




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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_      						=   logging.get_logger(__name__)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Union[str, Any]			=    ["pixel_values"]







      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       PILImageResampling = PILImageResampling.BICUBIC       ,				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    :       Union[str, Any]       ,				):
          super().__init__(**snake_case )
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  size if size is not None else {'''height''': 224, '''width''': 224}
          __UpperCamelCase     						=  get_size_dict(snake_case )

          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  resample
          __UpperCamelCase     						=  do_rescale
          __UpperCamelCase     						=  rescale_factor
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __UpperCamelCase     						=  image_std if image_std is not None else IMAGENET_DEFAULT_STD







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  self.size if size is None else size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )

          __UpperCamelCase						,					__UpperCamelCase     						=  get_image_size(snake_case )
          __UpperCamelCase     						=  min(snake_case       ,				snake_case )
          __UpperCamelCase     						=  (size['''height'''] / crop_size['''height''']) * min_dim
          __UpperCamelCase     						=  (size['''width'''] / crop_size['''width''']) * min_dim
          return center_crop(snake_case       ,				size=(cropped_height, cropped_width)       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       PILImageResampling = PIL.Image.BICUBIC       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[str]       ,				):
          __UpperCamelCase     						=  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    :       Union[int, float]       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       Optional[int]       ,				):
          return rescale(snake_case       ,				scale=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Optional[int]       ,				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    :       List[Any]       ,				snake_case    :       ImageInput       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Dict[str, int]] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[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    :       ChannelDimension = ChannelDimension.FIRST       ,				**snake_case    :       Tuple       ,				):
          __UpperCamelCase     						=  do_center_crop if do_center_crop is not None else self.do_center_crop
          __UpperCamelCase     						=  crop_size if crop_size is not None else self.crop_size
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  do_resize if do_resize is not None else self.do_resize
          __UpperCamelCase     						=  size if size is not None else self.size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  resample if resample is not None else self.resample
          __UpperCamelCase     						=  do_rescale if do_rescale is not None else self.do_rescale
          __UpperCamelCase     						=  rescale_factor if rescale_factor is not None else self.rescale_factor
          __UpperCamelCase     						=  do_normalize if do_normalize is not None else self.do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else self.image_mean
          __UpperCamelCase     						=  image_std if image_std is not None else self.image_std

          __UpperCamelCase     						=  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_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.''' )

          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.''' )

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.''' )

          # All transformations expect numpy arrays.
          __UpperCamelCase     						=  [to_numpy_array(snake_case ) for image in images]

          if do_center_crop:
              __UpperCamelCase     						=  [self.center_crop(snake_case       ,				snake_case       ,				size=snake_case ) for image in images]

          if do_resize:
              __UpperCamelCase     						=  [self.resize(image=snake_case       ,				size=snake_case       ,				resample=snake_case ) for image in images]

          if do_rescale:
              __UpperCamelCase     						=  [self.rescale(image=snake_case       ,				scale=snake_case ) for image in images]

          if do_normalize:
              __UpperCamelCase     						=  [self.normalize(image=snake_case       ,				mean=snake_case       ,				std=snake_case ) for image in images]

          __UpperCamelCase     						=  [to_channel_dimension_format(snake_case       ,				snake_case ) for image in images]

          __UpperCamelCase     						=  {'''pixel_values''': images}
          return BatchFeature(data=snake_case       ,				tensor_type=snake_case )












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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import TvltImageProcessor




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=10    ,   lowercase_=10    ,   lowercase_=False    ,   lowercase_=False					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.num_frames					):
        video.append(np.random.randint(2_55    ,   size=(image_processor_tester.num_channels, width, height)    ,   dtype=np.uinta					)					)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase     						=  [Image.fromarray(np.moveaxis(lowercase_    ,   0    ,   -1					)					) for frame in video]

    if torchify:
        __UpperCamelCase     						=  [torch.from_numpy(lowercase_					) for frame in video]

    return video




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=False    ,   lowercase_=False    ,   lowercase_=False					)				->		Tuple:

    """simple docstring"""


    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.batch_size					):
        if equal_resolution:
            __UpperCamelCase     						=  __UpperCamelCase     						=  image_processor_tester.max_resolution
        else:
            __UpperCamelCase						,					__UpperCamelCase     						=  np.random.choice(
                np.arange(image_processor_tester.min_resolution    ,   image_processor_tester.max_resolution					)    ,   2					)
            __UpperCamelCase     						=  prepare_video(
                image_processor_tester=lowercase_    ,   width=lowercase_    ,   height=lowercase_    ,   numpify=lowercase_    ,   torchify=lowercase_    ,   )
        video_inputs.append(lowercase_					)

    return video_inputs

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       int=7       ,				snake_case    :       int=3       ,				snake_case    :       Any=4       ,				snake_case    :       Tuple=18       ,				snake_case    :       Optional[int]=30       ,				snake_case    :       List[Any]=400       ,				snake_case    :       str=True       ,				snake_case    :       str=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       str=[0.5, 0.5, 0.5]       ,				snake_case    :       Optional[int]=[0.5, 0.5, 0.5]       ,				snake_case    :       Any=True       ,				snake_case    :       str=None       ,				):
          __UpperCamelCase     						=  size if size is not None else {'''shortest_edge''': 18}
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  num_frames
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  min_resolution
          __UpperCamelCase     						=  max_resolution
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean
          __UpperCamelCase     						=  image_std
          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size







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







@require_torch
@require_vision
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    TvltImageProcessor if is_vision_available() else None







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  TvltImageProcessorTester(self )







      @property
      def 					snake_case		(   self    :       Dict ):
          return self.image_processor_tester.prepare_image_processor_dict()







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  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       ,				'''do_center_crop''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''size''' ) )







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PIL videos
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				Image.Image )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       List[Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				numpify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				np.ndarray )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				torchify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				torch.Tensor )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)






