



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()







class 	_snake_case							(			unittest.TestCase      ):






   '''simple docstring'''






   A__	:      Union[str, Any]    =		StableDiffusionLDMaDPipeline
   A__	:      Union[str, Any]    =		TEXT_TO_IMAGE_PARAMS
   A__	:      List[str]    =		TEXT_TO_IMAGE_BATCH_PARAMS
   A__	:      Union[str, Any]    =		TEXT_TO_IMAGE_IMAGE_PARAMS


   def 	A__  (  self:			Any				)     ->	Tuple:
       torch.manual_seed(0				)
       UpperCAmelCase_			:    Optional[int]   						= UNetaDConditionModel(
           block_out_channels=(32, 64)   ,layers_per_block=2   ,sample_size=32   ,in_channels=4   ,out_channels=4   ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,cross_attention_dim=32   ,)
       UpperCAmelCase_			:    int   						= DDIMScheduler(
           beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,beta_schedule="""scaled_linear"""   ,clip_sample=lowerCAmelCase__   ,set_alpha_to_one=lowerCAmelCase__   ,)
       torch.manual_seed(0				)
       UpperCAmelCase_			:    Any   						= AutoencoderKL(
           block_out_channels=[32, 64]   ,in_channels=6   ,out_channels=6   ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,latent_channels=4   ,)
       torch.manual_seed(0				)
       UpperCAmelCase_			:    List[str]   						= 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=1000   ,)
       UpperCAmelCase_			:    Dict   						= CLIPTextModel(lowerCAmelCase__				)
       UpperCAmelCase_			:    Optional[int]   						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

       UpperCAmelCase_			:    Optional[int]   						= {
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """safety_checker""": None,
           """feature_extractor""": None,
       }
       return components


   def 	A__  (  self:			Any   ,lowerCamelCase_:			Any   ,lowerCamelCase_:			Optional[int]=0				)     ->	int:
       if str(lowerCAmelCase__				).startswith("""mps"""				):
           UpperCAmelCase_			:    Any   						= torch.manual_seed(lowerCAmelCase__				)
       else:
           UpperCAmelCase_			:    int   						= torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
       UpperCAmelCase_			:    Optional[Any]   						= {
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """guidance_scale""": 6.0,
           """output_type""": """numpy""",
       }
       return inputs


   def 	A__  (  self:			List[str]				)     ->	int:
       UpperCAmelCase_			:    Tuple   						= """cpu"""  # ensure determinism for the device-dependent torch.Generator

       UpperCAmelCase_			:    List[Any]   						= self.get_dummy_components()
       UpperCAmelCase_			:    Union[str, Any]   						= StableDiffusionLDMaDPipeline(**lowerCAmelCase__				)
       UpperCAmelCase_			:    Dict   						= ldmad_pipe.to(lowerCAmelCase__				)
       ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

       UpperCAmelCase_			:    str   						= self.get_dummy_inputs(lowerCAmelCase__				)
       UpperCAmelCase_			:    Optional[Any]   						= ldmad_pipe(**lowerCAmelCase__				)
       UpperCAmelCase_						,      UpperCAmelCase_			:    List[Any]   						= output.rgb, output.depth

       UpperCAmelCase_			:    int   						= rgb[0, -3:, -3:, -1]
       UpperCAmelCase_			:    Dict   						= depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       UpperCAmelCase_			:    Union[str, Any]   						= np.array(
           [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]				)
       UpperCAmelCase_			:    List[str]   						= np.array([103.46727, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]				)

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb				).max() < 1e-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth				).max() < 1e-2


   def 	A__  (  self:			List[Any]				)     ->	Tuple:
       UpperCAmelCase_			:    Dict   						= self.get_dummy_components()
       UpperCAmelCase_			:    str   						= StableDiffusionLDMaDPipeline(**lowerCAmelCase__				)
       UpperCAmelCase_			:    str   						= ldmad_pipe.to(lowerCAmelCase__				)
       ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

       UpperCAmelCase_			:    int   						= self.get_dummy_inputs(lowerCAmelCase__				)
       UpperCAmelCase_			:    Dict   						= 3 * [inputs["""prompt"""]]

       # forward
       UpperCAmelCase_			:    List[str]   						= ldmad_pipe(**lowerCAmelCase__				)
       UpperCAmelCase_						,      UpperCAmelCase_			:    int   						= output.rgb, output.depth
       UpperCAmelCase_			:    List[Any]   						= rgb_slice_a[0, -3:, -3:, -1]
       UpperCAmelCase_			:    str   						= depth_slice_a[0, -3:, -1]

