





# 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, is_vision_available


UpperCAmelCase	:	int		     =       {
    """configuration_efficientnet""": [
        """EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientNetConfig""",
        """EfficientNetOnnxConfig""",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Optional[int]		     =       ["""EfficientNetImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Dict		     =       [
         """EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """EfficientNetForImageClassification""",
         """EfficientNetModel""",
         """EfficientNetPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_efficientnet import (
         EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EfficientNetConfig,
         EfficientNetOnnxConfig,
     )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_efficientnet import EfficientNetImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_efficientnet import (
              EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              EfficientNetForImageClassification,
              EfficientNetModel,
              EfficientNetPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase	:	Optional[int]		     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)













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


UpperCAmelCase	:	int		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):
      pass







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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

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

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					Optional[Any]										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

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

        a__	:					Optional[Any]										=generator.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      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							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="cyberpunk 2077"
        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"		,      ).images

        a__	:					int										=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)
        a__	:					Any										=np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]							)

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

        a__	:					str										="A painting of a squirrel eating a burger "
        a__	:					Optional[int]										=torch.manual_seed(0							)
        a__	:					str										=pipe.text_to_image(
            prompt=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"							).images

        a__	:					Any										=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)
        a__	:					Optional[int]										=np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

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

        a__	:					Optional[Any]										=pipe.image_variation(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="numpy"							).images

        a__	:					Union[str, Any]										=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)
        a__	:					Any										=np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]							)

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






