


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Any					=							StableDiffusionSAGPipeline
						SCREAMING_SNAKE_CASE__      :  Optional[Any]					=							TEXT_TO_IMAGE_PARAMS
						SCREAMING_SNAKE_CASE__      :  str					=							TEXT_TO_IMAGE_BATCH_PARAMS
						SCREAMING_SNAKE_CASE__      :  Dict					=							TEXT_TO_IMAGE_IMAGE_PARAMS
						SCREAMING_SNAKE_CASE__      :  Any					=							TEXT_TO_IMAGE_IMAGE_PARAMS
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							False




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										torch.manual_seed(0							)
										lowerCAmelCase_ :      Dict    				=				UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)   ,						layers_per_block=2   ,						sample_size=3_2   ,						in_channels=4   ,						out_channels=4   ,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,						cross_attention_dim=3_2   ,						)
										lowerCAmelCase_ :      List[Any]    				=				DDIMScheduler(
										    beta_start=0.0_00_85   ,						beta_end=0.0_12   ,						beta_schedule='''scaled_linear'''   ,						clip_sample=__lowercase   ,						set_alpha_to_one=__lowercase   ,						)
										torch.manual_seed(0							)
										lowerCAmelCase_ :      List[Any]    				=				AutoencoderKL(
										    block_out_channels=[3_2, 6_4]   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,						latent_channels=4   ,						)
										torch.manual_seed(0							)
										lowerCAmelCase_ :      int    				=				CLIPTextConfig(
										    bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=3_2   ,						intermediate_size=3_7   ,						layer_norm_eps=1e-05   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=1_0_0_0   ,						)
										lowerCAmelCase_ :      Tuple    				=				CLIPTextModel(__lowercase							)
										lowerCAmelCase_ :      Optional[int]    				=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

										lowerCAmelCase_ :      Any    				=				{
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase=0							)					->    Tuple:
										if str(__lowercase							).startswith('''mps'''							):
														lowerCAmelCase_ :      List[str]    				=				torch.manual_seed(__lowercase							)
										else:
														lowerCAmelCase_ :      List[str]    				=				torch.Generator(device=__lowercase							).manual_seed(__lowercase							)
										lowerCAmelCase_ :      Any    				=				{
										    '''prompt''': '''.''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 1.0,
										    '''sag_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      List[str]    				=				StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''							)
										lowerCAmelCase_ :      Optional[int]    				=				sag_pipe.to(__lowercase							)
										sag_pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      List[Any]    				=				'''.'''
										lowerCAmelCase_ :      Optional[Any]    				=				torch.manual_seed(0							)
										lowerCAmelCase_ :      List[Any]    				=				sag_pipe(
										    [prompt]   ,						generator=__lowercase   ,						guidance_scale=7.5   ,						sag_scale=1.0   ,						num_inference_steps=2_0   ,						output_type='''np'''							)

										lowerCAmelCase_ :      List[str]    				=				output.images

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

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										lowerCAmelCase_ :      Any    				=				np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 5e-2




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      List[str]    				=				StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''							)
										lowerCAmelCase_ :      int    				=				sag_pipe.to(__lowercase							)
										sag_pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      Any    				=				'''.'''
										lowerCAmelCase_ :      str    				=				torch.manual_seed(0							)
										lowerCAmelCase_ :      Dict    				=				sag_pipe(
										    [prompt]   ,						generator=__lowercase   ,						guidance_scale=7.5   ,						sag_scale=1.0   ,						num_inference_steps=2_0   ,						output_type='''np'''							)

										lowerCAmelCase_ :      Optional[int]    				=				output.images

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

										assert image.shape == (1, 5_1_2, 5_1_2, 3)
										lowerCAmelCase_ :      Tuple    				=				np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 5e-2




						def 						lowercase_ (				self							)					->    str:
										lowerCAmelCase_ :      Tuple    				=				StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''							)
										lowerCAmelCase_ :      Tuple    				=				sag_pipe.to(__lowercase							)
										sag_pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      Any    				=				'''.'''
										lowerCAmelCase_ :      Dict    				=				torch.manual_seed(0							)
										lowerCAmelCase_ :      Dict    				=				sag_pipe(
										    [prompt]   ,						width=7_6_8   ,						height=5_1_2   ,						generator=__lowercase   ,						guidance_scale=7.5   ,						sag_scale=1.0   ,						num_inference_steps=2_0   ,						output_type='''np'''   ,						)

										lowerCAmelCase_ :      Tuple    				=				output.images

										assert image.shape == (1, 5_1_2, 7_6_8, 3)



from __future__ import annotations



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   )-> tuple:
				if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
								raise ValueError('''You cannot supply more or less than 2 values''' )
				elif electron_conc < 0:
								raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
				elif hole_conc < 0:
								raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
				elif intrinsic_conc < 0:
								raise ValueError(
								    '''Intrinsic concentration cannot be negative in a semiconductor''' )
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    intrinsic_conc**2 / hole_conc,
								)
				elif hole_conc == 0:
								return (
								    "hole_conc",
								    intrinsic_conc**2 / electron_conc,
								)
				elif intrinsic_conc == 0:
								return (
								    "intrinsic_conc",
								    (electron_conc * hole_conc) ** 0.5,
								)
				else:
								return (-1, -1)


if __name__ == "__main__":
						import doctest

						doctest.testmod()