

"""simple docstring"""

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       ):





       """simple docstring"""


       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        torch.manual_seed(0   )
        lowerCAmelCase__   :				Union[str, 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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        torch.manual_seed(0   )
        lowerCAmelCase__   :				List[str] 			=				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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        torch.manual_seed(0   )
        lowerCAmelCase__   :				Union[str, 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=1_000			,					)
        return CLIPTextModel(snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.dummy_uncond_unet
        lowerCAmelCase__   :				List[Any] 			=				DDIMScheduler()
        lowerCAmelCase__   :				Optional[Any] 			=				self.dummy_vq_model

        lowerCAmelCase__   :				str 			=				LDMPipeline(unet=snake_case			,					vqvae=snake_case			,					scheduler=snake_case   )
        ldm.to(snake_case   )
        ldm.set_progress_bar_config(disable=snake_case   )

        lowerCAmelCase__   :				Tuple 			=				torch.manual_seed(0   )
        lowerCAmelCase__   :				Optional[int] 			=				ldm(generator=snake_case			,					num_inference_steps=2			,					output_type="numpy"   ).images

        lowerCAmelCase__   :				Tuple 			=				torch.manual_seed(0   )
        lowerCAmelCase__   :				Tuple 			=				ldm(generator=snake_case			,					num_inference_steps=2			,					output_type="numpy"			,					return_dict=snake_case   )[0]

        lowerCAmelCase__   :				List[Any] 			=				image[0, -3:, -3:, -1]
        lowerCAmelCase__   :				Tuple 			=				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCAmelCase__   :				str 			=				np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]   )
        lowerCAmelCase__   :				List[str] 			=				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       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"   )
        ldm.to(snake_case   )
        ldm.set_progress_bar_config(disable=snake_case   )

        lowerCAmelCase__   :				Any 			=				torch.manual_seed(0   )
        lowerCAmelCase__   :				Tuple 			=				ldm(generator=snake_case			,					num_inference_steps=5			,					output_type="numpy"   ).images

        lowerCAmelCase__   :				Tuple 			=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        lowerCAmelCase__   :				int 			=				np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]   )
        lowerCAmelCase__   :				Dict 			=				1e-2 if torch_device != "mps" else 3e-2

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









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 if digit_amount > 0:
  return round(number - int(lowercase__   )					,					lowercase__   )
 return number - int(lowercase__   )


if __name__ == "__main__":
    print(decimal_isolate(1.53, 0))
    print(decimal_isolate(35.345, 1))
    print(decimal_isolate(35.345, 2))
    print(decimal_isolate(35.345, 3))
    print(decimal_isolate(-14.789, 3))
    print(decimal_isolate(0, 2))
    print(decimal_isolate(-14.123, 1))
    print(decimal_isolate(-14.123, 2))
    print(decimal_isolate(-14.123, 3))






