


'''simple docstring'''
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


UpperCAmelCase_ :     Any								=      False

@skip_mps
class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		List[Any]    							=						StableDiffusionAttendAndExcitePipeline
 __lowercase				:		str    							=						False
 __lowercase				:		List[Any]    							=						TEXT_TO_IMAGE_PARAMS
 __lowercase				:		Union[str, Any]    							=						TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}				)
 __lowercase				:		List[str]    							=						TEXT_TO_IMAGE_IMAGE_PARAMS
 __lowercase				:		Optional[Any]    							=						TEXT_TO_IMAGE_IMAGE_PARAMS


 @classmethod
 def  snake_case__   (      cls						):

       """simple docstring"""







       super().setUpClass()
       torch.use_deterministic_algorithms(__lowercase						)


 @classmethod
 def  snake_case__   (      cls						):

       """simple docstring"""







       super().tearDownClass()
       torch.use_deterministic_algorithms(__lowercase						)


 def  snake_case__   (      self						):

       """simple docstring"""







       torch.manual_seed(0						)
       __A  :		Optional[Any]       =			UNetaDConditionModel(
           block_out_channels=(32, 64)      ,      layers_per_block=1      ,      sample_size=32      ,      in_channels=4      ,      out_channels=4      ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,      cross_attention_dim=32      ,      attention_head_dim=(2, 4)      ,      use_linear_projection=__lowercase      ,      )
       __A  :		List[str]       =			DDIMScheduler(
           beta_start=0.0_0_0_8_5      ,      beta_end=0.0_1_2      ,      beta_schedule='scaled_linear'      ,      clip_sample=__lowercase      ,      set_alpha_to_one=__lowercase      ,      )
       torch.manual_seed(0						)
       __A  :		Optional[int]       =			AutoencoderKL(
           block_out_channels=[32, 64]      ,      in_channels=3      ,      out_channels=3      ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,      latent_channels=4      ,      sample_size=128      ,      )
       torch.manual_seed(0						)
       __A  :		List[Any]       =			CLIPTextConfig(
           bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1E-05      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=1_000      ,      hidden_act='gelu'      ,      projection_dim=512      ,      )
       __A  :		List[str]       =			CLIPTextModel(__lowercase						)
       __A  :		Optional[Any]       =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       __A  :		Optional[int]       =			{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }

       return components


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=0						):

       """simple docstring"""







       if str(__lowercase						).startswith('mps'						):
             __A  :		Tuple       =			torch.manual_seed(__lowercase						)
       else:
             __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(__lowercase						)
       __A  :		Union[str, Any]       =			{
           'prompt': 'a cat and a frog',
           'token_indices': [2, 5],
           'generator': generator,
           'num_inference_steps': 1,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'max_iter_to_alter': 2,
           'thresholds': {0: 0.7},
       }
       return inputs


 def  snake_case__   (      self						):

       """simple docstring"""







       __A  :		Tuple       =			'cpu'

       __A  :		Optional[int]       =			self.get_dummy_components()
       __A  :		Optional[int]       =			self.pipeline_class(**__lowercase						)
       pipe.to(__lowercase						)
       pipe.set_progress_bar_config(disable=__lowercase						)

       __A  :		Optional[Any]       =			self.get_dummy_inputs(__lowercase						)
       __A  :		Tuple       =			pipe(**__lowercase						).images
       __A  :		Any       =			image[0, -3:, -3:, -1]

       self.assertEqual(image.shape      ,      (1, 64, 64, 3)						)
       __A  :		List[Any]       =			np.array(
           [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]						)
       __A  :		Optional[int]       =			np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(__lowercase      ,      1E-3						)


 def  snake_case__   (      self						):

       """simple docstring"""







       super().test_cpu_offload_forward_pass(expected_max_diff=5E-4						)


 def  snake_case__   (      self						):

       """simple docstring"""







       self._test_inference_batch_consistent(batch_sizes=[1, 2]						)


 def  snake_case__   (      self						):

       """simple docstring"""







       self._test_inference_batch_single_identical(batch_size=2      ,      expected_max_diff=7E-4						)


 def  snake_case__   (      self						):

       """simple docstring"""







       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)


 def  snake_case__   (      self						):

       """simple docstring"""







       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4						)


 def  snake_case__   (      self						):

       """simple docstring"""







       super().test_save_load_local(expected_max_difference=5E-4						)







 def  snake_case__   (      self						):

       """simple docstring"""







       super().test_save_load_optional_components(expected_max_difference=4E-4						)


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




 '''simple docstring'''


 @classmethod
 def  snake_case__   (      cls						):

       """simple docstring"""







       super().setUpClass()
       torch.use_deterministic_algorithms(__lowercase						)


 @classmethod
 def  snake_case__   (      cls						):

       """simple docstring"""







       super().tearDownClass()
       torch.use_deterministic_algorithms(__lowercase						)


 def  snake_case__   (      self						):

       """simple docstring"""







       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







 def  snake_case__   (      self						):

       """simple docstring"""







       __A  :		int       =			torch.manual_seed(51						)

       __A  :		Any       =			StableDiffusionAttendAndExcitePipeline.from_pretrained(
           'CompVis/stable-diffusion-v1-4'      ,      safety_checker=__lowercase      ,      torch_dtype=torch.floataa						)
       pipe.to('cuda'						)

