





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


SCREAMING_SNAKE_CASE_		     = False

class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     pass

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



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""		,torch_dtype=torch.floataa )
           pipe.to(lowerCamelCase__ )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

           SCREAMING_SNAKE_CASE	=		torch.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		pipe.dual_guided(
               prompt="""first prompt"""		,image=lowerCamelCase__		,text_to_image_strength=0.75		,generator=lowerCamelCase__		,guidance_scale=7.5		,num_inference_steps=2		,output_type="""numpy"""		,).images

           with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		VersatileDiffusionPipeline.from_pretrained(lowerCamelCase__		,torch_dtype=torch.floataa )
           pipe.to(lowerCamelCase__ )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		generator.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		pipe.dual_guided(
               prompt="""first prompt"""		,image=lowerCamelCase__		,text_to_image_strength=0.75		,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		:					int )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""		,torch_dtype=torch.floataa )
           pipe.to(lowerCamelCase__ )
           pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

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

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

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

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

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

           SCREAMING_SNAKE_CASE	=		pipe.image_variation(lowerCamelCase__		,generator=lowerCamelCase__		,output_type="""numpy""" ).images

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

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









import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""				,		set()			)



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      class 	UpperCamelCase__				:



           '''simple docstring'''

           def __init__(							self		:					int		,lowerCamelCase__		:					Dict )  ->	List[Any]:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE	=		metric_id

      class 	UpperCamelCase__				:



           '''simple docstring'''

           __snake_case					:			List[str]				=    [MetricMock(lowerCAmelCase_     ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]






           def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




                 '''simple docstring'''


                 return self._metrics
      monkeypatch.setattr("""datasets.inspect.huggingface_hub"""				,		HfhMock()			)



@pytest.mark.parametrize(
    """func, args"""				,		[(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))]			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      if "tmp_path" in args:
            SCREAMING_SNAKE_CASE	=		tuple(arg if arg != """tmp_path""" else tmp_path for arg in args			)
      with pytest.warns(_SCREAMING_SNAKE_CASE				,		match="""https://huggingface.co/docs/evaluate"""			):
            func(*_SCREAMING_SNAKE_CASE			)


