

'''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						    =      False


class    A__ ( unittest.TestCase     ):
    pass


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



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						,			torch_dtype=torch.floataa    )
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

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

           A_		   = torch.manual_seed(0    )
           A_		   = 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__    )
                  A_		   = VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__						,			torch_dtype=torch.floataa    )
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = generator.manual_seed(0    )
           A_		   = 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    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""						,			torch_dtype=torch.floataa    )
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """cyberpunk 2077"""
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""    )
           A_		   = torch.manual_seed(0    )
           A_		   = pipe.dual_guided(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			text_to_image_strength=0.75						,			generator=UpperCamelCase__						,			guidance_scale=7.5						,			num_inference_steps=50						,			output_type="""numpy"""						,			).images

           A_		   = image[0, 253:256, 253:256, -1]

           assert image.shape == (1, 512, 512, 3)
           A_		   = 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

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

           A_		   = image[0, 253:256, 253:256, -1]

           assert image.shape == (1, 512, 512, 3)
           A_		   = 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

           A_		   = pipe.image_variation(UpperCamelCase__						,			generator=UpperCamelCase__						,			output_type="""numpy"""    ).images

           A_		   = image[0, 253:256, 253:256, -1]

           assert image.shape == (1, 512, 512, 3)
           A_		   = 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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						    =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      ['''BlenderbotTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BlenderbotForCausalLM''',
          '''BlenderbotForConditionalGeneration''',
          '''BlenderbotModel''',
          '''BlenderbotPreTrainedModel''',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''TFBlenderbotForConditionalGeneration''',
          '''TFBlenderbotModel''',
          '''TFBlenderbotPreTrainedModel''',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''FlaxBlenderbotForConditionalGeneration''',
          '''FlaxBlenderbotModel''',
          '''FlaxBlenderbotPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_blenderbot import (
          BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BlenderbotConfig,
          BlenderbotOnnxConfig,
      )
      from .tokenization_blenderbot import BlenderbotTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_blenderbot import (
                BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BlenderbotForCausalLM,
                BlenderbotForConditionalGeneration,
                BlenderbotModel,
                BlenderbotPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_blenderbot import (
                TFBlenderbotForConditionalGeneration,
                TFBlenderbotModel,
                TFBlenderbotPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_blenderbot import (
                FlaxBlenderbotForConditionalGeneration,
                FlaxBlenderbotModel,
                FlaxBlenderbotPreTrainedModel,
            )

else:
      import sys

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