
import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
 import jax
 import jax.numpy as jnp
@slow
@require_flax
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Optional[Any]   ):






      """simple docstring"""



      return F'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE__   ) for s in shape]   )}.npy'

 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			List[str]=0					, SCREAMING_SNAKE_CASE__			:			List[Any]=(4, 4, 64, 64)					, SCREAMING_SNAKE_CASE__			:			Tuple=False   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.bfloataa if fpaa else jnp.floataa
      UpperCamelCase						    =					jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )					, dtype=SCREAMING_SNAKE_CASE__   )
      return image

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			str=False					, SCREAMING_SNAKE_CASE__			:			Any="CompVis/stable-diffusion-v1-4"   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.bfloataa if fpaa else jnp.floataa
      UpperCamelCase						    =					'bf16' if fpaa else None

      UpperCamelCase    ,		UpperCamelCase						    =					FlaxUNetaDConditionModel.from_pretrained(
          SCREAMING_SNAKE_CASE__					, subfolder='unet'					, dtype=SCREAMING_SNAKE_CASE__					, revision=SCREAMING_SNAKE_CASE__   )
      return model, params

 def      __lowerCAmelCase  (						self			:			str					, SCREAMING_SNAKE_CASE__			:			int=0					, SCREAMING_SNAKE_CASE__			:			Dict=(4, 77, 7_68)					, SCREAMING_SNAKE_CASE__			:			str=False   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.bfloataa if fpaa else jnp.floataa
      UpperCamelCase						    =					jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )   )					, dtype=SCREAMING_SNAKE_CASE__   )
      return hidden_states

 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
         [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
         [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
         [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
         # fmt: on
     ]   )
 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'					, fpaa=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.get_latents(SCREAMING_SNAKE_CASE__					, fpaa=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE__					, fpaa=SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					model.apply(
          {'params': params}					, SCREAMING_SNAKE_CASE__					, jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.intaa   )					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, ).sample

      assert sample.shape == latents.shape

      UpperCamelCase						    =					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())   )					, dtype=jnp.floataa   )
      UpperCamelCase						    =					jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
      assert jnp.allclose(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, atol=1e-2   )






 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
         [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
         [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
         [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
         # fmt: on
     ]   )
 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase    ,		UpperCamelCase						    =					self.get_unet_model(model_id='stabilityai/stable-diffusion-2'					, fpaa=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.get_latents(SCREAMING_SNAKE_CASE__					, shape=(4, 4, 96, 96)					, fpaa=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE__					, shape=(4, 77, 10_24)					, fpaa=SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					model.apply(
          {'params': params}					, SCREAMING_SNAKE_CASE__					, jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.intaa   )					, encoder_hidden_states=SCREAMING_SNAKE_CASE__					, ).sample

      assert sample.shape == latents.shape

      UpperCamelCase						    =					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())   )					, dtype=jnp.floataa   )
      UpperCamelCase						    =					jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
      assert jnp.allclose(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__					, atol=1e-2   )




def  __lowerCamelCase					( _lowercase = 50000000			)       ->      int:
     UpperCamelCase						    =					set()
     UpperCamelCase						    =					int((limit - 24) ** (1 / 2)			)

     UpperCamelCase						    =					set(range(3						,     prime_square_limit + 1						,     2			)			)
     primes.add(2			)
     for p in range(3						,     prime_square_limit + 1						,     2			):
          if p not in primes:
               continue
          primes.difference_update(set(range(p * p						,     prime_square_limit + 1						,     _lowercase			)			)			)

     for primea in primes:
          UpperCamelCase						    =					primea * primea
          for primea in primes:
               UpperCamelCase						    =					primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    UpperCamelCase						    =					primea * primea * primea * primea
                    UpperCamelCase						    =					square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(_lowercase			)

     return len(_lowercase			)


if __name__ == "__main__":
 print(F"{solution() = }")


