
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


lowercase_											=	False





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






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)

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

       __SCREAMING_SNAKE_CASE     :					Dict			=						torch.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pipe(
           prompt='''first prompt'''      ,		image=_A      ,		text_to_image_strength=0.75      ,		generator=_A      ,		guidance_scale=7.5      ,		num_inference_steps=2      ,		output_type='''numpy'''      ,		).images

       with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(_A				)
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						VersatileDiffusionDualGuidedPipeline.from_pretrained(_A				)

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

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						generator.manual_seed(0				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						pipe(
           prompt='''first prompt'''      ,		image=_A      ,		text_to_image_strength=0.75      ,		generator=_A      ,		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    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
       pipe.remove_unused_weights()
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)

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

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       __SCREAMING_SNAKE_CASE     :					Any			=						np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

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


from typing import TYPE_CHECKING

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


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""LEDTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
