
"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A__      :	Any          =    False







class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     pass







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


     '''simple docstring'''





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



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: List[Any] 							=						VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"  )
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase  )
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: Optional[Any] 							=						"A painting of a squirrel eating a burger "
          UpperCAmelCase__							: Optional[int] 							=						torch.manual_seed(0  )
          UpperCAmelCase__							: int 							=						pipe(
              prompt=__UpperCamelCase ,			generator=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=2 ,			output_type="numpy"  ).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase  )
               UpperCAmelCase__							: int 							=						VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase  )
          pipe.to(__UpperCamelCase  )
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						generator.manual_seed(0  )
          UpperCAmelCase__							: Tuple 							=						pipe(
              prompt=__UpperCamelCase ,			generator=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=2 ,			output_type="numpy"  ).images

          assert np.abs(image - new_image  ).sum() < 1E-5, "Models don't have the same forward pass"



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: List[Any] 							=						VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion" ,			torch_dtype=torch.floataa  )
          pipe.to(__UpperCamelCase  )
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: Union[str, Any] 							=						"A painting of a squirrel eating a burger "
          UpperCAmelCase__							: Optional[Any] 							=						torch.manual_seed(0  )
          UpperCAmelCase__							: Optional[int] 							=						pipe(
              prompt=__UpperCamelCase ,			generator=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=50 ,			output_type="numpy"  ).images

          UpperCAmelCase__							: Dict 							=						image[0, 2_53:2_56, 2_53:2_56, -1]

          assert image.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: int 							=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2





"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



