




'''simple docstring'''




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class   __UpperCamelCase       :






       def      lowercase__     (							self  ):



              """simple docstring"""







              torch.manual_seed(0  )
              lowerCamelCase_							=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=UNetaDConditionModel(
                  sample_size=32,		layers_per_block=1,		block_out_channels=[32, 64],		down_block_types=[
                      '''ResnetDownsampleBlock2D''',
                      '''SimpleCrossAttnDownBlock2D''',
                  ],		mid_block_type='''UNetMidBlock2DSimpleCrossAttn''',		up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''],		in_channels=3,		out_channels=6,		cross_attention_dim=32,		encoder_hid_dim=32,		attention_head_dim=8,		addition_embed_type='''text''',		addition_embed_type_num_heads=2,		cross_attention_norm='''group_norm''',		resnet_time_scale_shift='''scale_shift''',		act_fn='''gelu''',		)
              unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              torch.manual_seed(0  )
              lowerCamelCase_							=DDPMScheduler(
                  num_train_timesteps=1_000,		beta_schedule='''squaredcos_cap_v2''',		beta_start=0.0_0_0_1,		beta_end=0.0_2,		thresholding=lowerCAmelCase,		dynamic_thresholding_ratio=0.9_5,		sample_max_value=1.0,		prediction_type='''epsilon''',		variance_type='''learned_range''',		)

              torch.manual_seed(0  )
              lowerCamelCase_							=IFWatermarker()

              return {
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "unet": unet,
                  "scheduler": scheduler,
                  "watermarker": watermarker,
                  "safety_checker": None,
                  "feature_extractor": None,
              }






       def      lowercase__     (							self  ):



              """simple docstring"""







              torch.manual_seed(0  )
              lowerCamelCase_							=TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )

              torch.manual_seed(0  )
              lowerCamelCase_							=UNetaDConditionModel(
                  sample_size=32,		layers_per_block=[1, 2],		block_out_channels=[32, 64],		down_block_types=[
                      '''ResnetDownsampleBlock2D''',
                      '''SimpleCrossAttnDownBlock2D''',
                  ],		mid_block_type='''UNetMidBlock2DSimpleCrossAttn''',		up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''],		in_channels=6,		out_channels=6,		cross_attention_dim=32,		encoder_hid_dim=32,		attention_head_dim=8,		addition_embed_type='''text''',		addition_embed_type_num_heads=2,		cross_attention_norm='''group_norm''',		resnet_time_scale_shift='''scale_shift''',		act_fn='''gelu''',		class_embed_type='''timestep''',		mid_block_scale_factor=1.4_1_4,		time_embedding_act_fn='''gelu''',		time_embedding_dim=32,		)
              unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              torch.manual_seed(0  )
              lowerCamelCase_							=DDPMScheduler(
                  num_train_timesteps=1_000,		beta_schedule='''squaredcos_cap_v2''',		beta_start=0.0_0_0_1,		beta_end=0.0_2,		thresholding=lowerCAmelCase,		dynamic_thresholding_ratio=0.9_5,		sample_max_value=1.0,		prediction_type='''epsilon''',		variance_type='''learned_range''',		)

              torch.manual_seed(0  )
              lowerCamelCase_							=DDPMScheduler(
                  num_train_timesteps=1_000,		beta_schedule='''squaredcos_cap_v2''',		beta_start=0.0_0_0_1,		beta_end=0.0_2,		)

              torch.manual_seed(0  )
              lowerCamelCase_							=IFWatermarker()

              return {
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "unet": unet,
                  "scheduler": scheduler,
                  "image_noising_scheduler": image_noising_scheduler,
                  "watermarker": watermarker,
                  "safety_checker": None,
                  "feature_extractor": None,
              }






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_dummy_components()
              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase  )
              pipe.to(lowerCAmelCase  )
              pipe.set_progress_bar_config(disable=lowerCAmelCase  )

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )

              lowerCamelCase_							=inputs['''prompt''']
              lowerCamelCase_							=inputs['''generator''']
              lowerCamelCase_							=inputs['''num_inference_steps''']
              lowerCamelCase_							=inputs['''output_type''']

              if "image" in inputs:
                     lowerCamelCase_							=inputs['''image''']
              else:
                     lowerCamelCase_							=None

              if "mask_image" in inputs:
                     lowerCamelCase_							=inputs['''mask_image''']
              else:
                     lowerCamelCase_							=None

              if "original_image" in inputs:
                     lowerCamelCase_							=inputs['''original_image''']
              else:
                     lowerCamelCase_							=None

              lowerCamelCase_,      lowerCamelCase_							=pipe.encode_prompt(lowerCAmelCase  )

