




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  ):



 '''simple docstring'''


 def 							a__					(							self   :    Union[str, Any]    )  ->      str:

       """simple docstring"""






       super().tearDown()
       gc.collect()

 def 							a__					(							self   :    Any    )  ->      Tuple:

       """simple docstring"""






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

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

       lowerCamelCase_							=       replicate(snake_case__    )
       lowerCamelCase_							=       shard(snake_case__    )

       lowerCamelCase_							=       jax.random.PRNGKey(0    )
       lowerCamelCase_							=       jax.random.split(snake_case__		,				jax.device_count()    )

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

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

       lowerCamelCase_							=       jnp.asarray(jax.device_get(image_slice.flatten()    )    )
       lowerCamelCase_							=       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 							a__					(							self   :    Dict    )  ->      List[str]:

       """simple docstring"""






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

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

       lowerCamelCase_							=       replicate(snake_case__    )
       lowerCamelCase_							=       shard(snake_case__    )

       lowerCamelCase_							=       jax.random.PRNGKey(0    )
       lowerCamelCase_							=       jax.random.split(snake_case__		,				jax.device_count()    )

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

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

       lowerCamelCase_							=       jnp.asarray(jax.device_get(image_slice.flatten()    )    )
       lowerCamelCase_							=       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 __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return math.sqrt(sum(pow(a - b  ,							2  ) for a, b in zip(lowerCamelCase_  ,							lowerCamelCase_  )  )  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  list[list[list[float] | float]]:







			"""simple docstring"""





			if dataset.ndim != value_array.ndim:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (
						    'Wrong input data\'s dimensions... '
						    F'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
						)
						raise ValueError(lowerCamelCase_  )

			try:
						if dataset.shape[1] != value_array.shape[1]:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      (
									    'Wrong input data\'s shape... '
									    F'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
									)
									raise ValueError(lowerCamelCase_  )
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError('Wrong shape'  )

			if dataset.dtype != value_array.dtype:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      (
						    'Input data have different datatype... '
						    F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
						)
						raise TypeError(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      []

			for value in value_array:
						SCREAMING_SNAKE_CASE_   : str					  =      euclidean(lowerCamelCase_  ,							dataset[0]  )
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      dataset[0].tolist()

						for dataset_value in dataset[1:]:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      euclidean(lowerCamelCase_  ,							lowerCamelCase_  )

									if dist > temp_dist:
												SCREAMING_SNAKE_CASE_   : Optional[int]					  =      temp_dist
												SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dataset_value.tolist()

						answer.append([vector, dist]  )

			return answer





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return np.dot(lowerCamelCase_  ,							lowerCamelCase_  ) / (norm(lowerCamelCase_  ) * norm(lowerCamelCase_  ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