       UpperCAmelCase_			:    Dict   						= self.get_dummy_inputs(lowerCAmelCase__				)
       UpperCAmelCase_			:    str   						= 3 * [inputs.pop("""prompt"""				)]

       UpperCAmelCase_			:    List[str]   						= ldmad_pipe.tokenizer(
           lowerCAmelCase__   ,padding="""max_length"""   ,max_length=ldmad_pipe.tokenizer.model_max_length   ,truncation=lowerCAmelCase__   ,return_tensors="""pt"""   ,)
       UpperCAmelCase_			:    int   						= text_inputs["""input_ids"""].to(lowerCAmelCase__				)

       UpperCAmelCase_			:    List[Any]   						= ldmad_pipe.text_encoder(lowerCAmelCase__				)[0]

       UpperCAmelCase_			:    Tuple   						= prompt_embeds

       # forward
       UpperCAmelCase_			:    Union[str, Any]   						= ldmad_pipe(**lowerCAmelCase__				)
       UpperCAmelCase_						,      UpperCAmelCase_			:    Any   						= output.rgb, output.depth
       UpperCAmelCase_			:    Optional[int]   						= rgb_slice_a[0, -3:, -3:, -1]
       UpperCAmelCase_			:    Optional[Any]   						= depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()				).max() < 1e-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()				).max() < 1e-4






   def 	A__  (  self:			Tuple				)     ->	Dict:
       UpperCAmelCase_			:    Any   						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_			:    int   						= self.get_dummy_components()
       UpperCAmelCase_			:    List[str]   						= PNDMScheduler(skip_prk_steps=lowerCAmelCase__				)
       UpperCAmelCase_			:    Union[str, Any]   						= StableDiffusionLDMaDPipeline(**lowerCAmelCase__				)
       UpperCAmelCase_			:    Optional[int]   						= ldmad_pipe.to(lowerCAmelCase__				)
       ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

       UpperCAmelCase_			:    Union[str, Any]   						= self.get_dummy_inputs(lowerCAmelCase__				)
       UpperCAmelCase_			:    Any   						= """french fries"""
       UpperCAmelCase_			:    str   						= ldmad_pipe(**lowerCAmelCase__   ,negative_prompt=lowerCAmelCase__				)

       UpperCAmelCase_						,      UpperCAmelCase_			:    List[str]   						= output.rgb, output.depth
       UpperCAmelCase_			:    Optional[Any]   						= rgb[0, -3:, -3:, -1]
       UpperCAmelCase_			:    Any   						= depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       UpperCAmelCase_			:    Optional[int]   						= np.array(
           [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]				)
       UpperCAmelCase_			:    Dict   						= np.array([107.84738, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]				)
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb				).max() < 1e-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth				).max() < 1e-2







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






   '''simple docstring'''






   def 	A__  (  self:			List[Any]				)     ->	Optional[Any]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def 	A__  (  self:			Union[str, Any]   ,lowerCamelCase_:			Union[str, Any]   ,lowerCamelCase_:			Optional[int]="cpu"   ,lowerCamelCase_:			str=torch.floataa   ,lowerCamelCase_:			Optional[int]=0				)     ->	Tuple:
       UpperCAmelCase_			:    Optional[Any]   						= torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
       UpperCAmelCase_			:    Dict   						= np.random.RandomState(lowerCAmelCase__				).standard_normal((1, 4, 64, 64)				)
       UpperCAmelCase_			:    str   						= torch.from_numpy(lowerCAmelCase__				).to(device=lowerCAmelCase__   ,dtype=lowerCAmelCase__				)
       UpperCAmelCase_			:    int   						= {
           """prompt""": """a photograph of an astronaut riding a horse""",
           """latents""": latents,
           """generator""": generator,
           """num_inference_steps""": 3,
           """guidance_scale""": 7.5,
           """output_type""": """numpy""",
       }
       return inputs






   def 	A__  (  self:			int				)     ->	Tuple:
       UpperCAmelCase_			:    List[Any]   						= StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""				)
       UpperCAmelCase_			:    List[str]   						= ldmad_pipe.to(lowerCAmelCase__				)
       ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

       UpperCAmelCase_			:    Optional[Any]   						= self.get_inputs(lowerCAmelCase__				)
       UpperCAmelCase_			:    Any   						= ldmad_pipe(**lowerCAmelCase__				)
       UpperCAmelCase_						,      UpperCAmelCase_			:    List[Any]   						= output.rgb, output.depth
       UpperCAmelCase_			:    Dict   						= rgb[0, -3:, -3:, -1].flatten()
       UpperCAmelCase_			:    Any   						= rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       UpperCAmelCase_			:    int   						= np.array(
           [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]				)
       UpperCAmelCase_			:    Optional[Any]   						= np.array(
           [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]				)
       assert np.abs(rgb_slice - expected_slice_rgb				).max() < 3e-3
       assert np.abs(depth_slice - expected_slice_depth				).max() < 3e-3







