ,      hint=lowercase ,      generator=lowercase ,      num_inference_steps=1_0_0 ,      height=5_1_2 ,      width=5_1_2 ,      strength=0.5 ,      output_type="""np""" ,      )

									UpperCamelCase__         = output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)

									assert_mean_pixel_difference(lowercase ,      lowercase			)








'''simple docstring'''


# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						Optional[int] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[Any] ,      lowercase							:						bool = True ,      lowercase							:						bool = False			)							->    Optional[int]:

									'''simple docstring'''







									UpperCamelCase__         = scheduler
									UpperCamelCase__         = optimizers if isinstance(lowercase ,      (list, tuple)			) else [optimizers]
									UpperCamelCase__         = split_batches
									UpperCamelCase__         = step_with_optimizer
									UpperCamelCase__         = GradientState()






			def 				A      (   self							:						int ,      *lowercase							:						int ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									if not self.step_with_optimizer:
															# No link between scheduler and optimizer -> just step
															self.scheduler.step(*lowercase ,      **lowercase			)
															return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
															if self.gradient_state.adjust_scheduler:
																					self.scheduler._step_count += 1
															return

									for opt in self.optimizers:
															if opt.step_was_skipped:
																					return
									if self.split_batches:
															# Split batches -> the training dataloader batch size is not changed so one step per training step
															self.scheduler.step(*lowercase ,      **lowercase			)
									else:
															# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
															# num_processes steps per training step
															UpperCamelCase__         = AcceleratorState().num_processes
															for _ in range(lowercase			):
																					# Special case when using OneCycle and `drop_last` was not used
																					if hasattr(self.scheduler ,      """total_steps"""			):
																											if self.scheduler._step_count <= self.scheduler.total_steps:
																																	self.scheduler.step(*lowercase ,      **lowercase			)
																					else:
																											self.scheduler.step(*lowercase ,      **lowercase			)






			def 				A      (   self							:						List[str]			)							->    Optional[Any]:

									'''simple docstring'''







									return self.scheduler.get_last_lr()






			def 				A      (   self							:						Any			)							->    int:

									'''simple docstring'''







									return self.scheduler.state_dict()






			def 				A      (   self							:						Any ,      lowercase							:						int			)							->    Optional[int]:

									'''simple docstring'''







									self.scheduler.load_state_dict(lowercase			)






			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.scheduler.get_lr()







			def 				A      (   self							:						Union[str, Any] ,      *lowercase							:						List[Any] ,      **lowercase							:						str			)							->    str:

									'''simple docstring'''







									return self.scheduler.print_lr(*lowercase ,      **lowercase			)
