


'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_					:    List[Any]          =    False




class  a     (     unittest.TestCase       ):
 pass





@nightly
@require_torch_gpu
class  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,				torch_dtype=torch.floataa						)
      pipe.to(__magic_name__						)
      pipe.set_progress_bar_config(disable=__magic_name__						)

      _a			      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

      _a			      =							torch.manual_seed(0						)
      _a			      =							pipe.dual_guided(
          prompt='first prompt'      ,				image=__magic_name__      ,				text_to_image_strength=0.7_5      ,				generator=__magic_name__      ,				guidance_scale=7.5      ,				num_inference_steps=2      ,				output_type='numpy'      ,				).images

      with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__						)
           _a			      =							VersatileDiffusionPipeline.from_pretrained(__magic_name__      ,				torch_dtype=torch.floataa						)
      pipe.to(__magic_name__						)
      pipe.set_progress_bar_config(disable=__magic_name__						)

      _a			      =							generator.manual_seed(0						)
      _a			      =							pipe.dual_guided(
          prompt='first prompt'      ,				image=__magic_name__      ,				text_to_image_strength=0.7_5      ,				generator=__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,				torch_dtype=torch.floataa						)
      pipe.to(__magic_name__						)
      pipe.set_progress_bar_config(disable=__magic_name__						)

      _a			      =							'cyberpunk 2077'
      _a			      =							load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
      _a			      =							torch.manual_seed(0						)
      _a			      =							pipe.dual_guided(
          prompt=__magic_name__      ,				image=__magic_name__      ,				text_to_image_strength=0.7_5      ,				generator=__magic_name__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type='numpy'      ,				).images

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

      assert image.shape == (1, 5_12, 5_12, 3)
      _a			      =							np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]						)

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

      _a			      =							'A painting of a squirrel eating a burger '
      _a			      =							torch.manual_seed(0						)
      _a			      =							pipe.text_to_image(
          prompt=__magic_name__      ,				generator=__magic_name__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type='numpy'						).images

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

      assert image.shape == (1, 5_12, 5_12, 3)
      _a			      =							np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]						)

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

      _a			      =							pipe.image_variation(__magic_name__      ,				generator=__magic_name__      ,				output_type='numpy'						).images

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

      assert image.shape == (1, 5_12, 5_12, 3)
      _a			      =							np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]						)

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





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

