

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor






class  _lowerCAmelCase     (  unittest.TestCase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=3      ,				__UpperCAmelCase=18      ,				__UpperCAmelCase=30      ,				__UpperCAmelCase=400      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=[0.5, 0.5, 0.5]      ,				__UpperCAmelCase=[0.5, 0.5, 0.5]      ,				):
        lowerCAmelCase__				:  List[str]		       =     size if size is not None else {'''shortest_edge''': 18}
        lowerCAmelCase__				:  List[str]		       =     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        lowerCAmelCase__				:  Dict		       =     parent
        lowerCAmelCase__				:  Dict		       =     batch_size
        lowerCAmelCase__				:  Tuple		       =     num_channels
        lowerCAmelCase__				:  Tuple		       =     image_size
        lowerCAmelCase__				:  int		       =     min_resolution
        lowerCAmelCase__				:  Tuple		       =     max_resolution
        lowerCAmelCase__				:  str		       =     do_resize
        lowerCAmelCase__				:  Union[str, Any]		       =     size
        lowerCAmelCase__				:  Dict		       =     do_center_crop
        lowerCAmelCase__				:  Optional[Any]		       =     crop_size
        lowerCAmelCase__				:  Tuple		       =     do_normalize
        lowerCAmelCase__				:  List[Any]		       =     image_mean
        lowerCAmelCase__				:  Any		       =     image_std







      def 	__magic_name__(   self		):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "do_center_crop": self.do_center_crop,
            "size": self.size,
            "crop_size": self.crop_size,
        }






@require_torch
@require_vision
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      A__		     =		LevitImageProcessor if is_vision_available() else None

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     LevitImageProcessingTester(self		)

      @property
      def 	__magic_name__(   self		):
        return self.image_processor_tester.prepare_image_processor_dict()

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(_lowerCAmelCase      ,				'''image_mean'''		)		)
        self.assertTrue(hasattr(_lowerCAmelCase      ,				'''image_std'''		)		)
        self.assertTrue(hasattr(_lowerCAmelCase      ,				'''do_normalize'''		)		)
        self.assertTrue(hasattr(_lowerCAmelCase      ,				'''do_resize'''		)		)
        self.assertTrue(hasattr(_lowerCAmelCase      ,				'''do_center_crop'''		)		)
        self.assertTrue(hasattr(_lowerCAmelCase      ,				'''size'''		)		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size      ,				{'''shortest_edge''': 18}		)
        self.assertEqual(image_processor.crop_size      ,				{'''height''': 18, '''width''': 18}		)

        lowerCAmelCase__				:  Dict		       =     self.image_processing_class.from_dict(self.image_processor_dict      ,				size=42      ,				crop_size=84		)
        self.assertEqual(image_processor.size      ,				{'''shortest_edge''': 42}		)
        self.assertEqual(image_processor.crop_size      ,				{'''height''': 84, '''width''': 84}		)

      def 	__magic_name__(   self		):
        pass

      def 	__magic_name__(   self		):
        # Initialize image_processing
        lowerCAmelCase__				:  Union[str, Any]		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        lowerCAmelCase__				:  Tuple		       =     prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=_lowerCAmelCase		)
        for image in image_inputs:
          self.assertIsInstance(_lowerCAmelCase      ,				Image.Image		)

        # Test not batched input
        lowerCAmelCase__				:  List[str]		       =     image_processing(image_inputs[0]      ,				return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape      ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      ,				)

        # Test batched
        lowerCAmelCase__				:  Dict		       =     image_processing(_lowerCAmelCase      ,				return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape      ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      ,				)

      def 	__magic_name__(   self		):
        # Initialize image_processing
        lowerCAmelCase__				:  Any		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        lowerCAmelCase__				:  Optional[Any]		       =     prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=_lowerCAmelCase      ,				numpify=_lowerCAmelCase		)
        for image in image_inputs:
          self.assertIsInstance(_lowerCAmelCase      ,				np.ndarray		)

        # Test not batched input
        lowerCAmelCase__				:  Optional[Any]		       =     image_processing(image_inputs[0]      ,				return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape      ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      ,				)

        # Test batched
        lowerCAmelCase__				:  int		       =     image_processing(_lowerCAmelCase      ,				return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape      ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      ,				)







      def 	__magic_name__(   self		):
        # Initialize image_processing
        lowerCAmelCase__				:  str		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        lowerCAmelCase__				:  Tuple		       =     prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=_lowerCAmelCase      ,				torchify=_lowerCAmelCase		)
        for image in image_inputs:
          self.assertIsInstance(_lowerCAmelCase      ,				torch.Tensor		)

        # Test not batched input
        lowerCAmelCase__				:  Dict		       =     image_processing(image_inputs[0]      ,				return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape      ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      ,				)

        # Test batched
        lowerCAmelCase__				:  Any		       =     image_processing(_lowerCAmelCase      ,				return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_images.shape      ,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      ,				)







import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)

