
"""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  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdirname:
											# pipeline has Flax weights
											SCREAMING_SNAKE_CASE_   = FlaxDiffusionPipeline.from_pretrained(
											    '''hf-internal-testing/tiny-stable-diffusion-pipe'''       ,       safety_checker=UpperCamelCase__       ,       cache_dir=UpperCamelCase__    )

											SCREAMING_SNAKE_CASE_   = [t[-1] for t in os.walk(os.path.join(UpperCamelCase__       ,       os.listdir(UpperCamelCase__    )[0]       ,       '''snapshots'''    )    )]
											SCREAMING_SNAKE_CASE_   = [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  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_   = FlaxStableDiffusionPipeline.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-pipe'''       ,       safety_checker=UpperCamelCase__    )

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

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

									SCREAMING_SNAKE_CASE_   = jax.device_count()
									SCREAMING_SNAKE_CASE_   = num_samples * [prompt]
									SCREAMING_SNAKE_CASE_   = pipeline.prepare_inputs(UpperCamelCase__    )

									# shard inputs and rng
									SCREAMING_SNAKE_CASE_   = replicate(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = jax.random.split(UpperCamelCase__       ,       UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = shard(UpperCamelCase__    )

									SCREAMING_SNAKE_CASE_   = pipeline(UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       jit=UpperCamelCase__    ).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(UpperCamelCase__       ,       dtype=np.floataa    ).sum() - 4_99_47.8_75    ) < 5e-1

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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_   = FlaxStableDiffusionPipeline.from_pretrained(
									    '''CompVis/stable-diffusion-v1-4'''       ,       revision='''flax'''       ,       safety_checker=UpperCamelCase__    )

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

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

									SCREAMING_SNAKE_CASE_   = jax.device_count()
									SCREAMING_SNAKE_CASE_   = num_samples * [prompt]
									SCREAMING_SNAKE_CASE_   = pipeline.prepare_inputs(UpperCamelCase__    )

									# shard inputs and rng
									SCREAMING_SNAKE_CASE_   = replicate(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = jax.random.split(UpperCamelCase__       ,       UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = shard(UpperCamelCase__    )

									SCREAMING_SNAKE_CASE_   = pipeline(UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       jit=UpperCamelCase__    ).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_65_24_01)    ) < 1e-3
											assert np.abs((np.abs(UpperCamelCase__       ,       dtype=np.floataa    ).sum() - 2_38_38_08.2)    ) < 5e-1

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

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

									SCREAMING_SNAKE_CASE_   = jax.device_count()
									SCREAMING_SNAKE_CASE_   = num_samples * [prompt]
									SCREAMING_SNAKE_CASE_   = pipeline.prepare_inputs(UpperCamelCase__    )

									# shard inputs and rng
									SCREAMING_SNAKE_CASE_   = replicate(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = jax.random.split(UpperCamelCase__       ,       UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = shard(UpperCamelCase__    )

									SCREAMING_SNAKE_CASE_   = pipeline(UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       jit=UpperCamelCase__    ).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_00_39_06)    ) < 1e-3
											assert np.abs((np.abs(UpperCamelCase__       ,       dtype=np.floataa    ).sum() - 2_37_35_16.75)    ) < 5e-1

							def _lowercase					(self    ):

									"""simple docstring"""







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

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

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

									SCREAMING_SNAKE_CASE_   = jax.device_count()
									SCREAMING_SNAKE_CASE_   = num_samples * [prompt]
									SCREAMING_SNAKE_CASE_   = pipeline.prepare_inputs(UpperCamelCase__    )

									# shard inputs and rng
									SCREAMING_SNAKE_CASE_   = replicate(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = jax.random.split(UpperCamelCase__       ,       UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = shard(UpperCamelCase__    )

									SCREAMING_SNAKE_CASE_   = pipeline(UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       jit=UpperCamelCase__    ).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_00_39_06)    ) < 1e-3
											assert np.abs((np.abs(UpperCamelCase__       ,       dtype=np.floataa    ).sum() - 2_37_35_16.75)    ) < 5e-1

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_   = FlaxDDIMScheduler(
									    beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       beta_schedule='''scaled_linear'''       ,       set_alpha_to_one=UpperCamelCase__       ,       steps_offset=1       ,       )

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

									SCREAMING_SNAKE_CASE_   = scheduler_state

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

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

									SCREAMING_SNAKE_CASE_   = jax.device_count()
									SCREAMING_SNAKE_CASE_   = num_samples * [prompt]
									SCREAMING_SNAKE_CASE_   = pipeline.prepare_inputs(UpperCamelCase__    )

									# shard inputs and rng
									SCREAMING_SNAKE_CASE_   = replicate(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = jax.random.split(UpperCamelCase__       ,       UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = shard(UpperCamelCase__    )

									SCREAMING_SNAKE_CASE_   = pipeline(UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       jit=UpperCamelCase__    ).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() - 0.0_45_04_39_45)    ) < 1e-3
											assert np.abs((np.abs(UpperCamelCase__       ,       dtype=np.floataa    ).sum() - 2_34_76_93.5)    ) < 5e-1







							def _lowercase					(self    ):

									"""simple docstring"""







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

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

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

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

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

									SCREAMING_SNAKE_CASE_   = replicate(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = pipeline.prepare_inputs(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = shard(UpperCamelCase__    )
									SCREAMING_SNAKE_CASE_   = pipeline(UpperCamelCase__       ,       UpperCamelCase__       ,       UpperCamelCase__       ,       jit=UpperCamelCase__    ).images
									assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_   = 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

"""simple docstring"""




from __future__ import annotations



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = 0
		SCREAMING_SNAKE_CASE_						 = len(__a    ) - 1

		while i < j:
				if nums[i] + nums[j] == target:
						return [i, j]
				elif nums[i] + nums[j] < target:
						SCREAMING_SNAKE_CASE_						 = i + 1
				else:
						SCREAMING_SNAKE_CASE_						 = j - 1

		return []


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f'''{two_pointer([2, 7, 11, 15], 9) = }''')