
"""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=SCREAMING_SNAKE_CASE_							,							cache_dir=SCREAMING_SNAKE_CASE_     )

											SCREAMING_SNAKE_CASE_    			=			[t[-1] for t in os.walk(os.path.join(SCREAMING_SNAKE_CASE_							,							os.listdir(SCREAMING_SNAKE_CASE_     )[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_			,SCREAMING_SNAKE_CASE_    			=			FlaxStableDiffusionPipeline.from_pretrained(
									    '''hf-internal-testing/tiny-stable-diffusion-pipe'''							,							safety_checker=SCREAMING_SNAKE_CASE_     )

									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(SCREAMING_SNAKE_CASE_     )

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

									SCREAMING_SNAKE_CASE_    			=			pipeline(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							jit=SCREAMING_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(SCREAMING_SNAKE_CASE_							,							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(SCREAMING_SNAKE_CASE_     ) == num_samples

							def _lowercase       (self     ):

									"""simple docstring"""







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

									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(SCREAMING_SNAKE_CASE_     )

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

									SCREAMING_SNAKE_CASE_    			=			pipeline(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							jit=SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_							,							dtype=np.floataa     ).sum() - 2_38_38_08.2)     ) < 5e-1

							def _lowercase       (self     ):

									"""simple docstring"""







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

									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(SCREAMING_SNAKE_CASE_     )

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

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

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_			,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(SCREAMING_SNAKE_CASE_     )

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

									SCREAMING_SNAKE_CASE_    			=			pipeline(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							jit=SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_							,							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=SCREAMING_SNAKE_CASE_							,							steps_offset=1							,							)

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

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

									SCREAMING_SNAKE_CASE_    			=			pipeline(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							jit=SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_							,							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     )							,							SCREAMING_SNAKE_CASE_     )

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

									SCREAMING_SNAKE_CASE_    			=			replicate(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			shard(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			pipeline(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							jit=SCREAMING_SNAKE_CASE_     ).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_			,SCREAMING_SNAKE_CASE_    			=			FlaxStableDiffusionPipeline.from_pretrained(
									    '''CompVis/stable-diffusion-v1-4'''							,							revision='''bf16'''							,							dtype=jnp.bfloataa							,							safety_checker=SCREAMING_SNAKE_CASE_							,							use_memory_efficient_attention=SCREAMING_SNAKE_CASE_							,							)

									SCREAMING_SNAKE_CASE_    			=			replicate(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			shard(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			pipeline(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							jit=SCREAMING_SNAKE_CASE_     ).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"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async



def 							_lowerCamelCase			(			__a=None     ):
		if subparsers is not None:
				SCREAMING_SNAKE_CASE_    			=			subparsers.add_parser('''test'''     )
		else:
				SCREAMING_SNAKE_CASE_    			=			argparse.ArgumentParser('''Accelerate test command'''     )

		parser.add_argument(
		    '''--config_file''',				default=__a,				help=(
		        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
		        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
		        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
		        '''with \'huggingface\'.'''
		    ),				)

		if subparsers is not None:
				parser.set_defaults(func=__a     )
		return parser



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			os.path.sep.join(__file__.split(os.path.sep     )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']     )

		if args.config_file is None:
				SCREAMING_SNAKE_CASE_    			=			script_name
		else:
				SCREAMING_SNAKE_CASE_    			=			F'--config_file={args.config_file} {script_name}'

		SCREAMING_SNAKE_CASE_    			=			['''accelerate-launch'''] + test_args.split()
		SCREAMING_SNAKE_CASE_    			=			execute_subprocess_async(__a,				env=os.environ.copy()     )
		if result.returncode == 0:
				print('''Test is a success! You are ready for your distributed training!'''     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			test_command_parser()
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		test_command(__a     )


if __name__ == "__main__":
						main()