
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 				A__ (			unittest.TestCase):

		@property
		def 							__lowerCamelCase							(   self				):
			torch.manual_seed(0				)
			__lowerCAmelCase    :		Optional[Any]		    =   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 							__lowerCamelCase							(   self				):
			torch.manual_seed(0				)
			__lowerCAmelCase    :		List[Any]		    =   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 							__lowerCamelCase							(   self				):
			torch.manual_seed(0				)
			__lowerCAmelCase    :		Optional[Any]		    =   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=10_00 ,	)
			return CLIPTextModel(SCREAMING_SNAKE_CASE_				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.dummy_uncond_unet
			__lowerCAmelCase    :		List[str]		    =   DDIMScheduler()
			__lowerCAmelCase    :		List[Any]		    =   self.dummy_vq_model

			__lowerCAmelCase    :		Optional[Any]		    =   LDMPipeline(unet=SCREAMING_SNAKE_CASE_ ,	vqvae=SCREAMING_SNAKE_CASE_ ,	scheduler=SCREAMING_SNAKE_CASE_				)
			ldm.to(SCREAMING_SNAKE_CASE_				)
			ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

			__lowerCAmelCase    :		Union[str, Any]		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		Optional[int]		    =   ldm(generator=SCREAMING_SNAKE_CASE_ ,	num_inference_steps=2 ,	output_type='numpy'				).images

			__lowerCAmelCase    :		List[str]		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		List[Any]		    =   ldm(generator=SCREAMING_SNAKE_CASE_ ,	num_inference_steps=2 ,	output_type='numpy' ,	return_dict=SCREAMING_SNAKE_CASE_				)[0]

			__lowerCAmelCase    :		List[str]		    =   image[0, -3:, -3:, -1]
			__lowerCAmelCase    :		str		    =   image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__lowerCAmelCase    :		Dict		    =   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]				)
			__lowerCAmelCase    :		Union[str, Any]		    =   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 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'				)
			ldm.to(SCREAMING_SNAKE_CASE_				)
			ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

			__lowerCAmelCase    :		Any		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		Tuple		    =   ldm(generator=SCREAMING_SNAKE_CASE_ ,	num_inference_steps=5 ,	output_type='numpy'				).images

			__lowerCAmelCase    :		List[str]		    =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 2_56, 2_56, 3)
			__lowerCAmelCase    :		List[Any]		    =   np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]				)
			__lowerCAmelCase    :		Tuple		    =   1E-2 if torch_device != 'mps' else 3E-2

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   [[0] * n for i in range(_UpperCamelCase	)]
	for i in range(_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   y_points[i]

	for i in range(2 ,						_UpperCamelCase	):
		for j in range(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		int		    =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()