

import gc
import tempfile
import unittest

import numpy as np
import torch

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


__snake_case							:			List[Any]      		= False



@nightly
@require_torch_gpu
class 					A__(unittest.TestCase       ):
      """simple docstring"""






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





      def  UpperCamelCase__				(						self						)    ->   Dict:
         a_	:	Dict          =   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						)
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(_lowercase						)
         pipe.set_progress_bar_config(disable=_lowercase						)

         a_	:	Optional[int]          =   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""						)

         a_	:	List[str]          =   torch.manual_seed(0						)
         a_	:	List[str]          =   pipe(
             prompt="""first prompt"""     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			guidance_scale=7.5     ,			num_inference_steps=2     ,			output_type="""numpy"""     ,			).images

         with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(_lowercase						)
            a_	:	str          =   VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowercase						)

         pipe.to(_lowercase						)
         pipe.set_progress_bar_config(disable=_lowercase						)

         a_	:	Optional[Any]          =   generator.manual_seed(0						)
         a_	:	List[str]          =   pipe(
             prompt="""first prompt"""     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			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						)    ->   Dict:
         a_	:	Optional[Any]          =   VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						)
         pipe.remove_unused_weights()
         pipe.to(_lowercase						)
         pipe.set_progress_bar_config(disable=_lowercase						)

         a_	:	List[str]          =   '''cyberpunk 2077'''
         a_	:	List[str]          =   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""						)
         a_	:	int          =   torch.manual_seed(0						)
         a_	:	Any          =   pipe(
             prompt=_lowercase     ,			image=_lowercase     ,			text_to_image_strength=0.7_5     ,			generator=_lowercase     ,			guidance_scale=7.5     ,			num_inference_steps=50     ,			output_type="""numpy"""     ,			).images

         a_	:	Dict          =   image[0, 253:256, 253:256, -1]

         assert image.shape == (1, 512, 512, 3)
         a_	:	Optional[int]          =   np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case							:			Any      		= {
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			Dict      		= [
	    """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """ConvNextV2ForImageClassification""",
	    """ConvNextV2Model""",
	    """ConvNextV2PreTrainedModel""",
	    """ConvNextV2Backbone""",
	]


if TYPE_CHECKING:
	from .configuration_convnextva import (
	    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ConvNextVaConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnextva import (
		    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextVaBackbone,
		    ConvNextVaForImageClassification,
		    ConvNextVaModel,
		    ConvNextVaPreTrainedModel,
		)

else:
	import sys

	__snake_case							:			Dict      		= _LazyModule(__name__, globals()["""__file__"""], _import_structure)