              # inputs with prompt converted to embeddings
              lowerCamelCase_							={
                  '''prompt_embeds''': prompt_embeds,
                  '''negative_prompt_embeds''': negative_prompt_embeds,
                  '''generator''': generator,
                  '''num_inference_steps''': num_inference_steps,
                  '''output_type''': output_type,
              }

              if image is not None:
                     lowerCamelCase_							=image

              if mask_image is not None:
                     lowerCamelCase_							=mask_image

              if original_image is not None:
                     lowerCamelCase_							=original_image

              # set all optional components to None
              for optional_component in pipe._optional_components:
                     setattr(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )

              lowerCamelCase_							=pipe(**lowerCAmelCase  )[0]

              with tempfile.TemporaryDirectory() as tmpdir:
                     pipe.save_pretrained(lowerCAmelCase  )
                     lowerCamelCase_							=self.pipeline_class.from_pretrained(lowerCAmelCase  )
                     pipe_loaded.to(lowerCAmelCase  )
                     pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase  )

              pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              for optional_component in pipe._optional_components:
                     self.assertTrue(
                         getattr(lowerCAmelCase,		lowerCAmelCase  ) is None,		f'''`{optional_component}` did not stay set to None after loading.''',		)

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )

              lowerCamelCase_							=inputs['''generator''']
              lowerCamelCase_							=inputs['''num_inference_steps''']
              lowerCamelCase_							=inputs['''output_type''']

              # inputs with prompt converted to embeddings
              lowerCamelCase_							={
                  '''prompt_embeds''': prompt_embeds,
                  '''negative_prompt_embeds''': negative_prompt_embeds,
                  '''generator''': generator,
                  '''num_inference_steps''': num_inference_steps,
                  '''output_type''': output_type,
              }

              if image is not None:
                     lowerCamelCase_							=image

              if mask_image is not None:
                     lowerCamelCase_							=mask_image

              if original_image is not None:
                     lowerCamelCase_							=original_image

              lowerCamelCase_							=pipe_loaded(**lowerCAmelCase  )[0]

              lowerCamelCase_							=np.abs(to_np(lowerCAmelCase  ) - to_np(lowerCAmelCase  )  ).max()
              self.assertLess(lowerCAmelCase,		1e-4  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_dummy_components()
              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase  )
              pipe.to(lowerCAmelCase  )
              pipe.set_progress_bar_config(disable=lowerCAmelCase  )

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )
              lowerCamelCase_							=pipe(**lowerCAmelCase  )[0]

              with tempfile.TemporaryDirectory() as tmpdir:
                     pipe.save_pretrained(lowerCAmelCase  )
                     lowerCamelCase_							=self.pipeline_class.from_pretrained(lowerCAmelCase  )
                     pipe_loaded.to(lowerCAmelCase  )
                     pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase  )

              pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

              lowerCamelCase_							=self.get_dummy_inputs(lowerCAmelCase  )
              lowerCamelCase_							=pipe_loaded(**lowerCAmelCase  )[0]

              lowerCamelCase_							=np.abs(to_np(lowerCAmelCase  ) - to_np(lowerCAmelCase  )  ).max()
              self.assertLess(lowerCAmelCase,		1e-4  )





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







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







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







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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_a < 0 or radius_a < 0 or height < 0:
    raise ValueError(
        'surface_area_conical_frustum() only accepts non-negative values'   )
  a__:     List[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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cylinder() only accepts non-negative values'   )
  return 2 * pi * radius * (height + radius)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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(_SCREAMING_SNAKE_CASE		,     2   ) * torus_radius * tube_radius







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







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







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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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__:     Tuple										=	sqrt(
      semi_perimeter
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)   )
  return area







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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('area_circle() only accepts non-negative values'   )
  return pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) 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(10, 20) = }")
 print(f"Square: {area_square(10) = }")
 print(f"Triangle: {area_triangle(10, 10) = }")
 print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
 print(f"Parallelogram: {area_parallelogram(10, 20) = }")
 print(f"Rhombus: {area_rhombus(10, 20) = }")
 print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
 print(f"Circle: {area_circle(20) = }")
 print(f"Ellipse: {area_ellipse(10, 20) = }")
 print('\nSurface Areas of various geometric shapes: \n')
 print(f"Cube: {surface_area_cube(20) = }")
 print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
 print(f"Sphere: {surface_area_sphere(20) = }")
 print(f"Hemisphere: {surface_area_hemisphere(20) = }")
 print(f"Cone: {surface_area_cone(10, 20) = }")
 print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
 print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
 print(f"Torus: {surface_area_torus(20, 10) = }")
 print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
 print(f"Square: {area_reg_polygon(4, 10) = }")
 print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")


