






"""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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[Any]    =				KandinskyVaaInpaintPipeline
	_A       :				Any    =				["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
	_A       :				Any    =				[
	    """image_embeds""",
	    """negative_image_embeds""",
	    """image""",
	    """mask_image""",
	]
	_A       :				List[Any]    =				[
	    """generator""",
	    """height""",
	    """width""",
	    """latents""",
	    """guidance_scale""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	_A       :				Optional[Any]    =				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 100





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

		A_		:						Union[str, 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,
		}

		A_		:						Any	    =   UNetaDConditionModel(**lowerCAmelCase_							)
		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							)
		A_		:						str	    =   VQModel(**self.dummy_movq_kwargs							)
		return model





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   self.dummy_unet
		A_		:						Optional[Any]	    =   self.dummy_movq

		A_		:						int	    =   DDIMScheduler(
		    num_train_timesteps=1000     ,     beta_schedule="""linear"""     ,     beta_start=0.00085     ,     beta_end=0.012     ,     clip_sample=lowerCAmelCase_     ,     set_alpha_to_one=lowerCAmelCase_     ,     steps_offset=1     ,     prediction_type="""epsilon"""     ,     thresholding=lowerCAmelCase_     ,     )

		A_		:						int	    =   {
		    """unet""": unet,
		    """scheduler""": scheduler,
		    """movq""": movq,
		}

		return components





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=0							):
		A_		:						Tuple	    =   floats_tensor((1, self.text_embedder_hidden_size)     ,     rng=random.Random(lowerCAmelCase_							)							).to(lowerCAmelCase_							)
		A_		:						List[Any]	    =   floats_tensor((1, self.text_embedder_hidden_size)     ,     rng=random.Random(seed + 1							)							).to(
		    lowerCAmelCase_							)
		# create init_image
		A_		:						Dict	    =   floats_tensor((1, 3, 64, 64)     ,     rng=random.Random(lowerCAmelCase_							)							).to(lowerCAmelCase_							)
		A_		:						List[str]	    =   image.cpu().permute(0     ,     2     ,     3     ,     1							)[0]
		A_		:						List[str]	    =   Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert("""RGB"""							).resize((256, 256)							)
		# create mask
		A_		:						Any	    =   np.ones((64, 64)     ,     dtype=np.floataa							)
		A_		:						Dict	    =   0

		if str(lowerCAmelCase_							).startswith("""mps"""							):
			A_		:						List[str]	    =   torch.manual_seed(lowerCAmelCase_							)
		else:
			A_		:						List[Any]	    =   torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
		A_		:						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							):
		A_		:						Optional[int]	    =   """cpu"""

		A_		:						List[Any]	    =   self.get_dummy_components()

		A_		:						str	    =   self.pipeline_class(**lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   pipe.to(lowerCAmelCase_							)

		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						List[str]	    =   pipe(**self.get_dummy_inputs(lowerCAmelCase_							)							)
		A_		:						Any	    =   output.images

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

		A_		:						Any	    =   image[0, -3:, -3:, -1]
		A_		:						int	    =   image_from_tuple[0, -3:, -3:, -1]

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

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

		A_		:						Optional[Any]	    =   np.array(
		    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]							)

		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      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




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





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""							)

		A_		:						Union[str, Any]	    =   load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
		A_		:						List[str]	    =   np.ones((768, 768)     ,     dtype=np.floataa							)
		A_		:						List[Any]	    =   0

		A_		:						int	    =   """a hat"""

		A_		:						List[Any]	    =   KandinskyVaaPriorPipeline.from_pretrained(
		    """kandinsky-community/kandinsky-2-2-prior"""     ,     torch_dtype=torch.floataa							)
		pipe_prior.to(lowerCAmelCase_							)

		A_		:						Optional[int]	    =   KandinskyVaaInpaintPipeline.from_pretrained(
		    """kandinsky-community/kandinsky-2-2-decoder-inpaint"""     ,     torch_dtype=torch.floataa							)
		A_		:						Optional[int]	    =   pipeline.to(lowerCAmelCase_							)
		pipeline.set_progress_bar_config(disable=lowerCAmelCase_							)

