import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class __snake_case				(						A__		):




   def __init__(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      data




   def __iter__(    self			:				Union[str, Any]      ):



     """simple docstring"""






     for element in self.data:
       yield element





def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str=True	)							->					List[str]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Accelerator(even_batches=_lowerCAmelCase	)
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Accelerator ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       bool = False	)							->					List[Any]:







  """simple docstring"""





  if iterable:
    SCREAMING_SNAKE_CASE__										=      DummyIterableDataset(torch.as_tensor(range(_lowerCAmelCase	)	)	)
  else:
    SCREAMING_SNAKE_CASE__										=      TensorDataset(torch.as_tensor(range(_lowerCAmelCase	)	)	)

  SCREAMING_SNAKE_CASE__										=      DataLoader(_lowerCAmelCase ,     batch_size=_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      accelerator.prepare(_lowerCAmelCase	)

  return dl



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Accelerator ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       List[int] ,     __UpperCamelCase    :       List[int] ,     )							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      create_dataloader(accelerator=_lowerCAmelCase ,     dataset_size=_lowerCAmelCase ,     batch_size=_lowerCAmelCase	)

  SCREAMING_SNAKE_CASE__										=      [len(batch[0]	) for batch in dl]

  if accelerator.process_index == 0:
    assert batch_sizes == process_0_expected_batch_sizes
  elif accelerator.process_index == 1:
    assert batch_sizes == process_1_expected_batch_sizes



def 	__SCREAMING_SNAKE_CASE  (					)							->					Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      create_accelerator()

  # without padding, we would expect a different number of batches
  verify_dataloader_batch_sizes(
      _lowerCAmelCase ,     dataset_size=3 ,     batch_size=1 ,     process_0_expected_batch_sizes=[1, 1] ,     process_1_expected_batch_sizes=[1, 1] ,     )

  # without padding, we would expect the same number of batches, but different sizes
  verify_dataloader_batch_sizes(
      _lowerCAmelCase ,     dataset_size=7 ,     batch_size=2 ,     process_0_expected_batch_sizes=[2, 2] ,     process_1_expected_batch_sizes=[2, 2] ,     )



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      create_accelerator(even_batches=_lowerCAmelCase	)

  verify_dataloader_batch_sizes(
      _lowerCAmelCase ,     dataset_size=3 ,     batch_size=1 ,     process_0_expected_batch_sizes=[1, 1] ,     process_1_expected_batch_sizes=[1] ,     )

  verify_dataloader_batch_sizes(
      _lowerCAmelCase ,     dataset_size=7 ,     batch_size=2 ,     process_0_expected_batch_sizes=[2, 2] ,     process_1_expected_batch_sizes=[2, 1] ,     )



def 	__SCREAMING_SNAKE_CASE  (					)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      create_accelerator(even_batches=_lowerCAmelCase	)

  SCREAMING_SNAKE_CASE__										=      torch.nn.Linear(1 ,     1	)
  SCREAMING_SNAKE_CASE__										=      accelerator.prepare(_lowerCAmelCase	)

  SCREAMING_SNAKE_CASE__										=      create_dataloader(_lowerCAmelCase ,     dataset_size=3 ,     batch_size=1	)

  SCREAMING_SNAKE_CASE__										=      []
  with accelerator.join_uneven_inputs([ddp_model]	):
    for batch_idx, batch in enumerate(_lowerCAmelCase	):
      SCREAMING_SNAKE_CASE__										=      ddp_model(batch[0].float()	)
      SCREAMING_SNAKE_CASE__										=      output.sum()
      loss.backward()
      batch_idxs.append(_lowerCAmelCase	)

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  with warnings.catch_warnings(record=_lowerCAmelCase	) as w:
    with accelerator.join_uneven_inputs([Mock()]	):
      pass

    assert issubclass(w[-1].category ,     _lowerCAmelCase	)
    assert "only supported for multi-GPU" in str(w[-1].message	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      True
  SCREAMING_SNAKE_CASE__										=      False
  SCREAMING_SNAKE_CASE__										=      create_accelerator(even_batches=_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      torch.nn.Linear(1 ,     1	)
  SCREAMING_SNAKE_CASE__										=      accelerator.prepare(_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      create_dataloader(_lowerCAmelCase ,     dataset_size=3 ,     batch_size=1	)
  SCREAMING_SNAKE_CASE__										=      create_dataloader(_lowerCAmelCase ,     dataset_size=3 ,     batch_size=1	)

  with accelerator.join_uneven_inputs([ddp_model] ,     even_batches=_lowerCAmelCase	):
    SCREAMING_SNAKE_CASE__										=      train_dl.batch_sampler.even_batches
    SCREAMING_SNAKE_CASE__										=      valid_dl.batch_sampler.even_batches

  assert train_dl_overridden_value == overridden_even_batches
  assert valid_dl_overridden_value == overridden_even_batches
  assert train_dl.batch_sampler.even_batches == default_even_batches
  assert valid_dl.batch_sampler.even_batches == default_even_batches



