





"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			StableDiffusionSAGPipeline
  a_      =			TEXT_TO_IMAGE_PARAMS
  a_      =			TEXT_TO_IMAGE_BATCH_PARAMS
  a_      =			TEXT_TO_IMAGE_IMAGE_PARAMS
  a_      =			TEXT_TO_IMAGE_IMAGE_PARAMS
  a_      =			False






  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDConditionModel(
         block_out_channels=(3_2, 6_4) ,  layers_per_block=2 ,  sample_size=3_2 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,  cross_attention_dim=3_2 ,  )
     __lowerCAmelCase							     =     DDIMScheduler(
         beta_start=0.0_00_85 ,  beta_end=0.0_12 ,  beta_schedule='scaled_linear' ,  clip_sample=lowerCAmelCase_ ,  set_alpha_to_one=lowerCAmelCase_ ,  )
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     AutoencoderKL(
         block_out_channels=[3_2, 6_4] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,  latent_channels=4 ,  )
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     CLIPTextConfig(
         bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=3_2 ,  intermediate_size=3_7 ,  layer_norm_eps=1e-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1_0_0_0 ,  )
     __lowerCAmelCase							     =     CLIPTextModel(lowerCAmelCase_					)
     __lowerCAmelCase							     =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

     __lowerCAmelCase							     =     {
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		int=0					)							->						Dict:
     if str(lowerCAmelCase_					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'prompt': '.',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 1.0,
         'sag_scale': 1.0,
         'output_type': 'numpy',
     }
     return inputs






  def     lowercase		(    self			:		int					)							->						List[Any]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





@slow
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Optional[int]					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		Optional[int]					)							->						List[Any]:
     __lowerCAmelCase							     =     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'					)
     __lowerCAmelCase							     =     sag_pipe.to(lowerCAmelCase_					)
     sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     '.'
     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     sag_pipe(
         [prompt] ,  generator=lowerCAmelCase_ ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=2_0 ,  output_type='np'					)

     __lowerCAmelCase							     =     output.images

     __lowerCAmelCase							     =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2






  def     lowercase		(    self			:		Tuple					)							->						Optional[Any]:
     __lowerCAmelCase							     =     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'					)
     __lowerCAmelCase							     =     sag_pipe.to(lowerCAmelCase_					)
     sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     '.'
     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     sag_pipe(
         [prompt] ,  generator=lowerCAmelCase_ ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=2_0 ,  output_type='np'					)

     __lowerCAmelCase							     =     output.images

     __lowerCAmelCase							     =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase							     =     np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() < 5e-2






  def     lowercase		(    self			:		Union[str, Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'					)
     __lowerCAmelCase							     =     sag_pipe.to(lowerCAmelCase_					)
     sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     '.'
     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     sag_pipe(
         [prompt] ,  width=7_6_8 ,  height=5_1_2 ,  generator=lowerCAmelCase_ ,  guidance_scale=7.5 ,  sag_scale=1.0 ,  num_inference_steps=2_0 ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images

     assert image.shape == (1, 5_1_2, 7_6_8, 3)








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

