





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


_lowerCamelCase			       =      False







class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      pass







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




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		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							:     str	=		'''A painting of a squirrel eating a burger '''
             _lowerCamelCase							:     Union[str, Any]	=		torch.manual_seed(0)
             _lowerCamelCase							:     str	=		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							:     Dict	=		VersatileDiffusionTextToImagePipeline.from_pretrained(a__)
             pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase							:     Any	=		generator.manual_seed(0)
             _lowerCamelCase							:     Union[str, Any]	=		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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		VersatileDiffusionTextToImagePipeline.from_pretrained(
                 '''shi-labs/versatile-diffusion'''      ,							torch_dtype=torch.floataa)
             pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase							:     int	=		'''A painting of a squirrel eating a burger '''
             _lowerCamelCase							:     str	=		torch.manual_seed(0)
             _lowerCamelCase							:     List[str]	=		pipe(
                 prompt=a__      ,							generator=a__      ,							guidance_scale=7.5      ,							num_inference_steps=50      ,							output_type='''numpy''').images

             _lowerCamelCase							:     Optional[int]	=		image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             _lowerCamelCase							:     Optional[Any]	=		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








import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DonutImageProcessor instead.'''      ,							a__      ,							)
             super().__init__(*a__      ,							**a__)