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      True
  SCREAMING_SNAKE_CASE__										=      False
  SCREAMING_SNAKE_CASE__										=      create_accelerator(even_batches=_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      torch.nn.Linear(1 ,     1	)
  SCREAMING_SNAKE_CASE__										=      accelerator.prepare(_lowerCAmelCase	)
  create_dataloader(_lowerCAmelCase ,     dataset_size=3 ,     batch_size=1 ,     iterable=_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      create_dataloader(_lowerCAmelCase ,     dataset_size=3 ,     batch_size=1	)

  with warnings.catch_warnings():
    warnings.filterwarnings("""ignore"""	)
    try:
      with accelerator.join_uneven_inputs([ddp_model] ,     even_batches=_lowerCAmelCase	):
        SCREAMING_SNAKE_CASE__										=      batch_dl.batch_sampler.even_batches
    except AttributeError:
      # ensure attribute error is not raised when processing iterable dl
      raise AssertionError

  assert batch_dl_overridden_value == overridden_even_batches
  assert batch_dl.batch_sampler.even_batches == default_even_batches



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      create_accelerator()
  SCREAMING_SNAKE_CASE__										=      torch.nn.Linear(1 ,     1	)
  SCREAMING_SNAKE_CASE__										=      accelerator.prepare(_lowerCAmelCase	)
  create_dataloader(_lowerCAmelCase ,     dataset_size=3 ,     batch_size=1 ,     iterable=_lowerCAmelCase	)

  with warnings.catch_warnings(record=_lowerCAmelCase	) as w:
    with accelerator.join_uneven_inputs([ddp_model] ,     even_batches=_lowerCAmelCase	):
      pass

    assert issubclass(w[-1].category ,     _lowerCAmelCase	)
    assert "only supported for map-style datasets" in str(w[-1].message	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      create_accelerator()

  accelerator.print("""Test that even_batches variable ensures uniform batches across processes"""	)
  test_default_ensures_even_batch_sizes()

  accelerator.print("""Run tests with even_batches disabled"""	)
  test_can_disable_even_batches()

  accelerator.print("""Test joining uneven inputs"""	)
  test_can_join_uneven_inputs()

  accelerator.print("""Test overriding even_batches when joining uneven inputs"""	)
  test_join_can_override_even_batches()

  accelerator.print("""Test overriding even_batches for mixed dataloader types"""	)
  test_join_can_override_for_mixed_type_dataloaders()

  accelerator.print("""Test overriding even_batches raises a warning for iterable dataloaders"""	)
  test_join_raises_warning_for_iterable_when_overriding_even_batches()

  accelerator.print("""Test join with non DDP distributed raises warning"""	)
  SCREAMING_SNAKE_CASE__										=      accelerator.state.distributed_type
  SCREAMING_SNAKE_CASE__										=      DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(_lowerCAmelCase	)
  SCREAMING_SNAKE_CASE__										=      original_state


if __name__ == "__main__":
   main()





'''simple docstring'''






import os
import tempfile
import unittest

import numpy as np

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


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

						from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline

@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     str		)			->		int:
								with tempfile.TemporaryDirectory() as tmpdirname:
															# pipeline has Flax weights
															UpperCAmelCase :       Optional[Any] 							=		FlaxDiffusionPipeline.from_pretrained(
															    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		,			cache_dir=__snake_case		)

															UpperCAmelCase :       str 							=		[t[-1] for t in os.walk(os.path.join(__snake_case		,			os.listdir(__snake_case		)[0]		,			'''snapshots'''		)		)]
															UpperCAmelCase :       str 							=		[item for sublist in all_root_files for item in sublist]

															# None of the downloaded files should be a PyTorch file even if we have some here:
															# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
															assert not any(f.endswith('''.bin'''		) for f in files		)

@slow
@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       str 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Optional[Any] 							=		4

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Tuple 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 64, 64, 3)
								if jax.device_count() == 8:
															assert np.abs(np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 4.1_51_47_45		) < 1E-3
															assert np.abs(np.abs(__snake_case		,			dtype=np.floataa		).sum() - 4_99_47.8_75		) < 5E-1

								UpperCAmelCase :       Union[str, Any] 							=		pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
								assert len(__snake_case		) == num_samples







	def      A	(						self			:     List[Any]		)			->		List[str]:
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''flax'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Optional[Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Any 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       int 							=		num_samples * [prompt]
								UpperCAmelCase :       Union[str, Any] 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Tuple 							=		shard(__snake_case		)

								UpperCAmelCase :       Tuple 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.05_65_24_01)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_38_38_08.2)		) < 5E-1







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       List[str] 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     int		)			->		Any:
								UpperCAmelCase							,		UpperCAmelCase :       Dict 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		)

								UpperCAmelCase :       List[str] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[int] 							=		jax.device_count()
								UpperCAmelCase :       List[str] 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       str 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		FlaxDDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			set_alpha_to_one=__snake_case		,			steps_offset=1		,			)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			scheduler=__snake_case		,			safety_checker=__snake_case		,			)
								UpperCAmelCase :       Tuple 							=		scheduler.create_state()

								UpperCAmelCase :       Dict 							=		scheduler_state

								UpperCAmelCase :       str 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       int 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Any 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       str 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[str] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Dict 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.0_45_04_39_45)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_34_76_93.5)		) < 5E-1







	def      A	(						self			:     Any		)			->		Tuple:
								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       str 							=		jax.random.split(jax.random.PRNGKey(0		)		,			__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			)

								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[str] 							=		shard(__snake_case		)
								UpperCAmelCase :       Any 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       Optional[int] 							=		images[2, 0, 256, 10:17, 1]

								# With memory efficient attention
								UpperCAmelCase							,		UpperCAmelCase :       Any 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			use_memory_efficient_attention=__snake_case		,			)

								UpperCAmelCase :       int 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		shard(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images_eff.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       int 							=		images[2, 0, 256, 10:17, 1]

								# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
								# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
								assert abs(slice_eff - slice		).max() < 1E-2





