
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     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			def  lowerCAmelCase__		(    self	:    Any ):
							super().tearDown()
							gc.collect()

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							int		=  FlaxStableDiffusionPipeline.from_pretrained(
							    """stabilityai/stable-diffusion-2"""  ,		revision="""bf16"""  ,		dtype=jnp.bfloataa  ,		)

							UpperCamelCase_:							Any		=  """A painting of a squirrel eating a burger"""
							UpperCamelCase_:							Union[str, Any]		=  jax.device_count()
							UpperCamelCase_:							List[str]		=  num_samples * [prompt]
							UpperCamelCase_:							Optional[Any]		=  sd_pipe.prepare_inputs(_SCREAMING_SNAKE_CASE )

							UpperCamelCase_:							List[str]		=  replicate(_SCREAMING_SNAKE_CASE )
							UpperCamelCase_:							Any		=  shard(_SCREAMING_SNAKE_CASE )

							UpperCamelCase_:							int		=  jax.random.PRNGKey(0 )
							UpperCamelCase_:							Tuple		=  jax.random.split(_SCREAMING_SNAKE_CASE  ,		jax.device_count() )

							UpperCamelCase_:							Dict		=  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_:							Union[str, Any]		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
							UpperCamelCase_:							int		=  images[0, 253:256, 253:256, -1]

							UpperCamelCase_:							Any		=  jnp.asarray(jax.device_get(image_slice.flatten() ) )
							UpperCamelCase_:							str		=  jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512] )
							print(f'''output_slice: {output_slice}''' )
							assert jnp.abs(output_slice - expected_slice ).max() < 1e-2




			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							Optional[Any]		=  """stabilityai/stable-diffusion-2"""
							UpperCamelCase_:							Dict		=  FlaxDPMSolverMultistepScheduler.from_pretrained(_SCREAMING_SNAKE_CASE  ,		subfolder="""scheduler""" )
							UpperCamelCase_:							Optional[Any]		=  FlaxStableDiffusionPipeline.from_pretrained(
							    _SCREAMING_SNAKE_CASE  ,		scheduler=_SCREAMING_SNAKE_CASE  ,		revision="""bf16"""  ,		dtype=jnp.bfloataa  ,		)
							UpperCamelCase_:							Optional[Any]		=  scheduler_params

							UpperCamelCase_:							int		=  """A painting of a squirrel eating a burger"""
							UpperCamelCase_:							Optional[Any]		=  jax.device_count()
							UpperCamelCase_:							Optional[Any]		=  num_samples * [prompt]
							UpperCamelCase_:							Any		=  sd_pipe.prepare_inputs(_SCREAMING_SNAKE_CASE )

							UpperCamelCase_:							Optional[int]		=  replicate(_SCREAMING_SNAKE_CASE )
							UpperCamelCase_:							Dict		=  shard(_SCREAMING_SNAKE_CASE )

							UpperCamelCase_:							int		=  jax.random.PRNGKey(0 )
							UpperCamelCase_:							Tuple		=  jax.random.split(_SCREAMING_SNAKE_CASE  ,		jax.device_count() )

							UpperCamelCase_:							List[str]		=  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_:							Dict		=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
							UpperCamelCase_:							Optional[Any]		=  images[0, 253:256, 253:256, -1]

							UpperCamelCase_:							Any		=  jnp.asarray(jax.device_get(image_slice.flatten() ) )
							UpperCamelCase_:							List[Any]		=  jnp.array([0.4336, 0.4_2969, 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








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		list:
				UpperCamelCase_:							Optional[int]		=  word.split()

				def justify(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  ) -> str:
								UpperCamelCase_:							Tuple		=  max_width - width
								UpperCamelCase_:							Optional[Any]		=  len(lowerCamelCase  )
								if len(lowerCamelCase  ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												UpperCamelCase_:							List[Any]		=  words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												UpperCamelCase_:							Optional[Any]		=  spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												UpperCamelCase_:							List[str]		=  (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowerCamelCase  ):
																num_spaces_between_words_list[i] += 1
												UpperCamelCase_:							Dict		=  []
												for i in range(lowerCamelCase  ):
																# add the word
																aligned_words_list.append(line[i]  )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * """ """  )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]  )
												# join the aligned words list to form a justified line
												return "".join(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  []
				UpperCamelCase_:							list[str]		=  []
				UpperCamelCase_:							List[str]		=  0
				for word in words:
								if width + len(lowerCamelCase  ) + len(lowerCamelCase  ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowerCamelCase  )
												width += len(lowerCamelCase  )
								else:
												# justify the line and add it to result
												answer.append(justify(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )  )
												# reset new line and new width
												UpperCamelCase_,				UpperCamelCase_:							List[str]		=  [word], len(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  max_width - width - len(lowerCamelCase  )
				answer.append(""" """.join(lowerCamelCase  ) + (remaining_spaces + 1) * """ """  )
				return answer


if __name__ == "__main__":
							from doctest import testmod

							testmod()






