


'''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__       :



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            torch.manual_seed(0 )
            _lowerCAmelCase     :				List[str]       =							TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )

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

            torch.manual_seed(0 )
            _lowerCAmelCase     :				str       =							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     :				List[str]       =							DDPMScheduler(
                num_train_timesteps=1000   ,						beta_schedule='squaredcos_cap_v2'   ,						beta_start=0.0001   ,						beta_end=0.02   ,						thresholding=snake_case__   ,						dynamic_thresholding_ratio=0.95   ,						sample_max_value=1.0   ,						prediction_type='epsilon'   ,						variance_type='learned_range'   ,						)

            torch.manual_seed(0 )
            _lowerCAmelCase     :				Union[str, Any]       =							IFWatermarker()

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







     def a						(			self ):

            '''simple docstring'''

            torch.manual_seed(0 )
            _lowerCAmelCase     :				List[str]       =							TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )

            torch.manual_seed(0 )
            _lowerCAmelCase     :				Optional[int]       =							AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )

            torch.manual_seed(0 )
            _lowerCAmelCase     :				int       =							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=1.414   ,						time_embedding_act_fn='gelu'   ,						time_embedding_dim=32   ,						)
            unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            torch.manual_seed(0 )
            _lowerCAmelCase     :				Any       =							DDPMScheduler(
                num_train_timesteps=1000   ,						beta_schedule='squaredcos_cap_v2'   ,						beta_start=0.0001   ,						beta_end=0.02   ,						thresholding=snake_case__   ,						dynamic_thresholding_ratio=0.95   ,						sample_max_value=1.0   ,						prediction_type='epsilon'   ,						variance_type='learned_range'   ,						)

            torch.manual_seed(0 )
            _lowerCAmelCase     :				int       =							DDPMScheduler(
                num_train_timesteps=1000   ,						beta_schedule='squaredcos_cap_v2'   ,						beta_start=0.0001   ,						beta_end=0.02   ,						)

            torch.manual_seed(0 )
            _lowerCAmelCase     :				str       =							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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.get_dummy_components()
            _lowerCAmelCase     :				int       =							self.pipeline_class(**snake_case__ )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				str       =							self.get_dummy_inputs(snake_case__ )

            _lowerCAmelCase     :				Dict       =							inputs['prompt']
            _lowerCAmelCase     :				Dict       =							inputs['generator']
            _lowerCAmelCase     :				int       =							inputs['num_inference_steps']
            _lowerCAmelCase     :				Any       =							inputs['output_type']

            if "image" in inputs:
                   _lowerCAmelCase     :				Union[str, Any]       =							inputs['image']
            else:
                   _lowerCAmelCase     :				Optional[Any]       =							None

            if "mask_image" in inputs:
                   _lowerCAmelCase     :				int       =							inputs['mask_image']
            else:
                   _lowerCAmelCase     :				Union[str, Any]       =							None

            if "original_image" in inputs:
                   _lowerCAmelCase     :				Any       =							inputs['original_image']
            else:
                   _lowerCAmelCase     :				Tuple       =							None

            _lowerCAmelCase      ,  _lowerCAmelCase     :				List[Any]       =							pipe.encode_prompt(snake_case__ )

            # inputs with prompt converted to embeddings
            _lowerCAmelCase     :				List[str]       =							{
                '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     :				List[Any]       =							image

            if mask_image is not None:
                   _lowerCAmelCase     :				Union[str, Any]       =							mask_image

            if original_image is not None:
                   _lowerCAmelCase     :				Optional[Any]       =							original_image

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

            _lowerCAmelCase     :				Optional[int]       =							pipe(**snake_case__ )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(snake_case__ )
                   _lowerCAmelCase     :				Optional[int]       =							self.pipeline_class.from_pretrained(snake_case__ )
                   pipe_loaded.to(snake_case__ )
                   pipe_loaded.set_progress_bar_config(disable=snake_case__ )

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

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

            _lowerCAmelCase     :				Optional[int]       =							self.get_dummy_inputs(snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							inputs['generator']
            _lowerCAmelCase     :				List[str]       =							inputs['num_inference_steps']
            _lowerCAmelCase     :				Optional[int]       =							inputs['output_type']

            # inputs with prompt converted to embeddings
            _lowerCAmelCase     :				Optional[int]       =							{
                '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     :				Union[str, Any]       =							image

            if mask_image is not None:
                   _lowerCAmelCase     :				List[str]       =							mask_image

            if original_image is not None:
                   _lowerCAmelCase     :				Tuple       =							original_image

            _lowerCAmelCase     :				str       =							pipe_loaded(**snake_case__ )[0]

            _lowerCAmelCase     :				Dict       =							np.abs(to_np(snake_case__ ) - to_np(snake_case__ ) ).max()
            self.assertLess(snake_case__   ,						1E-4 )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.get_dummy_components()
            _lowerCAmelCase     :				List[str]       =							self.pipeline_class(**snake_case__ )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				str       =							self.get_dummy_inputs(snake_case__ )
            _lowerCAmelCase     :				Union[str, Any]       =							pipe(**snake_case__ )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                   pipe.save_pretrained(snake_case__ )
                   _lowerCAmelCase     :				Tuple       =							self.pipeline_class.from_pretrained(snake_case__ )
                   pipe_loaded.to(snake_case__ )
                   pipe_loaded.set_progress_bar_config(disable=snake_case__ )

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

            _lowerCAmelCase     :				List[Any]       =							self.get_dummy_inputs(snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							pipe_loaded(**snake_case__ )[0]

            _lowerCAmelCase     :				Dict       =							np.abs(to_np(snake_case__ ) - to_np(snake_case__ ) ).max()
            self.assertLess(snake_case__   ,						1E-4 )





'''simple docstring'''







from math import pi, sqrt, tan






def       lowercase					(_A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       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					(_A       ):







       """simple docstring"""







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






def       lowercase					(_A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       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					(_A ,    _A ,    _A       ):







       """simple docstring"""







       if radius_a < 0 or radius_a < 0 or height < 0:
              raise ValueError(
                  'surface_area_conical_frustum() only accepts non-negative values'       )
       _lowerCAmelCase     :				int       =							(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					(_A ,    _A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       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(_A ,    2       ) * torus_radius * tube_radius






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







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






def       lowercase					(_A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       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'       )
       _lowerCAmelCase     :				Tuple       =							(sidea + sidea + sidea) / 2
       _lowerCAmelCase     :				Dict       =							sqrt(
           semi_perimeter
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)
           * (semi_perimeter - sidea)       )
       return area






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       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					(_A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       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					(_A ,    _A       ):







       """simple docstring"""







       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					(_A ,    _A       ):







       """simple docstring"""







       if not isinstance(_A ,    _A       ) 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) = }''')

