


'''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


lowerCAmelCase    :     Optional[int]					=						False
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,	torch_dtype=torch.floataa   )
            pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )

            _lowerCAmelCase    :							List[Any]   			=       torch.manual_seed(0   )
            _lowerCAmelCase    :							List[str]   			=       pipe.dual_guided(
                prompt='first prompt'      ,	image=snake_case__      ,	text_to_image_strength=0.75      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=2      ,	output_type='numpy'      ,	).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(snake_case__   )
                   _lowerCAmelCase    :							Optional[Any]   			=       VersatileDiffusionPipeline.from_pretrained(snake_case__      ,	torch_dtype=torch.floataa   )
            pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       generator.manual_seed(0   )
            _lowerCAmelCase    :							List[Any]   			=       pipe.dual_guided(
                prompt='first prompt'      ,	image=snake_case__      ,	text_to_image_strength=0.75      ,	generator=snake_case__      ,	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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,	torch_dtype=torch.floataa   )
            pipe.to(snake_case__   )
            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							str   			=       'cyberpunk 2077'
            _lowerCAmelCase    :							Optional[int]   			=       load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
            _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )
            _lowerCAmelCase    :							Optional[int]   			=       pipe.dual_guided(
                prompt=snake_case__      ,	image=snake_case__      ,	text_to_image_strength=0.75      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=50      ,	output_type='numpy'      ,	).images

            _lowerCAmelCase    :							Union[str, Any]   			=       image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase    :							int   			=       np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]   )

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

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

            _lowerCAmelCase    :							Optional[Any]   			=       image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase    :							Dict   			=       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-1

            _lowerCAmelCase    :							int   			=       pipe.image_variation(snake_case__      ,	generator=snake_case__      ,	output_type='numpy'   ).images

            _lowerCAmelCase    :							List[Any]   			=       image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase    :							Dict   			=       np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]   )

            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


lowerCAmelCase    :     Any					=						{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     Optional[Any]					=						[
           """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """XCLIPModel""",
           """XCLIPPreTrainedModel""",
           """XCLIPTextModel""",
           """XCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_x_clip import (
           XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           XCLIPConfig,
           XCLIPTextConfig,
           XCLIPVisionConfig,
       )
       from .processing_x_clip import XCLIPProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_x_clip import (
                  XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  XCLIPModel,
                  XCLIPPreTrainedModel,
                  XCLIPTextModel,
                  XCLIPVisionModel,
              )

else:
       import sys

       lowerCAmelCase    :     Dict					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

