import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class   lowercase      (       unittest.TestCase       ):




							@property
							def 		__snake_case(  self				:   Any )   ->			List[str]:



												'''simple docstring'''

												torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    UNetaDModel(
												    block_out_channels=(32, 64)		,							layers_per_block=2		,							sample_size=32		,							in_channels=3		,							out_channels=3		,							down_block_types=("DownBlock2D", "AttnDownBlock2D")		,							up_block_types=("AttnUpBlock2D", "UpBlock2D")		,							)
												return model




							@property
							def 		__snake_case(  self				:   Optional[int] )   ->			int:



												'''simple docstring'''

												torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    VQModel(
												    block_out_channels=[32, 64]		,							in_channels=3		,							out_channels=3		,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,							latent_channels=3		,							)
												return model




							@property
							def 		__snake_case(  self				:   Dict )   ->			List[str]:



												'''simple docstring'''

												torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    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		,							)
												return CLIPTextModel(_UpperCamelCase )





							def 		__snake_case(  self				:   Union[str, Any] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.dummy_uncond_unet
												SCREAMING_SNAKE_CASE           =    DDIMScheduler()
												SCREAMING_SNAKE_CASE           =    self.dummy_vq_model

												SCREAMING_SNAKE_CASE           =    LDMPipeline(unet=_UpperCamelCase		,							vqvae=_UpperCamelCase		,							scheduler=_UpperCamelCase )
												ldm.to(_UpperCamelCase )
												ldm.set_progress_bar_config(disable=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    ldm(generator=_UpperCamelCase		,							num_inference_steps=2		,							output_type="numpy" ).images

												SCREAMING_SNAKE_CASE           =    torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    ldm(generator=_UpperCamelCase		,							num_inference_steps=2		,							output_type="numpy"		,							return_dict=_UpperCamelCase )[0]

												SCREAMING_SNAKE_CASE           =    image[0, -3:, -3:, -1]
												SCREAMING_SNAKE_CASE           =    image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												SCREAMING_SNAKE_CASE           =    np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2] )
												SCREAMING_SNAKE_CASE           =    1e-2 if torch_device != "mps" else 3e-2

												assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance



@slow
@require_torch
class   lowercase      (       unittest.TestCase       ):




							def 		__snake_case(  self				:   Dict )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" )
												ldm.to(_UpperCamelCase )
												ldm.set_progress_bar_config(disable=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.manual_seed(0 )
												SCREAMING_SNAKE_CASE           =    ldm(generator=_UpperCamelCase		,							num_inference_steps=5		,							output_type="numpy" ).images

												SCREAMING_SNAKE_CASE           =    image[0, -3:, -3:, -1]

												assert image.shape == (1, 256, 256, 3)
												SCREAMING_SNAKE_CASE           =    np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7] )
												SCREAMING_SNAKE_CASE           =    1e-2 if torch_device != "mps" else 3e-2

												assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




