

import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
	import jax
	import jax.numpy as jnp
	from flax.jax_utils import replicate
	from flax.training.common_utils import shard

@slow
@require_flax
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							super().tearDown()
							gc.collect()


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__			, A__						=	FlaxStableDiffusionPipeline.from_pretrained(
							    "stabilityai/stable-diffusion-2"					,					revision="bf16"					,					dtype=jnp.bfloataa					,					)

							A__						=	"A painting of a squirrel eating a burger"
							A__						=	jax.device_count()
							A__						=	num_samples * [prompt]
							A__						=	sd_pipe.prepare_inputs(_lowerCamelCase				)

							A__						=	replicate(_lowerCamelCase				)
							A__						=	shard(_lowerCamelCase				)

							A__						=	jax.random.PRNGKey(0				)
							A__						=	jax.random.split(_lowerCamelCase					,					jax.device_count()				)

							A__						=	sd_pipe(_lowerCamelCase					,					_lowerCamelCase					,					_lowerCamelCase					,					num_inference_steps=25					,					jit=_lowerCamelCase				)[0]
							assert images.shape == (jax.device_count(), 1, 768, 768, 3)

							A__						=	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
							A__						=	images[0, 253:256, 253:256, -1]

							A__						=	jnp.asarray(jax.device_get(image_slice.flatten()				)				)
							A__						=	jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]				)
							print(F'output_slice: {output_slice}'				)
							assert jnp.abs(output_slice - expected_slice				).max() < 1e-2


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	"stabilityai/stable-diffusion-2"
							A__			, A__						=	FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase					,					subfolder="scheduler"				)
							A__			, A__						=	FlaxStableDiffusionPipeline.from_pretrained(
							    _lowerCamelCase					,					scheduler=_lowerCamelCase					,					revision="bf16"					,					dtype=jnp.bfloataa					,					)
							A__						=	scheduler_params

							A__						=	"A painting of a squirrel eating a burger"
							A__						=	jax.device_count()
							A__						=	num_samples * [prompt]
							A__						=	sd_pipe.prepare_inputs(_lowerCamelCase				)

							A__						=	replicate(_lowerCamelCase				)
							A__						=	shard(_lowerCamelCase				)

							A__						=	jax.random.PRNGKey(0				)
							A__						=	jax.random.split(_lowerCamelCase					,					jax.device_count()				)

							A__						=	sd_pipe(_lowerCamelCase					,					_lowerCamelCase					,					_lowerCamelCase					,					num_inference_steps=25					,					jit=_lowerCamelCase				)[0]
							assert images.shape == (jax.device_count(), 1, 768, 768, 3)

							A__						=	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
							A__						=	images[0, 253:256, 253:256, -1]

							A__						=	jnp.asarray(jax.device_get(image_slice.flatten()				)				)
							A__						=	jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]				)
							print(F'output_slice: {output_slice}'				)
							assert jnp.abs(output_slice - expected_slice				).max() < 1e-2









"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

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


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


