import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()



class    lowercase_    (     unittest.TestCase		):



							@property
							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    1
													_snake_case						:     Optional[int]				=    3
													_snake_case						:     List[str]				=    (32, 32)

													_snake_case						:     int				=    floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0				)				).to(lowercase_				)
													return image



							@property
							def        UpperCamelCase       (						self				):
													torch.manual_seed(0				)
													_snake_case						:     str				=    UNetaDModel(
													    block_out_channels=(32, 64)      ,				layers_per_block=2      ,				sample_size=32      ,				in_channels=6      ,				out_channels=3      ,				down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,				up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,				)
													return model



							@property
							def        UpperCamelCase       (						self				):
													torch.manual_seed(0				)
													_snake_case						:     List[Any]				=    VQModel(
													    block_out_channels=[32, 64]      ,				in_channels=3      ,				out_channels=3      ,				down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,				up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,				latent_channels=3      ,				)
													return model



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[Any]				=    "cpu"
													_snake_case						:     List[str]				=    self.dummy_uncond_unet
													_snake_case						:     List[str]				=    DDIMScheduler()
													_snake_case						:     List[Any]				=    self.dummy_vq_model

													_snake_case						:     Tuple				=    LDMSuperResolutionPipeline(unet=lowercase_      ,				vqvae=lowercase_      ,				scheduler=lowercase_				)
													ldm.to(lowercase_				)
													ldm.set_progress_bar_config(disable=lowercase_				)

													_snake_case						:     int				=    self.dummy_image.to(lowercase_				)

													_snake_case						:     Optional[int]				=    torch.Generator(device=lowercase_				).manual_seed(0				)
													_snake_case						:     Union[str, Any]				=    ldm(image=lowercase_      ,				generator=lowercase_      ,				num_inference_steps=2      ,				output_type="numpy"				).images

													_snake_case						:     Optional[int]				=    image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													_snake_case						:     List[Any]				=    np.array([0.8_678, 0.8_245, 0.6_381, 0.6_830, 0.4_385, 0.5_599, 0.4_641, 0.6_201, 0.5_150]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2



							@unittest.skipIf(torch_device != "cuda"      ,				"This test requires a GPU"				)
							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    self.dummy_uncond_unet
													_snake_case						:     int				=    DDIMScheduler()
													_snake_case						:     Dict				=    self.dummy_vq_model

													# put models in fp16
													_snake_case						:     Dict				=    unet.half()
													_snake_case						:     Optional[int]				=    vqvae.half()

													_snake_case						:     int				=    LDMSuperResolutionPipeline(unet=lowercase_      ,				vqvae=lowercase_      ,				scheduler=lowercase_				)
													ldm.to(lowercase_				)
													ldm.set_progress_bar_config(disable=lowercase_				)

													_snake_case						:     Optional[int]				=    self.dummy_image.to(lowercase_				)

													_snake_case						:     List[Any]				=    ldm(lowercase_      ,				num_inference_steps=2      ,				output_type="numpy"				).images

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




@slow
@require_torch
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[int]				=    load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/vq_diffusion/teddy_bear_pool.png"				)
													_snake_case						:     Tuple				=    init_image.resize((64, 64)      ,				resample=PIL_INTERPOLATION["lanczos"]				)

													_snake_case						:     Union[str, Any]				=    LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution"      ,				device_map="auto"				)
													ldm.set_progress_bar_config(disable=lowercase_				)

													_snake_case						:     str				=    torch.manual_seed(0				)
													_snake_case						:     List[str]				=    ldm(image=lowercase_      ,				generator=lowercase_      ,				num_inference_steps=20      ,				output_type="numpy"				).images

													_snake_case						:     List[str]				=    image[0, -3:, -3:, -1]

													assert image.shape == (1, 256, 256, 3)
													_snake_case						:     List[Any]				=    np.array([0.7_644, 0.7_679, 0.7_642, 0.7_633, 0.7_666, 0.7_560, 0.7_425, 0.7_257, 0.6_907]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(__lowercase						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize("shards_per_node"					,			[1, 2, 3]						)
def 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						def gen(__lowercase						):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17						))

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       Dict:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle(seed=42						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle()

						_snake_case						:     Optional[int]				=    split_dataset_by_node(full_ds.shuffle(seed=42						)					,			rank=0					,			world_size=__lowercase						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(full_ds.shuffle()					,			rank=0					,			world_size=__lowercase						)
@pytest.mark.parametrize("streaming"					,			[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)

@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,			[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,			)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     Dict				=    True
						_snake_case						:     Optional[int]				=    get_torch_dist_unique_port()
						_snake_case						:     List[str]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     List[str]				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     Tuple				=    F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
						_snake_case						:     Any				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)