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

UpperCAmelCase_			    =							logging.get_logger(__name__)




def       lowerCAmelCase_     (		lowercase:							List[Any]		)    ->				List[List[ImageInput]]:







 '''simple docstring'''
 if isinstance(lowercase  ,					(list, tuple)		) and isinstance(videos[0]  ,					(list, tuple)		) and is_valid_image(videos[0][0]		):
  return videos

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

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

 raise ValueError(F"""Could not make batched video from {videos}"""		)





class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    int  			=   ['''pixel_values''']



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



    """simple docstring"""



    super().__init__(**_lowercase							)
    _UpperCamelCase:     Optional[int]							= size if size is not None else {'''shortest_edge''': 256}
    _UpperCamelCase:     int							= get_size_dict(_lowercase		,  default_to_square=_lowercase							)
    _UpperCamelCase:     Any							= crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
    _UpperCamelCase:     List[Any]							= get_size_dict(_lowercase		,  param_name='''crop_size'''							)

    _UpperCamelCase:     List[Any]							= do_resize
    _UpperCamelCase:     Optional[int]							= size
    _UpperCamelCase:     List[str]							= do_center_crop
    _UpperCamelCase:     Tuple							= crop_size
    _UpperCamelCase:     Optional[int]							= resample
    _UpperCamelCase:     Optional[int]							= do_rescale
    _UpperCamelCase:     List[Any]							= rescale_factor
    _UpperCamelCase:     Optional[int]							= offset
    _UpperCamelCase:     Optional[Any]							= do_normalize
    _UpperCamelCase:     List[Any]							= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _UpperCamelCase:     int							= image_std if image_std is not None else IMAGENET_STANDARD_STD



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  np.ndarray		,  _lowercase					:  Dict[str, int]		,  _lowercase					:  PILImageResampling = PILImageResampling.BILINEAR		,  _lowercase					:  Optional[Union[str, ChannelDimension]] = None		,  **_lowercase					:  str		,  ):



    """simple docstring"""



    _UpperCamelCase:     str							= get_size_dict(_lowercase		,  default_to_square=_lowercase							)
    if "shortest_edge" in size:
     _UpperCamelCase:     Optional[Any]							= get_resize_output_image_size(_lowercase		,  size['''shortest_edge''']		,  default_to_square=_lowercase							)
    elif "height" in size and "width" in size:
     _UpperCamelCase:     Union[str, Any]							= (size['''height'''], size['''width'''])
    else:
     raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""							)
    return resize(_lowercase		,  size=_lowercase		,  resample=_lowercase		,  data_format=_lowercase		,  **_lowercase							)



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



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= get_size_dict(_lowercase							)
    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(_lowercase		,  size=(size['''height'''], size['''width'''])		,  data_format=_lowercase		,  **_lowercase							)



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



    """simple docstring"""



    _UpperCamelCase:     List[str]							= image.astype(np.floataa							)
    if offset:
     _UpperCamelCase:     List[Any]							= image - (scale / 2)
    return rescale(_lowercase		,  scale=_lowercase		,  data_format=_lowercase		,  **_lowercase							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  np.ndarray		,  _lowercase					:  Union[float, List[float]]		,  _lowercase					:  Union[float, List[float]]		,  _lowercase					:  Optional[Union[str, ChannelDimension]] = None		,  **_lowercase					:  Optional[int]		,  ):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  ImageInput		,  _lowercase					:  bool = None		,  _lowercase					:  Dict[str, int] = None		,  _lowercase					:  PILImageResampling = None		,  _lowercase					:  bool = None		,  _lowercase					:  Dict[str, int] = None		,  _lowercase					:  bool = None		,  _lowercase					:  float = None		,  _lowercase					:  bool = None		,  _lowercase					:  bool = None		,  _lowercase					:  Optional[Union[float, List[float]]] = None		,  _lowercase					:  Optional[Union[float, List[float]]] = None		,  _lowercase					:  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.
    _UpperCamelCase:     List[str]							= to_numpy_array(_lowercase							)

    if do_resize:
     _UpperCamelCase:     Union[str, Any]							= self.resize(image=_lowercase		,  size=_lowercase		,  resample=_lowercase							)

    if do_center_crop:
     _UpperCamelCase:     int							= self.center_crop(_lowercase		,  size=_lowercase							)

    if do_rescale:
     _UpperCamelCase:     List[str]							= self.rescale(image=_lowercase		,  scale=_lowercase		,  offset=_lowercase							)

    if do_normalize:
     _UpperCamelCase:     List[str]							= self.normalize(image=_lowercase		,  mean=_lowercase		,  std=_lowercase							)

