



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 	_lowercase							(   lowerCAmelCase_,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							KandinskyVaaControlnetImgaImgPipeline
	__A							=							["image_embeds", "negative_image_embeds", "image", "hint"]
	__A							=							["image_embeds", "negative_image_embeds", "image", "hint"]
	__A							=							[
	    "generator",
	    "height",
	    "width",
	    "strength",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	__A							=							False




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 32




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 32




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.time_input_dim




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.time_input_dim * 4




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 100




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						torch.manual_seed(0		)

						a					     =   {
						    '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,
						}

						a					     =   UNetaDConditionModel(**__a		)
						return model




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return {
						    "block_out_channels": [32, 32, 64, 64],
						    "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": 12,
						    "out_channels": 3,
						    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.dummy_unet
						a					     =   self.dummy_movq

						a					     =   {
						    'num_train_timesteps': 1000,
						    'beta_schedule': 'linear',
						    'beta_start': 0.0_0085,
						    'beta_end': 0.012,
						    'clip_sample': False,
						    'set_alpha_to_one': False,
						    'steps_offset': 0,
						    'prediction_type': 'epsilon',
						    'thresholding': False,
						}

						a					     =   DDIMScheduler(**__a		)

						a					     =   {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0		):
						"""simple docstring"""

						a					     =   floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(__a		)		).to(__a		)
						a					     =   floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(seed + 1		)		).to(
						    __a		)
						# create init_image
						a					     =   floats_tensor((1, 3, 64, 64)		,       rng=random.Random(__a		)		).to(__a		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		)[0]
						a					     =   Image.fromarray(np.uinta(__a		)		).convert("RGB"		).resize((256, 256)		)
						# create hint
						a					     =   floats_tensor((1, 3, 64, 64)		,       rng=random.Random(__a		)		).to(__a		)

						if str(__a		).startswith("mps"		):
											a					     =   torch.manual_seed(__a		)
						else:
											a					     =   torch.Generator(device=__a		).manual_seed(__a		)
						a					     =   {
						    'image': init_image,
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'hint': hint,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'num_inference_steps': 10,
						    'guidance_scale': 7.0,
						    'strength': 0.2,
						    'output_type': 'np',
						}
						return inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   'cpu'

						a					     =   self.get_dummy_components()

						a					     =   self.pipeline_class(**__a		)
						a					     =   pipe.to(__a		)

						pipe.set_progress_bar_config(disable=__a		)

						a					     =   pipe(**self.get_dummy_inputs(__a		)		)
						a					     =   output.images

						a					     =   pipe(
						    **self.get_dummy_inputs(__a		)		,       return_dict=__a		,       )[0]

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

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

						a					     =   np.array(
						    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]		)
						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 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
						a					     =   init_image.resize((512, 512)		)

						a					     =   load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinskyv22/hint_image_cat.png"		)
						a					     =   torch.from_numpy(np.array(__a		)		).float() / 255.0
						a					     =   hint.permute(2		,       0		,       1		).unsqueeze(0		)

						a					     =   'A robot, 4k photo'

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

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

						pipeline.set_progress_bar_config(disable=__a		)

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

						a					     =   pipe_prior(
						    __a		,       image=__a		,       strength=0.85		,       generator=__a		,       negative_prompt=""		,       ).to_tuple()

						a					     =   pipeline(
						    image=__a		,       image_embeds=__a		,       negative_image_embeds=__a		,       hint=__a		,       generator=__a		,       num_inference_steps=100		,       height=512		,       width=512		,       strength=0.5		,       output_type="np"		,       )

						a					     =   output.images[0]

						assert image.shape == (512, 512, 3)

						assert_mean_pixel_difference(__a		,       __a		)




'''simple docstring'''



#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'   ) as fh:
										fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_EX   )
										try:
															print(*_SCREAMING_SNAKE_CASE   )
										finally:
															fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_UN   )


__lowercase	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[str]       =	f'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, Any]       =	dist.get_world_size()

	printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(f'''{gpu} is broken''')
	raise



