




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        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			Optional[int]=7      ,				UpperCAmelCase__   :			Union[str, Any]=3      ,				UpperCAmelCase__   :			Dict=18      ,				UpperCAmelCase__   :			List[Any]=30      ,				UpperCAmelCase__   :			Tuple=400      ,				UpperCAmelCase__   :			Tuple=True      ,				UpperCAmelCase__   :			str=None      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Union[str, Any]=None      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Dict=[0.5, 0.5, 0.5]      ,				UpperCAmelCase__   :			Union[str, Any]=[0.5, 0.5, 0.5]      ,				)		->List[str]:





           '''simple docstring'''

           A__   				=					size if size is not None else {'''shortest_edge''': 18}
           A__   				=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
           A__   				=					parent
           A__   				=					batch_size
           A__   				=					num_channels
           A__   				=					image_size
           A__   				=					min_resolution
           A__   				=					max_resolution
           A__   				=					do_resize
           A__   				=					size
           A__   				=					do_center_crop
           A__   				=					crop_size
           A__   				=					do_normalize
           A__   				=					image_mean
           A__   				=					image_std





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           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        UpperCamelCase_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   LevitImageProcessor if is_vision_available() else None


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[Any]:





           '''simple docstring'''

           A__   				=					LevitImageProcessingTester(self)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Tuple:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


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





           '''simple docstring'''

           A__   				=					self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''image_mean'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''image_std'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''do_normalize'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''do_center_crop'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''size'''))


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					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})

           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Dict:





           '''simple docstring'''

           A__   				=					self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__   				=					prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__      ,				Image.Image)

           # Test not batched input
           A__   				=					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
           A__   				=					image_processing(UpperCAmelCase__      ,				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  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Any:





           '''simple docstring'''

           A__   				=					self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__   				=					prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=UpperCAmelCase__      ,				numpify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__      ,				np.ndarray)

           # Test not batched input
           A__   				=					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
           A__   				=					image_processing(UpperCAmelCase__      ,				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  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Any:





           '''simple docstring'''

           A__   				=					self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__   				=					prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=UpperCAmelCase__      ,				torchify=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__      ,				torch.Tensor)

           # Test not batched input
           A__   				=					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
           A__   				=					image_processing(UpperCAmelCase__      ,				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'''],
               )      ,				)








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			int = 32      ,				UpperCAmelCase__   :			Dict=PILImageResampling.BILINEAR      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					do_resize
           A__   				=					do_rescale
           A__   				=					size_divisor
           A__   				=					resample
           super().__init__(**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			Dict)		->np.ndarray:





           '''simple docstring'''

           A__				,							A__   				=					get_image_size(UpperCAmelCase__)
           # Rounds the height and width down to the closest multiple of size_divisor
           A__   				=					height // size_divisor * size_divisor
           A__   				=					width // size_divisor * size_divisor
           A__   				=					resize(UpperCAmelCase__      ,				(new_h, new_w)      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			float      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			List[str])		->np.ndarray:





           '''simple docstring'''

           return rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[Union[TensorType, str]] = None      ,				UpperCAmelCase__   :			ChannelDimension = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchFeature:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					size_divisor if size_divisor is not None else self.size_divisor
           A__   				=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                  raise ValueError('''size_divisor is required for resizing''')

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError('''Invalid image(s)''')

           # All transformations expect numpy arrays.
           A__   				=					[to_numpy_array(UpperCAmelCase__) for img in images]

           if do_resize:
                  A__   				=					[self.resize(UpperCAmelCase__      ,				size_divisor=UpperCAmelCase__      ,				resample=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__   				=					[self.rescale(UpperCAmelCase__      ,				scale=1 / 255) for image in images]

           A__   				=					[to_channel_dimension_format(UpperCAmelCase__      ,				UpperCAmelCase__) for image in images]

           A__   				=					{'''pixel_values''': images}
           return BatchFeature(data=UpperCAmelCase__      ,				tensor_type=UpperCAmelCase__)


