
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 ViTImageProcessor


class   __lowerCAmelCase		(		unittest.TestCase ):



      """simple docstring"""







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




      def 						snake_case_     ( self						:    int			):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }






@require_torch
@require_vision
class   __lowerCAmelCase		(		lowerCAmelCase_	,	unittest.TestCase ):



      """simple docstring"""







      A__		:     int       				=      ViTImageProcessor if is_vision_available() else None




      def 						snake_case_     ( self						:    Tuple			):
          __lowercase						:     Union[str, Any]		      =    ViTImageProcessingTester(self			)




      @property
      def 						snake_case_     ( self						:    str			):
          return self.image_processor_tester.prepare_image_processor_dict()




      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     int		      =    self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(_snake_case			,    '''image_mean'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''image_std'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_normalize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''do_resize'''			)			)
          self.assertTrue(hasattr(_snake_case			,    '''size'''			)			)




      def 						snake_case_     ( self						:    List[str]			):
          __lowercase						:     int		      =    self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size			,    {'''height''': 18, '''width''': 18}			)

          __lowercase						:     str		      =    self.image_processing_class.from_dict(self.image_processor_dict			,    size=42			)
          self.assertEqual(image_processor.size			,    {'''height''': 42, '''width''': 42}			)




      def 						snake_case_     ( self						:    Tuple			):
          pass




      def 						snake_case_     ( self						:    int			):
          # Initialize image_processing
          __lowercase						:     Dict		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          __lowercase						:     Any		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    Image.Image			)

          # Test not batched input
          __lowercase						:     int		      =    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )

          # Test batched
          __lowercase						:     List[str]		      =    image_processing(_snake_case			,    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )




      def 						snake_case_     ( self						:    Optional[Any]			):
          # Initialize image_processing
          __lowercase						:     List[str]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          __lowercase						:     int		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    numpify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    np.ndarray			)

          # Test not batched input
          __lowercase						:     Tuple		      =    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )

          # Test batched
          __lowercase						:     Optional[int]		      =    image_processing(_snake_case			,    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )




      def 						snake_case_     ( self						:    List[Any]			):
          # Initialize image_processing
          __lowercase						:     List[Any]		      =    self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          __lowercase						:     int		      =    prepare_image_inputs(self.image_processor_tester			,    equal_resolution=_snake_case			,    torchify=_snake_case			)
          for image in image_inputs:
              self.assertIsInstance(_snake_case			,    torch.Tensor			)

          # Test not batched input
          __lowercase						:     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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )

          # Test batched
          __lowercase						:     Optional[Any]		      =    image_processing(_snake_case			,    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.size['''height'''],
                  self.image_processor_tester.size['''width'''],
              )			,    )




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase   :     Dict      =						logging.get_logger(__name__)


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''pixel_values''']




      def __init__( self						:    int			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Dict[str, int]] = None			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    bool = True			,    _snake_case						:    Union[int, float] = 1 / 255			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    **_snake_case						:    List[str]			,    ):
          super().__init__(**_snake_case			)
          __lowercase						:     Tuple		      =    size if size is not None else {'''height''': 224, '''width''': 224}
          __lowercase						:     List[str]		      =    get_size_dict(_snake_case			)
          __lowercase						:     Union[str, Any]		      =    do_resize
          __lowercase						:     int		      =    do_rescale
          __lowercase						:     List[Any]		      =    do_normalize
          __lowercase						:     Dict		      =    size
          __lowercase						:     Any		      =    resample
          __lowercase						:     int		      =    rescale_factor
          __lowercase						:     Optional[Any]		      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __lowercase						:     Optional[Any]		      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def 						snake_case_     ( self						:    int			,    _snake_case						:    np.ndarray			,    _snake_case						:    Dict[str, int]			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Optional[int]			,    ):
          __lowercase						:     List[Any]		      =    get_size_dict(_snake_case			)
          if "height" not in size or "width" not in size:
              raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'			)
          return resize(
              _snake_case			,    size=(size['''height'''], size['''width'''])			,    resample=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Union[str, Any]			,    _snake_case						:    np.ndarray			,    _snake_case						:    float			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Dict			):
          return rescale(_snake_case			,    scale=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Any			,    _snake_case						:    np.ndarray			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Any			,    ):
          return normalize(_snake_case			,    mean=_snake_case			,    std=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Dict			,    _snake_case						:    ImageInput			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Dict[str, int] = None			,    _snake_case						:    PILImageResampling = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[float] = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[str, TensorType]] = None			,    _snake_case						:    Union[str, ChannelDimension] = ChannelDimension.FIRST			,    **_snake_case						:    str			,    ):
          __lowercase						:     List[Any]		      =    do_resize if do_resize is not None else self.do_resize
          __lowercase						:     Any		      =    do_rescale if do_rescale is not None else self.do_rescale
          __lowercase						:     Optional[Any]		      =    do_normalize if do_normalize is not None else self.do_normalize
          __lowercase						:     Optional[Any]		      =    resample if resample is not None else self.resample
          __lowercase						:     Tuple		      =    rescale_factor if rescale_factor is not None else self.rescale_factor
          __lowercase						:     Dict		      =    image_mean if image_mean is not None else self.image_mean
          __lowercase						:     Any		      =    image_std if image_std is not None else self.image_std

          __lowercase						:     Optional[int]		      =    size if size is not None else self.size
          __lowercase						:     Tuple		      =    get_size_dict(_snake_case			)

          __lowercase						:     Optional[int]		      =    make_list_of_images(_snake_case			)

          if not valid_images(_snake_case			):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

          if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize is True.'''			)

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

          # All transformations expect numpy arrays.
          __lowercase						:     Optional[Any]		      =    [to_numpy_array(_snake_case			) for image in images]

          if do_resize:
              __lowercase						:     List[Any]		      =    [self.resize(image=_snake_case			,    size=_snake_case			,    resample=_snake_case			) for image in images]

          if do_rescale:
              __lowercase						:     Tuple		      =    [self.rescale(image=_snake_case			,    scale=_snake_case			) for image in images]

          if do_normalize:
              __lowercase						:     Union[str, Any]		      =    [self.normalize(image=_snake_case			,    mean=_snake_case			,    std=_snake_case			) for image in images]

          __lowercase						:     Tuple		      =    [to_channel_dimension_format(_snake_case			,    _snake_case			) for image in images]

          __lowercase						:     List[str]		      =    {'''pixel_values''': images}
          return BatchFeature(data=_snake_case			,    tensor_type=_snake_case			)


