






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

																	_A								= [t[-1] for t in os.walk(os.path.join(__lowercase		,     os.listdir(__lowercase   )[0]		,     '''snapshots'''   )   )]
																	_A								= [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:   Any   )    ->				int:
												_A							,_A								= FlaxStableDiffusionPipeline.from_pretrained(
												    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,     safety_checker=__lowercase   )

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

												_A								= jax.random.PRNGKey(0   )
												_A								= 4

												_A								= jax.device_count()
												_A								= num_samples * [prompt]
												_A								= pipeline.prepare_inputs(__lowercase   )

												# shard inputs and rng
												_A								= replicate(__lowercase   )
												_A								= jax.random.split(__lowercase		,     __lowercase   )
												_A								= shard(__lowercase   )

												_A								= pipeline(__lowercase		,     __lowercase		,     __lowercase		,     __lowercase		,     jit=__lowercase   ).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_514_745   ) < 1e-3
																	assert np.abs(np.abs(__lowercase		,     dtype=np.floataa   ).sum() - 49_947.875   ) < 5e-1

												_A								= pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]   )   )   )
												assert len(__lowercase   ) == num_samples

							def       __A (  self:   Union[str, Any]   )    ->				str:
												_A							,_A								= FlaxStableDiffusionPipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''		,     revision='''flax'''		,     safety_checker=__lowercase   )

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

												_A								= jax.random.PRNGKey(0   )
												_A								= 50

												_A								= jax.device_count()
												_A								= num_samples * [prompt]
												_A								= pipeline.prepare_inputs(__lowercase   )

												# shard inputs and rng
												_A								= replicate(__lowercase   )
												_A								= jax.random.split(__lowercase		,     __lowercase   )
												_A								= shard(__lowercase   )

												_A								= pipeline(__lowercase		,     __lowercase		,     __lowercase		,     __lowercase		,     jit=__lowercase   ).images

												assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
												if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,     dtype=np.floataa   ).sum() - 0.05_652_401)   ) < 1e-3
																	assert np.abs((np.abs(__lowercase		,     dtype=np.floataa   ).sum() - 2_383_808.2)   ) < 5e-1

							def       __A (  self:   int   )    ->				List[Any]:
												_A							,_A								= FlaxStableDiffusionPipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''		,     revision='''bf16'''		,     dtype=jnp.bfloataa		,     safety_checker=__lowercase   )

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

												_A								= jax.random.PRNGKey(0   )
												_A								= 50

												_A								= jax.device_count()
												_A								= num_samples * [prompt]
												_A								= pipeline.prepare_inputs(__lowercase   )

												# shard inputs and rng
												_A								= replicate(__lowercase   )
												_A								= jax.random.split(__lowercase		,     __lowercase   )
												_A								= shard(__lowercase   )

												_A								= pipeline(__lowercase		,     __lowercase		,     __lowercase		,     __lowercase		,     jit=__lowercase   ).images

												assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
												if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,     dtype=np.floataa   ).sum() - 0.04_003_906)   ) < 1e-3
																	assert np.abs((np.abs(__lowercase		,     dtype=np.floataa   ).sum() - 2_373_516.75)   ) < 5e-1

							def       __A (  self:   Dict   )    ->				Tuple:
												_A							,_A								= FlaxStableDiffusionPipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''		,     revision='''bf16'''		,     dtype=jnp.bfloataa   )

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

												_A								= jax.random.PRNGKey(0   )
												_A								= 50

												_A								= jax.device_count()
												_A								= num_samples * [prompt]
												_A								= pipeline.prepare_inputs(__lowercase   )

												# shard inputs and rng
												_A								= replicate(__lowercase   )
												_A								= jax.random.split(__lowercase		,     __lowercase   )
												_A								= shard(__lowercase   )

												_A								= pipeline(__lowercase		,     __lowercase		,     __lowercase		,     __lowercase		,     jit=__lowercase   ).images

												assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
												if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,     dtype=np.floataa   ).sum() - 0.04_003_906)   ) < 1e-3
																	assert np.abs((np.abs(__lowercase		,     dtype=np.floataa   ).sum() - 2_373_516.75)   ) < 5e-1

							def       __A (  self:   Union[str, Any]   )    ->				List[Any]:
												_A								= FlaxDDIMScheduler(
												    beta_start=0.00_085		,     beta_end=0.012		,     beta_schedule='''scaled_linear'''		,     set_alpha_to_one=__lowercase		,     steps_offset=1		,     )

												_A							,_A								= FlaxStableDiffusionPipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''		,     revision='''bf16'''		,     dtype=jnp.bfloataa		,     scheduler=__lowercase		,     safety_checker=__lowercase		,     )
												_A								= scheduler.create_state()

												_A								= scheduler_state

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

												_A								= jax.random.PRNGKey(0   )
												_A								= 50

												_A								= jax.device_count()
												_A								= num_samples * [prompt]
												_A								= pipeline.prepare_inputs(__lowercase   )

												# shard inputs and rng
												_A								= replicate(__lowercase   )
												_A								= jax.random.split(__lowercase		,     __lowercase   )
												_A								= shard(__lowercase   )

												_A								= pipeline(__lowercase		,     __lowercase		,     __lowercase		,     __lowercase		,     jit=__lowercase   ).images

												assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
												if jax.device_count() == 8:
																	assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,     dtype=np.floataa   ).sum() - 0.045_043_945)   ) < 1e-3
																	assert np.abs((np.abs(__lowercase		,     dtype=np.floataa   ).sum() - 2_347_693.5)   ) < 5e-1



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

												_A								= jax.device_count()
												_A								= num_samples * [prompt]
												_A								= jax.random.split(jax.random.PRNGKey(0   )		,     __lowercase   )

												_A							,_A								= FlaxStableDiffusionPipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''		,     revision='''bf16'''		,     dtype=jnp.bfloataa		,     safety_checker=__lowercase		,     )

												_A								= replicate(__lowercase   )
												_A								= pipeline.prepare_inputs(__lowercase   )
												_A								= shard(__lowercase   )
												_A								= pipeline(__lowercase		,     __lowercase		,     __lowercase		,     jit=__lowercase   ).images
												assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
												_A								= images[2, 0, 2_56, 10:17, 1]

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

												_A								= replicate(__lowercase   )
												_A								= pipeline.prepare_inputs(__lowercase   )
												_A								= shard(__lowercase   )
												_A								= pipeline(__lowercase		,     __lowercase		,     __lowercase		,     jit=__lowercase   ).images
												assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
												_A								= images[2, 0, 2_56, 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











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



