
"""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					:	Union[str, Any]							=		False




class 		a_						( unittest.TestCase	):

    '''simple docstring'''





    pass







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

    '''simple docstring'''





    def      _snake_case					(			self			:       List[str]     )		->Any:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def      _snake_case					(			self			:       Union[str, Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     ,					torch_dtype=torch.floataa     )
        pipe.to(__UpperCamelCase     )
        pipe.set_progress_bar_config(disable=__UpperCamelCase     )

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

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

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

        _UpperCAmelCase          =   generator.manual_seed(0     )
        _UpperCAmelCase          =   pipe.dual_guided(
            prompt="""first prompt"""     ,					image=__UpperCamelCase     ,					text_to_image_strength=0.7_5     ,					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      _snake_case					(			self			:       Union[str, Any]     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""     ,					torch_dtype=torch.floataa     )
        pipe.to(__UpperCamelCase     )
        pipe.set_progress_bar_config(disable=__UpperCamelCase     )

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

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase          =   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

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

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase          =   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

        _UpperCAmelCase          =   pipe.image_variation(__UpperCamelCase     ,					generator=__UpperCamelCase     ,					output_type="""numpy"""     ).images

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase          =   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"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a					:	Tuple							=		{
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a					:	List[str]							=		[
     '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''PegasusXForConditionalGeneration''',
     '''PegasusXModel''',
     '''PegasusXPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 a					:	Dict							=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)