import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


snake_case__				= False



class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     pass





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

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				List[Any]:






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



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
           # remove text_unet
           pipe.remove_unused_weights()
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				'''A painting of a squirrel eating a burger '''
           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				generator=A_				,				guidance_scale=7.5				,				num_inference_steps=2				,				output_type='''numpy'''						).images

           with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(A_						)
                 _lowerCamelCase			    =				VersatileDiffusionTextToImagePipeline.from_pretrained(A_						)
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				generator.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				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[int]:






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

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

           _lowerCamelCase			    =				image[0, 2_53:2_56, 2_53:2_56, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowerCamelCase			    =				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-2
from typing import TYPE_CHECKING

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


snake_case__				= {
    'configuration_trajectory_transformer': [
        'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TrajectoryTransformerConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__				= [
         'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TrajectoryTransformerModel',
         'TrajectoryTransformerPreTrainedModel',
         'load_tf_weights_in_trajectory_transformer',
     ]


if TYPE_CHECKING:
     from .configuration_trajectory_transformer import (
         TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TrajectoryTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_trajectory_transformer import (
              TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TrajectoryTransformerModel,
              TrajectoryTransformerPreTrainedModel,
              load_tf_weights_in_trajectory_transformer,
          )


else:
     import sys

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