@nightly
@require_torch_gpu
class 	_snake_case							(			unittest.TestCase      ):






   '''simple docstring'''






   def 	A__  (  self:			Any				)     ->	Union[str, Any]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def 	A__  (  self:			Optional[Any]   ,lowerCamelCase_:			Optional[Any]   ,lowerCamelCase_:			List[str]="cpu"   ,lowerCamelCase_:			List[Any]=torch.floataa   ,lowerCamelCase_:			List[Any]=0				)     ->	int:
       UpperCAmelCase_			:    Optional[int]   						= torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
       UpperCAmelCase_			:    int   						= np.random.RandomState(lowerCAmelCase__				).standard_normal((1, 4, 64, 64)				)
       UpperCAmelCase_			:    str   						= torch.from_numpy(lowerCAmelCase__				).to(device=lowerCAmelCase__   ,dtype=lowerCAmelCase__				)
       UpperCAmelCase_			:    Dict   						= {
           """prompt""": """a photograph of an astronaut riding a horse""",
           """latents""": latents,
           """generator""": generator,
           """num_inference_steps""": 50,
           """guidance_scale""": 7.5,
           """output_type""": """numpy""",
       }
       return inputs


   def 	A__  (  self:			List[str]				)     ->	Union[str, Any]:
       UpperCAmelCase_			:    Dict   						= StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""				).to(lowerCAmelCase__				)
       ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

       UpperCAmelCase_			:    List[Any]   						= self.get_inputs(lowerCAmelCase__				)
       UpperCAmelCase_			:    Dict   						= ldmad_pipe(**lowerCAmelCase__				)
       UpperCAmelCase_						,      UpperCAmelCase_			:    List[str]   						= output.rgb, output.depth

       UpperCAmelCase_			:    List[Any]   						= 0.4_9_5_5_8_6
       UpperCAmelCase_			:    List[Any]   						= 0.3_3_7_9_5_5_1_5
       UpperCAmelCase_			:    Union[str, Any]   						= 112.48518
       UpperCAmelCase_			:    Tuple   						= 9_8.4_8_9_7_4_6
       assert np.abs(expected_rgb_mean - rgb.mean()				) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()				) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()				) < 1e-3
       assert np.abs(expected_depth_std - depth.std()				) < 1e-3






   def 	A__  (  self:			int				)     ->	str:
       UpperCAmelCase_			:    List[Any]   						= StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c"""				).to(lowerCAmelCase__				)
       ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

       UpperCAmelCase_			:    Optional[int]   						= self.get_inputs(lowerCAmelCase__				)
       UpperCAmelCase_			:    Tuple   						= ldmad_pipe(**lowerCAmelCase__				)
       UpperCAmelCase_						,      UpperCAmelCase_			:    List[Any]   						= output.rgb, output.depth

       UpperCAmelCase_			:    Any   						= 0.4_1_9_4_1_2_7
       UpperCAmelCase_			:    Union[str, Any]   						= 0.3_5_3_7_5_5_8_6
       UpperCAmelCase_			:    Tuple   						= 0.5_6_3_8_5_0_2
       UpperCAmelCase_			:    str   						= 0.3_4_6_8_6_1_0_3

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()				) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()				) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()				) < 1e-3
       assert np.abs(expected_depth_std - depth.std()				) < 1e-3




"""simple docstring"""




from __future__ import annotations

from collections.abc import Callable


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Callable[[int | float], int | float] ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int | float ,			SCREAMING_SNAKE_CASE_		:				int = 1_00 ,			)   ->						float:
						SCREAMING_SNAKE_CASE													=					x_start
						SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0.0
						for _ in range(SCREAMING_SNAKE_CASE_  ):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												SCREAMING_SNAKE_CASE													=					(x_end - x_start) / steps + xa
												SCREAMING_SNAKE_CASE													=					fnc(SCREAMING_SNAKE_CASE_  )
												area += abs(fxa + fxa  ) * (xa - xa) / 2
												# Increment step
												SCREAMING_SNAKE_CASE													=					xa
												SCREAMING_SNAKE_CASE													=					fxa
						return area


if __name__ == "__main__":


	def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Dict:
							return x**3 + x**2

	print('''f(x) = x^3 + x^2''')
	print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
	__UpperCamelCase						=      10
	while i <= 100000:
		print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
		i *= 10


