



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     :List[str]			       =logging.get_logger(__name__)






def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]				)       ->							List[List[ImageInput]]:

							'''simple docstring'''






							if isinstance(__A			,	(list, tuple)				) and isinstance(videos[0]			,	(list, tuple)				) and is_valid_image(videos[0][0]				):
														return videos

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

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

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



class        lowerCAmelCase__					(  __A       ):
				A_   :     List[Any]								=     ['pixel_values']


				def __init__(							self      :  Dict						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  PILImageResampling = PILImageResampling.BILINEAR						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Union[int, float] = 1 / 255						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, **__UpperCamelCase      :  List[Any]						, )		->     None:
											super().__init__(**__UpperCamelCase       )
											A      						=    size if size is not None else {'''shortest_edge''': 256}
											A      						=    get_size_dict(__UpperCamelCase						, default_to_square=__UpperCamelCase       )
											A      						=    crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											A      						=    get_size_dict(__UpperCamelCase						, 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 __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  np.ndarray						, __UpperCamelCase      :  Dict[str, int]						, __UpperCamelCase      :  PILImageResampling = PILImageResampling.BILINEAR						, __UpperCamelCase      :  Optional[Union[str, ChannelDimension]] = None						, **__UpperCamelCase      :  Optional[int]						, )		->     np.ndarray:
											A      						=    get_size_dict(__UpperCamelCase						, default_to_square=__UpperCamelCase       )
											if "shortest_edge" in size:
																		A      						=    get_resize_output_image_size(__UpperCamelCase						, size['shortest_edge']						, default_to_square=__UpperCamelCase       )
											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(__UpperCamelCase						, size=__UpperCamelCase						, resample=__UpperCamelCase						, data_format=__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  np.ndarray						, __UpperCamelCase      :  Dict[str, int]						, __UpperCamelCase      :  Optional[Union[str, ChannelDimension]] = None						, **__UpperCamelCase      :  List[Any]						, )		->     np.ndarray:
											A      						=    get_size_dict(__UpperCamelCase       )
											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(__UpperCamelCase						, size=(size['height'], size['width'])						, data_format=__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  np.ndarray						, __UpperCamelCase      :  Union[int, float]						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Optional[Union[str, ChannelDimension]] = None						, **__UpperCamelCase      :  Any						, )		->     str:
											A      						=    image.astype(np.floataa       )
											if offset:
																		A      						=    image - (scale / 2)
											return rescale(__UpperCamelCase						, scale=__UpperCamelCase						, data_format=__UpperCamelCase						, **__UpperCamelCase       )


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


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  ImageInput						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  PILImageResampling = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  float = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[ChannelDimension] = ChannelDimension.FIRST						, )		->     np.ndarray:

											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(__UpperCamelCase       )

											if do_resize:
																		A      						=    self.resize(image=__UpperCamelCase						, size=__UpperCamelCase						, resample=__UpperCamelCase       )

											if do_center_crop:
																		A      						=    self.center_crop(__UpperCamelCase						, size=__UpperCamelCase       )

											if do_rescale:
																		A      						=    self.rescale(image=__UpperCamelCase						, scale=__UpperCamelCase						, offset=__UpperCamelCase       )

											if do_normalize:
																		A      						=    self.normalize(image=__UpperCamelCase						, mean=__UpperCamelCase						, std=__UpperCamelCase       )

											A      						=    to_channel_dimension_format(__UpperCamelCase						, __UpperCamelCase       )
											return image


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  ImageInput						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  PILImageResampling = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  float = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[Union[str, TensorType]] = None						, __UpperCamelCase      :  ChannelDimension = ChannelDimension.FIRST						, **__UpperCamelCase      :  List[Any]						, )		->     PIL.Image.Image:
											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(__UpperCamelCase						, default_to_square=__UpperCamelCase       )
											A      						=    crop_size if crop_size is not None else self.crop_size
											A      						=    get_size_dict(__UpperCamelCase						, param_name='crop_size'       )

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

											A      						=    make_batched(__UpperCamelCase       )

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

											A      						=    {'''pixel_values''': videos}
											return BatchFeature(data=__UpperCamelCase						, tensor_type=__UpperCamelCase       )







'''simple docstring'''



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import torch
class UpperCAmelCase_       (     TensorFormatter[Mapping, '''torch.Tensor''', Mapping]					):




      """simple docstring"""




      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		str=None  ,   **UpperCAmelCase      :		List[Any]		)	->	int:


       '''simple docstring'''

       super().__init__(features=UpperCAmelCase		)
       lowercase       : List[Any]						=torch_tensor_kwargs
       import torch  # noqa import torch at initialization






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       import torch

       if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) and column:
        if all(
            isinstance(UpperCAmelCase  ,   torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
            for x in column		):
         return torch.stack(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Optional[Any]:


       '''simple docstring'''

       import torch

       if isinstance(UpperCAmelCase  ,   (str, bytes, type(UpperCAmelCase		))		):
        return value
       elif isinstance(UpperCAmelCase  ,   (np.character, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.character		):
        return value.tolist()

       lowercase       : Any						={}

       if isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.integer		):
        lowercase       : Tuple						={'''dtype''': torch.intaa}
       elif isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.floating		):
        lowercase       : int						={'''dtype''': torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(UpperCAmelCase  ,   PIL.Image.Image		):
         lowercase       : List[Any]						=np.asarray(UpperCAmelCase		)
       return torch.tensor(UpperCAmelCase  ,   **{**default_dtype, **self.torch_tensor_kwargs}		)






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       import torch

       # support for torch, tf, jax etc.
       if hasattr(UpperCAmelCase  ,   '''__array__'''		) and not isinstance(UpperCAmelCase  ,   torch.Tensor		):
        lowercase       : List[Any]						=data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(UpperCAmelCase  ,   np.ndarray		):
        if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
         return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       elif isinstance(UpperCAmelCase  ,   (list, tuple)		):
        return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       return self._tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		dict		)	->	Any:


       '''simple docstring'''

       return map_nested(self._recursive_tensorize  ,   UpperCAmelCase  ,   map_list=UpperCAmelCase		)






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : List[str]						=self.numpy_arrow_extractor().extract_row(UpperCAmelCase		)
       lowercase       : Any						=self.python_features_decoder.decode_row(UpperCAmelCase		)
       return self.recursive_tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		pa.Table		)	->	"torch.Tensor":


       '''simple docstring'''

       lowercase       : int						=self.numpy_arrow_extractor().extract_column(UpperCAmelCase		)
       lowercase       : Tuple						=self.python_features_decoder.decode_column(UpperCAmelCase  ,   pa_table.column_names[0]		)
       lowercase       : Optional[Any]						=self.recursive_tensorize(UpperCAmelCase		)
       lowercase       : Any						=self._consolidate(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : Tuple						=self.numpy_arrow_extractor().extract_batch(UpperCAmelCase		)
       lowercase       : List[str]						=self.python_features_decoder.decode_batch(UpperCAmelCase		)
       lowercase       : Dict						=self.recursive_tensorize(UpperCAmelCase		)
       for column_name in batch:
        lowercase       : str						=self._consolidate(batch[column_name]		)
       return batch



