


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			):
		def 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
		def 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""simple docstring"""





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

									_UpperCAmelCase 		=			'A painting of a squirrel eating a burger'
									_UpperCAmelCase 		=			jax.device_count()
									_UpperCAmelCase 		=			num_samples * [prompt]
									_UpperCAmelCase 		=			sd_pipe.prepare_inputs(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			replicate(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			shard(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			jax.random.PRNGKey(0						)
									_UpperCAmelCase 		=			jax.random.split(_SCREAMING_SNAKE_CASE			,    jax.device_count()						)

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

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

									_UpperCAmelCase 		=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
									_UpperCAmelCase 		=			jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]						)
									print(f'''output_slice: {output_slice}'''						)
									assert jnp.abs(output_slice - expected_slice						).max() < 1e-2






		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			'stabilityai/stable-diffusion-2'
									_UpperCAmelCase 		=			FlaxDPMSolverMultistepScheduler.from_pretrained(_SCREAMING_SNAKE_CASE			,    subfolder='scheduler'						)
									_UpperCAmelCase 		=			FlaxStableDiffusionPipeline.from_pretrained(
									    _SCREAMING_SNAKE_CASE			,    scheduler=_SCREAMING_SNAKE_CASE			,    revision='bf16'			,    dtype=jnp.bfloataa			,    )
									_UpperCAmelCase 		=			scheduler_params

									_UpperCAmelCase 		=			'A painting of a squirrel eating a burger'
									_UpperCAmelCase 		=			jax.device_count()
									_UpperCAmelCase 		=			num_samples * [prompt]
									_UpperCAmelCase 		=			sd_pipe.prepare_inputs(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			replicate(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			shard(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			jax.random.PRNGKey(0						)
									_UpperCAmelCase 		=			jax.random.split(_SCREAMING_SNAKE_CASE			,    jax.device_count()						)

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

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

									_UpperCAmelCase 		=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
									_UpperCAmelCase 		=			jnp.array([0.4336, 0.42969, 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

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[Any] =					[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in TensorFlowBenchmark.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]	)
   else:
    wrong_args.append(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



