
'''simple docstring'''


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


__SCREAMING_SNAKE_CASE						=      False




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




		def 	_snake_case	(  self					:str)					->			Union[str, Any]:






								'''simple docstring'''






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




		def 	_snake_case	(  self					:Dict)					->			int:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion""")
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(_SCREAMING_SNAKE_CASE)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

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

								UpperCamelCase_     :	int					     =     torch.manual_seed(0)
								UpperCamelCase_     :	Union[str, Any]					     =     pipe(
								    prompt="""first prompt"""			,    image=_SCREAMING_SNAKE_CASE			,    text_to_image_strength=0.75			,    generator=_SCREAMING_SNAKE_CASE			,    guidance_scale=7.5			,    num_inference_steps=2			,    output_type="""numpy"""			,    ).images

								with tempfile.TemporaryDirectory() as tmpdirname:
														pipe.save_pretrained(_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Optional[Any]					     =     VersatileDiffusionDualGuidedPipeline.from_pretrained(_SCREAMING_SNAKE_CASE)

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

								UpperCamelCase_     :	int					     =     generator.manual_seed(0)
								UpperCamelCase_     :	Dict					     =     pipe(
								    prompt="""first prompt"""			,    image=_SCREAMING_SNAKE_CASE			,    text_to_image_strength=0.75			,    generator=_SCREAMING_SNAKE_CASE			,    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					:List[str])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion""")
								pipe.remove_unused_weights()
								pipe.to(_SCREAMING_SNAKE_CASE)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

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

								UpperCamelCase_     :	Optional[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)
								UpperCamelCase_     :	str					     =     np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779])

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








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_       :     Any 							=     {"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     int 							=     [
     "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BitForImageClassification",
     "BitModel",
     "BitPreTrainedModel",
     "BitBackbone",
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     Tuple 							=     ["BitImageProcessor"]


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 a_       :     Union[str, Any] 							=     _LazyModule(__name__, globals()["__file__"], _import_structure)