       __A  :		Dict       =			'a painting of an elephant with glasses'
       __A  :		int       =			[5, 7]

       __A  :		Dict       =			pipe(
           prompt=__lowercase      ,      token_indices=__lowercase      ,      guidance_scale=7.5      ,      generator=__lowercase      ,      num_inference_steps=5      ,      max_iter_to_alter=5      ,      output_type='numpy'      ,      ).images[0]

       __A  :		Any       =			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'						)
       assert np.abs((expected_image - image).max()						) < 5E-1










'''simple docstring'''
from math import pi, sqrt, tan


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if side_length < 0:
  raise ValueError('surface_area_cube() only accepts non-negative values'			)
 return 6 * side_length**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if length < 0 or breadth < 0 or height < 0:
  raise ValueError('surface_area_cuboid() only accepts non-negative values'			)
 return 2 * ((length * breadth) + (breadth * height) + (length * height))


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if radius < 0:
  raise ValueError('surface_area_sphere() only accepts non-negative values'			)
 return 4 * pi * radius**2


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if radius < 0:
  raise ValueError('surface_area_hemisphere() only accepts non-negative values'			)
 return 3 * pi * radius**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius < 0 or height < 0:
  raise ValueError('surface_area_cone() only accepts non-negative values'			)
 return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError(
      'surface_area_conical_frustum() only accepts non-negative values'			)
 __A  :		Optional[Any]       =			(height**2 + (radius_a - radius_a) ** 2) ** 0.5
 return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius < 0 or height < 0:
  raise ValueError('surface_area_cylinder() only accepts non-negative values'			)
 return 2 * pi * radius * (height + radius)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError('surface_area_torus() only accepts non-negative values'			)
 if torus_radius < tube_radius:
  raise ValueError(
      'surface_area_torus() does not support spindle or self intersecting tori'			)
 return 4 * pow(UpperCamelCase__,				2			) * torus_radius * tube_radius


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if length < 0 or width < 0:
  raise ValueError('area_rectangle() only accepts non-negative values'			)
 return length * width


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if side_length < 0:
  raise ValueError('area_square() only accepts non-negative values'			)
 return side_length**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if base < 0 or height < 0:
  raise ValueError('area_triangle() only accepts non-negative values'			)
 return (base * height) / 2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if sidea < 0 or sidea < 0 or sidea < 0:
  raise ValueError('area_triangle_three_sides() only accepts non-negative values'			)
 elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
  raise ValueError('Given three sides do not form a triangle'			)
 __A  :		int       =			(sidea + sidea + sidea) / 2
 __A  :		List[Any]       =			sqrt(
     semi_perimeter
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)
     * (semi_perimeter - sidea)			)
 return area


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if base < 0 or height < 0:
  raise ValueError('area_parallelogram() only accepts non-negative values'			)
 return base * height


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if basea < 0 or basea < 0 or height < 0:
  raise ValueError('area_trapezium() only accepts non-negative values'			)
 return 1 / 2 * (basea + basea) * height


def 			_lowercase       (				UpperCamelCase__       :					float			):
 if radius < 0:
  raise ValueError('area_circle() only accepts non-negative values'			)
 return pi * radius**2


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if radius_x < 0 or radius_y < 0:
  raise ValueError('area_ellipse() only accepts non-negative values'			)
 return pi * radius_x * radius_y


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					float			):
 if diagonal_a < 0 or diagonal_a < 0:
  raise ValueError('area_rhombus() only accepts non-negative values'			)
 return 1 / 2 * diagonal_a * diagonal_a


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					float			):
 if not isinstance(UpperCamelCase__,				UpperCamelCase__			) or sides < 3:
  raise ValueError(
      'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'			)
 elif length < 0:
  raise ValueError(
      'area_reg_polygon() only accepts non-negative values as \
length of a side'			)
 return (sides * length**2) / (4 * tan(pi / sides			))
 return (sides * length**2) / (4 * tan(pi / sides			))


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

 print('[DEMO] Areas of various geometric shapes: \n')
 print(f'''Rectangle: {area_rectangle(1_0, 2_0) = }''')
 print(f'''Square: {area_square(1_0) = }''')
 print(f'''Triangle: {area_triangle(1_0, 1_0) = }''')
 print(f'''Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }''')
 print(f'''Parallelogram: {area_parallelogram(1_0, 2_0) = }''')
 print(f'''Rhombus: {area_rhombus(1_0, 2_0) = }''')
 print(f'''Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }''')
 print(f'''Circle: {area_circle(2_0) = }''')
 print(f'''Ellipse: {area_ellipse(1_0, 2_0) = }''')
 print('\nSurface Areas of various geometric shapes: \n')
 print(f'''Cube: {surface_area_cube(2_0) = }''')
 print(f'''Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }''')
 print(f'''Sphere: {surface_area_sphere(2_0) = }''')
 print(f'''Hemisphere: {surface_area_hemisphere(2_0) = }''')
 print(f'''Cone: {surface_area_cone(1_0, 2_0) = }''')
 print(f'''Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }''')
 print(f'''Cylinder: {surface_area_cylinder(1_0, 2_0) = }''')
 print(f'''Torus: {surface_area_torus(2_0, 1_0) = }''')
 print(f'''Equilateral Triangle: {area_reg_polygon(3, 1_0) = }''')
 print(f'''Square: {area_reg_polygon(4, 1_0) = }''')
 print(f'''Reqular Pentagon: {area_reg_polygon(5, 1_0) = }''')






