



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 		UpperCAmelCase__			(			__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       KandinskyVaaImgaImgPipeline
			UpperCamelCase									=       ["""image_embeds""", """negative_image_embeds""", """image"""]
			UpperCamelCase									=       [
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			]
			UpperCamelCase									=       [
			    """generator""",
			    """height""",
			    """width""",
			    """strength""",
			    """guidance_scale""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			UpperCamelCase									=       False




			@property
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					return 32




			@property
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return 32




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return self.time_input_dim




			@property
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					return self.time_input_dim * 4




			@property
			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					return 1_00




			@property
			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					torch.manual_seed(0 )

					__UpperCAmelCase    :     Dict													=	{
					    '''in_channels''': 4,
					    # 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,
					}

					__UpperCAmelCase    :     Any													=	UNetaDConditionModel(**a_ )
					return model




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					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  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	VQModel(**self.dummy_movq_kwargs )
					return model




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.dummy_unet
					__UpperCAmelCase    :     int													=	self.dummy_movq

					__UpperCAmelCase    :     Any													=	{
					    '''num_train_timesteps''': 10_00,
					    '''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    :     Union[str, Any]													=	DDIMScheduler(**a_ )

					__UpperCAmelCase    :     int													=	{
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''movq''': movq,
					}

					return components




			def  snake_case__     (   self							: Dict       ,       a_							: Union[str, Any]       ,       a_							: List[Any]=0 ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(a_ ) ).to(a_ )
					__UpperCAmelCase    :     Tuple													=	floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(seed + 1 ) ).to(
					    a_ )
					# create init_image
					__UpperCAmelCase    :     Optional[Any]													=	floats_tensor((1, 3, 64, 64)       ,       rng=random.Random(a_ ) ).to(a_ )
					__UpperCAmelCase    :     List[Any]													=	image.cpu().permute(0       ,       2       ,       3       ,       1 )[0]
					__UpperCAmelCase    :     Tuple													=	Image.fromarray(np.uinta(a_ ) ).convert('''RGB''' ).resize((2_56, 2_56) )

					if str(a_ ).startswith('''mps''' ):
							__UpperCAmelCase    :     Dict													=	torch.manual_seed(a_ )
					else:
							__UpperCAmelCase    :     List[Any]													=	torch.Generator(device=a_ ).manual_seed(a_ )
					__UpperCAmelCase    :     Optional[Any]													=	{
					    '''image''': init_image,
					    '''image_embeds''': image_embeds,
					    '''negative_image_embeds''': negative_image_embeds,
					    '''generator''': generator,
					    '''height''': 64,
					    '''width''': 64,
					    '''num_inference_steps''': 10,
					    '''guidance_scale''': 7.0,
					    '''strength''': 0.2,
					    '''output_type''': '''np''',
					}
					return inputs




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	'''cpu'''

					__UpperCAmelCase    :     Any													=	self.get_dummy_components()

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

					pipe.set_progress_bar_config(disable=a_ )

					__UpperCAmelCase    :     Tuple													=	pipe(**self.get_dummy_inputs(a_ ) )
					__UpperCAmelCase    :     List[str]													=	output.images

					__UpperCAmelCase    :     List[str]													=	pipe(
					    **self.get_dummy_inputs(a_ )       ,       return_dict=a_       ,       )[0]

					__UpperCAmelCase    :     Dict													=	image[0, -3:, -3:, -1]
					__UpperCAmelCase    :     List[str]													=	image_from_tuple[0, -3:, -3:, -1]

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

					__UpperCAmelCase    :     Dict													=	np.array(
					    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
					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 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





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




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )

					__UpperCAmelCase    :     List[str]													=	load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
					__UpperCAmelCase    :     str													=	'''A red cartoon frog, 4k'''

					__UpperCAmelCase    :     Union[str, Any]													=	KandinskyVaaPriorPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-2-prior'''       ,       torch_dtype=torch.floataa )
					pipe_prior.to(a_ )

					__UpperCAmelCase    :     Union[str, Any]													=	KandinskyVaaImgaImgPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-2-decoder'''       ,       torch_dtype=torch.floataa )
					__UpperCAmelCase    :     int													=	pipeline.to(a_ )

					pipeline.set_progress_bar_config(disable=a_ )

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

					__UpperCAmelCase    :     Any													=	pipeline(
					    image=a_       ,       image_embeds=a_       ,       negative_image_embeds=a_       ,       generator=a_       ,       num_inference_steps=1_00       ,       height=7_68       ,       width=7_68       ,       strength=0.2       ,       output_type='''np'''       ,       )

					__UpperCAmelCase    :     str													=	output.images[0]

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

					assert_mean_pixel_difference(a_       ,       a_ )










#
# 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        a						(      *_UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





		with open(_UpperCAmelCase ,   '''r'''						) as fh:
				fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_EX						)
				try:
						print(*_UpperCAmelCase						)
				finally:
						fcntl.flock(_UpperCAmelCase ,   fcntl.LOCK_UN						)


__A   			=int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__A   			=torch.device("cuda", local_rank)
__A   			=socket.gethostname()

__A   			=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
				__A   			=dist.get_rank()
				__A   			=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





