






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 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			List[Any]							)						->	Tuple:


         """simple docstring"""
         return f"""gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase							) for s in shape]							)}.npy"""


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


         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			List[Any]=0       ,				lowerCamelCase		:			Tuple=(4, 4, 64, 64)       ,				lowerCamelCase		:			Tuple=False							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    jnp.bfloataa if fpaa else jnp.floataa
         _UpperCAmelCase							=    jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase       ,				lowerCamelCase							)							)       ,				dtype=lowerCamelCase							)
         return image


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			str="CompVis/stable-diffusion-v1-4"							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    jnp.bfloataa if fpaa else jnp.floataa
         _UpperCAmelCase							=    """bf16""" if fpaa else None

         _UpperCAmelCase	,			_UpperCAmelCase							=    FlaxUNetaDConditionModel.from_pretrained(
             lowerCamelCase       ,				subfolder="""unet"""       ,				dtype=lowerCamelCase       ,				revision=lowerCamelCase							)
         return model, params


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Any=0       ,				lowerCamelCase		:			Any=(4, 77, 768)       ,				lowerCamelCase		:			Optional[Any]=False							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    jnp.bfloataa if fpaa else jnp.floataa
         _UpperCAmelCase							=    jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase       ,				lowerCamelCase							)							)       ,				dtype=lowerCamelCase							)
         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, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
          # fmt: on
      ]							)
  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_latents(lowerCamelCase       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_encoder_hidden_states(lowerCamelCase       ,				fpaa=lowerCamelCase							)

         _UpperCAmelCase							=    model.apply(
             {"""params""": params}       ,				lowerCamelCase       ,				jnp.array(lowerCamelCase       ,				dtype=jnp.intaa							)       ,				encoder_hidden_states=lowerCamelCase       ,				).sample

         assert sample.shape == latents.shape

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

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(lowerCamelCase       ,				lowerCamelCase       ,				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, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
          # fmt: on
      ]							)
  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_latents(lowerCamelCase       ,				shape=(4, 4, 96, 96)       ,				fpaa=lowerCamelCase							)
         _UpperCAmelCase							=    self.get_encoder_hidden_states(lowerCamelCase       ,				shape=(4, 77, 1024)       ,				fpaa=lowerCamelCase							)

         _UpperCAmelCase							=    model.apply(
             {"""params""": params}       ,				lowerCamelCase       ,				jnp.array(lowerCamelCase       ,				dtype=jnp.intaa							)       ,				encoder_hidden_states=lowerCamelCase       ,				).sample

         assert sample.shape == latents.shape

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

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




"""simple docstring"""






from copy import deepcopy



class     UpperCAmelCase    :



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	list[int] | None = None							,       __lowerCamelCase    :	int | None = None				):



     """simple docstring"""

     if arr is None and size is not None:
       _snake_case   	=      size
       _snake_case   	=      [0] * size
     elif arr is not None:
       self.init(__lowerCamelCase				)
     else:
       raise ValueError('''Either arr or size must be specified'''				)



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	list[int]				):



     """simple docstring"""
     _snake_case   	=      len(__lowerCamelCase				)
     _snake_case   	=      deepcopy(__lowerCamelCase				)
     for i in range(1							,       self.size				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         self.tree[j] += self.tree[i]



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.tree[:]
     for i in range(self.size - 1							,       0							,       -1				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         arr[j] -= arr[i]
     return arr



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index + (index & (-index))



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index - (index & (-index))



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     if index == 0:
       self.tree[0] += value
       return
     while index < self.size:
       self.tree[index] += value
       _snake_case   	=      self.next_(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     self.add(__lowerCamelCase							,       value - self.get(__lowerCamelCase				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     if right == 0:
       return 0
     _snake_case   	=      self.tree[0]
     right -= 1  # make right inclusive
     while right > 0:
       result += self.tree[right]
       _snake_case   	=      self.prev(__lowerCamelCase				)
     return result



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.prefix(__lowerCamelCase				) - self.prefix(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.query(__lowerCamelCase							,       index + 1				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     value -= self.tree[0]
     if value < 0:
       return -1

     _snake_case   	=      1  # Largest power of 2 <= size
     while j * 2 < self.size:
       j *= 2

     _snake_case   	=      0

     while j > 0:
       if i + j < self.size and self.tree[i + j] <= value:
         value -= self.tree[i + j]
         i += j
       j //= 2
     return i


if __name__ == "__main__":
     import doctest

     doctest.testmod()





