





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 UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					str							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Optional[int]=0      ,				UpperCamelCase      :					Optional[int]=(4, 4, 64, 64)      ,				UpperCamelCase      :					List[str]=False							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	jnp.bfloataa if fpaa else jnp.floataa
    _snake_case   :				Tuple														=	jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase      ,				UpperCamelCase							)							)      ,				dtype=UpperCamelCase							)
    return image







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


    '''simple docstring'''





    _snake_case   :				Tuple														=	jnp.bfloataa if fpaa else jnp.floataa
    _snake_case   :				Optional[Any]														=	'bf16' if fpaa else None

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







  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					int=0      ,				UpperCamelCase      :					List[str]=(4, 77, 7_68)      ,				UpperCamelCase      :					Union[str, Any]=False							):


    '''simple docstring'''





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







  @parameterized.expand(
      [
          # fmt: off
          [83, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
          [17, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
          [8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
          [3, 10_00, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
          # fmt: on
      ]							)
  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,				fpaa=UpperCamelCase							)
    _snake_case   :				List[str]														=	self.get_latents(UpperCamelCase      ,				fpaa=UpperCamelCase							)
    _snake_case   :				Optional[int]														=	self.get_encoder_hidden_states(UpperCamelCase      ,				fpaa=UpperCamelCase							)

    _snake_case   :				Optional[int]														=	model.apply(
        {'params': params}      ,				UpperCamelCase      ,				jnp.array(UpperCamelCase      ,				dtype=jnp.intaa							)      ,				encoder_hidden_states=UpperCamelCase      ,				).sample

    assert sample.shape == latents.shape

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

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







  @parameterized.expand(
      [
          # fmt: off
          [83, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
          [17, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
          [8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
          [3, 10_00, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
          # fmt: on
      ]							)
  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					Dict      ,				UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





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

    _snake_case   :				List[str]														=	model.apply(
        {'params': params}      ,				UpperCamelCase      ,				jnp.array(UpperCamelCase      ,				dtype=jnp.intaa							)      ,				encoder_hidden_states=UpperCamelCase      ,				).sample

    assert sample.shape == latents.shape

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

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











import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       Optional[Union[str, Path]]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=True
  a_							:       Optional[int]      					=None
  a_							:       int      					=1
  a_							:       Optional[Union[str, bool]]      					=None
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    return self.__class__(**{k: copy.deepcopy(UpperCamelCase							) for k, v in self.__dict__.items()}							)