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

		A_		:						Any	    =   pipeline(
		    image=lowerCAmelCase_     ,     mask_image=lowerCAmelCase_     ,     image_embeds=lowerCAmelCase_     ,     negative_image_embeds=lowerCAmelCase_     ,     generator=lowerCAmelCase_     ,     num_inference_steps=100     ,     height=768     ,     width=768     ,     output_type="""np"""     ,     )

		A_		:						Tuple	    =   output.images[0]

		assert image.shape == (768, 768, 3)

		assert_mean_pixel_difference(lowerCAmelCase_     ,     lowerCAmelCase_							)









"""simple docstring"""
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_lowerCAmelCase         =							logging.getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   git.Repo(search_parent_directories=snake_case__					)
	A_		:						List[str]	    =   {
	    """repo_id""": str(snake_case__					),
	    """repo_sha""": str(repo.head.object.hexsha					),
	    """repo_branch""": str(repo.active_branch					),
	}

	with open(os.path.join(snake_case__   ,   """git_log.json"""					)   ,   """w"""					) as f:
		json.dump(snake_case__   ,   snake_case__   ,   indent=4					)






def 			__UpperCamelCase	(		snake_case__					):
	if params.n_gpu <= 0:
		A_		:						Dict	    =   0
		A_		:						str	    =   -1
		A_		:						int	    =   True
		A_		:						Union[str, Any]	    =   False
		return

	assert torch.cuda.is_available()

	logger.info("""Initializing GPUs"""					)
	if params.n_gpu > 1:
		assert params.local_rank != -1

		A_		:						str	    =   int(os.environ["""WORLD_SIZE"""]					)
		A_		:						int	    =   int(os.environ["""N_GPU_NODE"""]					)
		A_		:						int	    =   int(os.environ["""RANK"""]					)

		# number of nodes / node ID
		A_		:						Optional[int]	    =   params.world_size // params.n_gpu_per_node
		A_		:						Optional[int]	    =   params.global_rank // params.n_gpu_per_node
		A_		:						Tuple	    =   True

		assert params.n_nodes == int(os.environ["""N_NODES"""]					)
		assert params.node_id == int(os.environ["""NODE_RANK"""]					)

	# local job (single GPU)
	else:
		assert params.local_rank == -1

		A_		:						Dict	    =   1
		A_		:						Tuple	    =   0
		A_		:						Dict	    =   0
		A_		:						Optional[int]	    =   0
		A_		:						List[str]	    =   1
		A_		:						List[Any]	    =   1
		A_		:						List[Any]	    =   False

	# sanity checks
	assert params.n_nodes >= 1
	assert 0 <= params.node_id < params.n_nodes
	assert 0 <= params.local_rank <= params.global_rank < params.world_size
	assert params.world_size == params.n_nodes * params.n_gpu_per_node

	# define whether this is the master process / if we are in multi-node distributed mode
	A_		:						Optional[int]	    =   params.node_id == 0 and params.local_rank == 0
	A_		:						Optional[Any]	    =   params.n_nodes > 1

	# summary
	A_		:						str	    =   F"""--- Global rank: {params.global_rank} - """
	logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes					)
	logger.info(PREFIX + """Node ID        : %i""" % params.node_id					)
	logger.info(PREFIX + """Local rank     : %i""" % params.local_rank					)
	logger.info(PREFIX + """World size     : %i""" % params.world_size					)
	logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node					)
	logger.info(PREFIX + """Master         : %s""" % str(params.is_master					)					)
	logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node					)					)
	logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu					)					)
	logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()					)

	# set GPU device
	torch.cuda.set_device(params.local_rank					)

	# initialize multi-GPU
	if params.multi_gpu:
		logger.info("""Initializing PyTorch distributed"""					)
		torch.distributed.init_process_group(
		    init_method="""env://"""   ,   backend="""nccl"""   ,   )






def 			__UpperCamelCase	(		snake_case__					):
	np.random.seed(args.seed					)
	torch.manual_seed(args.seed					)
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed					)

