
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        lowerCAmelCase__      (   unittest.TestCase	):
      pass







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

      def  A_				(			self				)					->      List[str]:


        '''simple docstring'''

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

      def  A_				(			self				)					->      Dict:


        '''simple docstring'''

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

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

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

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

        _UpperCamelCase						    =	generator.manual_seed(0				)
        _UpperCamelCase						    =	pipe.dual_guided(
            prompt="""first prompt"""       ,							image=lowercase_       ,							text_to_image_strength=0.75       ,							generator=lowercase_       ,							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  A_				(			self				)					->      Any:


        '''simple docstring'''

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

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

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCamelCase						    =	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

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

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCamelCase						    =	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

        _UpperCamelCase						    =	pipe.image_variation(lowercase_       ,							generator=lowercase_       ,							output_type="""numpy"""				).images

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCamelCase						    =	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 warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