    _UpperCamelCase:     Optional[Any]							= to_channel_dimension_format(_lowercase		,  _lowercase							)
    return image



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



    """simple docstring"""



    _UpperCamelCase:     Any							= do_resize if do_resize is not None else self.do_resize
    _UpperCamelCase:     Any							= resample if resample is not None else self.resample
    _UpperCamelCase:     str							= do_center_crop if do_center_crop is not None else self.do_center_crop
    _UpperCamelCase:     Union[str, Any]							= do_rescale if do_rescale is not None else self.do_rescale
    _UpperCamelCase:     Any							= rescale_factor if rescale_factor is not None else self.rescale_factor
    _UpperCamelCase:     List[str]							= offset if offset is not None else self.offset
    _UpperCamelCase:     Tuple							= do_normalize if do_normalize is not None else self.do_normalize
    _UpperCamelCase:     Optional[Any]							= image_mean if image_mean is not None else self.image_mean
    _UpperCamelCase:     int							= image_std if image_std is not None else self.image_std

    _UpperCamelCase:     List[str]							= size if size is not None else self.size
    _UpperCamelCase:     str							= get_size_dict(_lowercase		,  default_to_square=_lowercase							)
    _UpperCamelCase:     str							= crop_size if crop_size is not None else self.crop_size
    _UpperCamelCase:     Tuple							= get_size_dict(_lowercase		,  param_name='''crop_size'''							)

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

    _UpperCamelCase:     str							= make_batched(_lowercase							)

    _UpperCamelCase:     Optional[Any]							= [
        [
            self._preprocess_image(
                image=_lowercase		,  do_resize=_lowercase		,  size=_lowercase		,  resample=_lowercase		,  do_center_crop=_lowercase		,  crop_size=_lowercase		,  do_rescale=_lowercase		,  rescale_factor=_lowercase		,  offset=_lowercase		,  do_normalize=_lowercase		,  image_mean=_lowercase		,  image_std=_lowercase		,  data_format=_lowercase		,  )
            for img in video
        ]
        for video in videos
    ]

    _UpperCamelCase:     Any							= {'''pixel_values''': videos}
    return BatchFeature(data=_lowercase		,  tensor_type=_lowercase							)
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    '''split_dict'''  ,					[
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42  ,					dataset_name='''my_dataset'''		)}		),
        SplitDict({'''train''': SplitInfo(name='''train'''  ,					num_bytes=1_337  ,					num_examples=42		)}		),
        SplitDict({'''train''': SplitInfo()}		),
    ]  ,					)
def       lowerCAmelCase_     (		lowercase:							SplitDict		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= split_dict._to_yaml_list()
 assert len(lowercase		) == len(lowercase		)
 _UpperCamelCase:     Tuple							= SplitDict._from_yaml_list(lowercase		)
 for split_name, split_info in split_dict.items():
  # dataset_name field is deprecated, and is therefore not part of the YAML dump
  _UpperCamelCase:     Optional[Any]							= None
  # the split name of split_dict takes over the name of the split info object
  _UpperCamelCase:     Any							= split_name
 assert split_dict == reloaded





@pytest.mark.parametrize(
    '''split_info'''  ,					[SplitInfo(), SplitInfo(dataset_name=lowercase		), SplitInfo(dataset_name='''my_dataset'''		)]		)
def       lowerCAmelCase_     (		lowercase:							str		)    ->				Tuple:







 '''simple docstring'''
 # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
 # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
 _UpperCamelCase:     Optional[Any]							= asdict(SplitDict({'''train''': split_info}		)		)
 assert "dataset_name" in split_dict_asdict["train"]
 assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name