
"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			@property
			def  __lowerCAmelCase (					self       )			->     int:
						torch.manual_seed(0       )
						lowerCAmelCase_    :Optional[int]			     =  UNetaDModel(
						    block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,   up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,   )
						return model






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :List[Any]			     =  self.dummy_uncond_unet
						lowerCAmelCase_    :str			     =  PNDMScheduler()

						lowerCAmelCase_    :Optional[Any]			     =  PNDMPipeline(unet=SCREAMING_SNAKE_CASE__       ,   scheduler=SCREAMING_SNAKE_CASE__       )
						pndm.to(SCREAMING_SNAKE_CASE__       )
						pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__       )

						lowerCAmelCase_    :Optional[Any]			     =  torch.manual_seed(0       )
						lowerCAmelCase_    :Union[str, Any]			     =  pndm(generator=SCREAMING_SNAKE_CASE__       ,   num_inference_steps=20       ,   output_type="""numpy"""       ).images

						lowerCAmelCase_    :Dict			     =  torch.manual_seed(0       )
						lowerCAmelCase_    :Dict			     =  pndm(generator=SCREAMING_SNAKE_CASE__       ,   num_inference_steps=20       ,   output_type="""numpy"""       ,   return_dict=SCREAMING_SNAKE_CASE__       )[0]

						lowerCAmelCase_    :Tuple			     =  image[0, -3:, -3:, -1]
						lowerCAmelCase_    :Tuple			     =  image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						lowerCAmelCase_    :str			     =  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2




@slow
@require_torch
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def  __lowerCAmelCase (					self       )			->     Any:
						lowerCAmelCase_    :Optional[Any]			     =  """google/ddpm-cifar10-32"""

						lowerCAmelCase_    :Union[str, Any]			     =  UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__       )
						lowerCAmelCase_    :str			     =  PNDMScheduler()

						lowerCAmelCase_    :List[str]			     =  PNDMPipeline(unet=SCREAMING_SNAKE_CASE__       ,   scheduler=SCREAMING_SNAKE_CASE__       )
						pndm.to(SCREAMING_SNAKE_CASE__       )
						pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__       )
						lowerCAmelCase_    :Optional[int]			     =  torch.manual_seed(0       )
						lowerCAmelCase_    :int			     =  pndm(generator=SCREAMING_SNAKE_CASE__       ,   output_type="""numpy"""       ).images

						lowerCAmelCase_    :Any			     =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						lowerCAmelCase_    :Union[str, Any]			     =  np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2







'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       list[int]						,		snake_case_       :       list[int]      )					->     tuple[float, float]:






 '''simple docstring'''

 if not len(snake_case_      ) == len(snake_case_      ) == 3:
  raise ValueError("""Please enter a valid equation."""      )
 if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
  raise ValueError("""Both a & b of two equations can't be zero."""      )

 # Extract the coefficients
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						equationa
 __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						equationa

 # Calculate the determinants of the matrices
 __lowerCAmelCase											=						aa * ba - aa * ba
 __lowerCAmelCase											=						ca * ba - ca * ba
 __lowerCAmelCase											=						aa * ca - aa * ca

 # Check if the system of linear equations has a solution (using Cramer's rule)
 if determinant == 0:
  if determinant_x == determinant_y == 0:
   raise ValueError("""Infinite solutions. (Consistent system)"""      )
  else:
   raise ValueError("""No solution. (Inconsistent system)"""      )
 else:
  if determinant_x == determinant_y == 0:
   # Trivial solution (Inconsistent system)
   return (0.0, 0.0)
  else:
   __lowerCAmelCase											=						determinant_x / determinant
   __lowerCAmelCase											=						determinant_y / determinant
   # Non-Trivial Solution (Consistent system)
   return (x, y)






