
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


lowercase   =	False





class    __A(					unittest.TestCase							):
					pass





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

					def  lowercase__   (						self       :  Optional[Any] ):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" ,       torch_dtype=torch.floataa )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												lowerCamelCase_					=       torch.manual_seed(0 )
												lowerCamelCase_					=       pipe.dual_guided(
												    prompt="""first prompt""" ,       image=__UpperCamelCase ,       text_to_image_strength=0.75 ,       generator=__UpperCamelCase ,       guidance_scale=7.5 ,       num_inference_steps=2 ,       output_type="""numpy""" ,       ).images

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

												lowerCamelCase_					=       generator.manual_seed(0 )
												lowerCamelCase_					=       pipe.dual_guided(
												    prompt="""first prompt""" ,       image=__UpperCamelCase ,       text_to_image_strength=0.75 ,       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  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" ,       torch_dtype=torch.floataa )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       """cyberpunk 2077"""
												lowerCamelCase_					=       load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
												lowerCamelCase_					=       torch.manual_seed(0 )
												lowerCamelCase_					=       pipe.dual_guided(
												    prompt=__UpperCamelCase ,       image=__UpperCamelCase ,       text_to_image_strength=0.75 ,       generator=__UpperCamelCase ,       guidance_scale=7.5 ,       num_inference_steps=5_0 ,       output_type="""numpy""" ,       ).images

												lowerCamelCase_					=       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       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_					=       """A painting of a squirrel eating a burger """
												lowerCamelCase_					=       torch.manual_seed(0 )
												lowerCamelCase_					=       pipe.text_to_image(
												    prompt=__UpperCamelCase ,       generator=__UpperCamelCase ,       guidance_scale=7.5 ,       num_inference_steps=5_0 ,       output_type="""numpy""" ).images

												lowerCamelCase_					=       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       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_					=       pipe.image_variation(__UpperCamelCase ,       generator=__UpperCamelCase ,       output_type="""numpy""" ).images

												lowerCamelCase_					=       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase   =	{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''BlipImageProcessor''']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BlipModel''',
		    '''BlipPreTrainedModel''',
		    '''BlipForConditionalGeneration''',
		    '''BlipForQuestionAnswering''',
		    '''BlipVisionModel''',
		    '''BlipTextModel''',
		    '''BlipForImageTextRetrieval''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	[
		    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFBlipModel''',
		    '''TFBlipPreTrainedModel''',
		    '''TFBlipForConditionalGeneration''',
		    '''TFBlipForQuestionAnswering''',
		    '''TFBlipVisionModel''',
		    '''TFBlipTextModel''',
		    '''TFBlipForImageTextRetrieval''',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





