



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
     import PIL

__snake_case		:str   	=  logging.get_logger(__name__)





def   __snake_case			(    _UpperCAmelCase       ):
    if isinstance(__snake_case     ,				(list, tuple)       ) and isinstance(videos[0]     ,				(list, tuple)       ) and is_valid_image(videos[0][0]       ):
        return videos

    elif isinstance(__snake_case     ,				(list, tuple)       ) and is_valid_image(videos[0]       ):
        return [videos]

    elif is_valid_image(__snake_case       ):
        return [[videos]]

    raise ValueError(f'Could not make batched video from {videos}'       )

class 					_A (			__lowercase					):
       UpperCamelCase__						:					List[str]		      =		['''pixel_values''']



       def __init__(	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  PILImageResampling = PILImageResampling.BILINEAR  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Union[int, float] = 1 / 255  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[float, List[float]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[float, List[float]]] = None  ,	**__SCREAMING_SNAKE_CASE							:  List[str]  ,	):


           '''simple docstring'''

           super().__init__(**_A)
           __a		 =       size if size is not None else {'''shortest_edge''': 256}
           __a		 =       get_size_dict(_A  ,	default_to_square=_A)
           __a		 =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           __a		 =       get_size_dict(_A  ,	param_name='''crop_size''')

           __a		 =       do_resize
           __a		 =       size
           __a		 =       do_center_crop
           __a		 =       crop_size
           __a		 =       resample
           __a		 =       do_rescale
           __a		 =       rescale_factor
           __a		 =       offset
           __a		 =       do_normalize
           __a		 =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           __a		 =       image_std if image_std is not None else IMAGENET_STANDARD_STD



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int]  ,	__SCREAMING_SNAKE_CASE							:  PILImageResampling = PILImageResampling.BILINEAR  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


           '''simple docstring'''

           __a		 =       get_size_dict(_A  ,	default_to_square=_A)
           if "shortest_edge" in size:
               __a		 =       get_resize_output_image_size(_A  ,	size['''shortest_edge''']  ,	default_to_square=_A)
           elif "height" in size and "width" in size:
               __a		 =       (size['''height'''], size['''width'''])
           else:
               raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}')
           return resize(_A  ,	size=_A  ,	resample=_A  ,	data_format=_A  ,	**_A)



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  Any  ,	):


           '''simple docstring'''

           __a		 =       get_size_dict(_A)
           if "height" not in size or "width" not in size:
               raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}')
           return center_crop(_A  ,	size=(size['''height'''], size['''width'''])  ,	data_format=_A  ,	**_A)



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Union[int, float]  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


           '''simple docstring'''

           __a		 =       image.astype(np.floataa)
           if offset:
               __a		 =       image - (scale / 2)
           return rescale(_A  ,	scale=_A  ,	data_format=_A  ,	**_A)



       def 	_lowerCamelCase    (	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  np.ndarray  ,	__SCREAMING_SNAKE_CASE							:  Union[float, List[float]]  ,	__SCREAMING_SNAKE_CASE							:  Union[float, List[float]]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, ChannelDimension]] = None  ,	**__SCREAMING_SNAKE_CASE							:  str  ,	):


           '''simple docstring'''

           return normalize(_A  ,	mean=_A  ,	std=_A  ,	data_format=_A  ,	**_A)



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  ImageInput  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  PILImageResampling = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  float = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[float, List[float]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[float, List[float]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[ChannelDimension] = ChannelDimension.FIRST  ,	):


           '''simple docstring'''

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

           if do_center_crop and crop_size is None:
               raise ValueError('''Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.''')

           if offset and not do_rescale:
               raise ValueError('''For offset, do_rescale must also be set to True.''')

           # All transformations expect numpy arrays.
           __a		 =       to_numpy_array(_A)

           if do_resize:
               __a		 =       self.resize(image=_A  ,	size=_A  ,	resample=_A)

           if do_center_crop:
               __a		 =       self.center_crop(_A  ,	size=_A)

           if do_rescale:
               __a		 =       self.rescale(image=_A  ,	scale=_A  ,	offset=_A)

           if do_normalize:
               __a		 =       self.normalize(image=_A  ,	mean=_A  ,	std=_A)

           __a		 =       to_channel_dimension_format(_A  ,	_A)
           return image





       def 	_lowerCamelCase    (	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  ImageInput  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  PILImageResampling = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Dict[str, int] = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  float = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  bool = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[float, List[float]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[float, List[float]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[str, TensorType]] = None  ,	__SCREAMING_SNAKE_CASE							:  ChannelDimension = ChannelDimension.FIRST  ,	**__SCREAMING_SNAKE_CASE							:  Dict  ,	):


           '''simple docstring'''

           __a		 =       do_resize if do_resize is not None else self.do_resize
           __a		 =       resample if resample is not None else self.resample
           __a		 =       do_center_crop if do_center_crop is not None else self.do_center_crop
           __a		 =       do_rescale if do_rescale is not None else self.do_rescale
           __a		 =       rescale_factor if rescale_factor is not None else self.rescale_factor
           __a		 =       offset if offset is not None else self.offset
           __a		 =       do_normalize if do_normalize is not None else self.do_normalize
           __a		 =       image_mean if image_mean is not None else self.image_mean
           __a		 =       image_std if image_std is not None else self.image_std

           __a		 =       size if size is not None else self.size
           __a		 =       get_size_dict(_A  ,	default_to_square=_A)
           __a		 =       crop_size if crop_size is not None else self.crop_size
           __a		 =       get_size_dict(_A  ,	param_name='''crop_size''')

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

           __a		 =       make_batched(_A)

           __a		 =       [
               [
                   self._preprocess_image(
                       image=_A  ,	do_resize=_A  ,	size=_A  ,	resample=_A  ,	do_center_crop=_A  ,	crop_size=_A  ,	do_rescale=_A  ,	rescale_factor=_A  ,	offset=_A  ,	do_normalize=_A  ,	image_mean=_A  ,	image_std=_A  ,	data_format=_A  ,	)
                   for img in video
               ]
               for video in videos
           ]

           __a		 =       {'''pixel_values''': videos}
           return BatchFeature(data=_A  ,	tensor_type=_A)










from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class 					_A (			__UpperCAmelCase					):



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
           return Dataset.from_dict(__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(dset.column_names  ,	['''col_1''', '''col_2'''])
           for i, r in enumerate(__SCREAMING_SNAKE_CASE):
               self.assertDictEqual(__SCREAMING_SNAKE_CASE  ,	example_records[i])



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self._create_example_records()
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           __a		 =       Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
           self.assertEqual(dset.info  ,	dset_from_dict.info)



       def 	_lowerCamelCase    (	self							:  int):  # checks what happens with missing columns


           '''simple docstring'''

           __a		 =       [{'''col_1''': 1}, {'''col_2''': '''x'''}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertDictEqual(dset[0]  ,	{'''col_1''': 1})
           self.assertDictEqual(dset[1]  ,	{'''col_1''': None})  # NB: first record is used for columns



       def 	_lowerCamelCase    (	self							:  Optional[Any]):  # checks if the type can be inferred from the second record


           '''simple docstring'''

           __a		 =       [{'''col_1''': []}, {'''col_1''': [1, 2]}]
           __a		 =       Dataset.from_list(__SCREAMING_SNAKE_CASE)
           self.assertEqual(dset.info.features['''col_1''']  ,	Sequence(Value('''int64''')))





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       Dataset.from_list([])
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	0)
           self.assertListEqual(dset.column_names  ,	[])





