


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 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'				, torch_dtype=torch.floataa )
        pipe.to(lowerCAmelCase__ )
        pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

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

        lowercase__:   Optional[Any]  			=   torch.manual_seed(0 )
        lowercase__:   Optional[Any]  			=   pipe.dual_guided(
            prompt='first prompt'				, image=lowerCAmelCase__				, text_to_image_strength=0.7_5				, generator=lowerCAmelCase__				, guidance_scale=7.5				, num_inference_steps=2				, output_type='numpy'				, ).images

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

        lowercase__:   int  			=   generator.manual_seed(0 )
        lowercase__:   Optional[int]  			=   pipe.dual_guided(
            prompt='first prompt'				, image=lowerCAmelCase__				, text_to_image_strength=0.7_5				, 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 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   Dict  			=   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'				, torch_dtype=torch.floataa )
        pipe.to(lowerCAmelCase__ )
        pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

        lowercase__:   Union[str, Any]  			=   'cyberpunk 2077'
        lowercase__:   List[Any]  			=   load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
        lowercase__:   int  			=   torch.manual_seed(0 )
        lowercase__:   int  			=   pipe.dual_guided(
            prompt=lowerCAmelCase__				, image=lowerCAmelCase__				, text_to_image_strength=0.7_5				, generator=lowerCAmelCase__				, guidance_scale=7.5				, num_inference_steps=50				, output_type='numpy'				, ).images

        lowercase__:   Tuple  			=   image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__:   Tuple  			=   np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1] )

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

        lowercase__:   Any  			=   'A painting of a squirrel eating a burger '
        lowercase__:   int  			=   torch.manual_seed(0 )
        lowercase__:   Optional[Any]  			=   pipe.text_to_image(
            prompt=lowerCAmelCase__				, generator=lowerCAmelCase__				, guidance_scale=7.5				, num_inference_steps=50				, output_type='numpy' ).images

        lowercase__:   Any  			=   image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__:   Dict  			=   np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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

        lowercase__:   List[Any]  			=   pipe.image_variation(lowerCAmelCase__				, generator=lowerCAmelCase__				, output_type='numpy' ).images

        lowercase__:   List[Any]  			=   image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        lowercase__:   str  			=   np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6] )

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






from typing import TYPE_CHECKING

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


__lowerCAmelCase		       =							{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EncodecModel''',
     '''EncodecPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

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


