




'''simple docstring'''


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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


UpperCamelCase       			= logging.get_logger(__name__)






class 		lowerCAmelCase_     (    UpperCAmelCase_				):







  '''simple docstring'''

  UpperCamelCase_       :	Optional[Any]				       =							["""pixel_values"""]



  def __init__(      self							:				str ,   SCREAMING_SNAKE_CASE_							:				bool = True ,   SCREAMING_SNAKE_CASE_							:				Dict[str, int] = None ,   SCREAMING_SNAKE_CASE_							:				PILImageResampling = PIL.Image.BICUBIC ,   SCREAMING_SNAKE_CASE_							:				bool = True ,   SCREAMING_SNAKE_CASE_							:				Dict[str, int] = None ,   SCREAMING_SNAKE_CASE_							:				Union[int, float] = 1 / 2_55 ,   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_							:				Optional[int] ,   )       ->						Tuple:







       '''simple docstring'''



       super().__init__(**_lowerCAmelCase     )
       A:						int						=						size if size is not None else {"height": 2_56, "width": 2_56}
       A:						Union[str, Any]						=						get_size_dict(_lowerCAmelCase     )
       A:						Tuple						=						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       A:						List[Any]						=						get_size_dict(_lowerCAmelCase ,   param_name='''crop_size'''     )

       A:						Dict						=						do_resize
       A:						int						=						size
       A:						Any						=						resample
       A:						Optional[Any]						=						do_center_crop
       A:						Any						=						crop_size
       A:						List[Any]						=						do_rescale
       A:						int						=						rescale_factor
       A:						List[Any]						=						do_normalize
       A:						Union[str, Any]						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A:						Optional[Any]						=						image_std if image_std is not None else IMAGENET_STANDARD_STD



  def  _snake_case			(      self							:				Any ,   SCREAMING_SNAKE_CASE_							:				np.ndarray ,   SCREAMING_SNAKE_CASE_							:				Dict[str, int] ,   SCREAMING_SNAKE_CASE_							:				PILImageResampling = PIL.Image.BICUBIC ,   SCREAMING_SNAKE_CASE_							:				Optional[Union[str, ChannelDimension]] = None ,   **SCREAMING_SNAKE_CASE_							:				List[Any] ,   )       ->						Dict:







       '''simple docstring'''



       A:						Any						=						get_size_dict(_lowerCAmelCase     )
       if "height" not in size or "width" not in size:
            raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
       return resize(
           _lowerCAmelCase ,   size=(size['''height'''], size['''width''']) ,   resample=_lowerCAmelCase ,   data_format=_lowerCAmelCase ,   **_lowerCAmelCase     )



  def  _snake_case			(      self							:				int ,   SCREAMING_SNAKE_CASE_							:				np.ndarray ,   SCREAMING_SNAKE_CASE_							:				Dict[str, int] ,   SCREAMING_SNAKE_CASE_							:				Optional[Union[str, ChannelDimension]] = None ,   **SCREAMING_SNAKE_CASE_							:				Tuple ,   )       ->						List[Any]:







       '''simple docstring'''



       A:						Dict						=						get_size_dict(_lowerCAmelCase     )
       if "height" not in size or "width" not in size:
            raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
       return center_crop(_lowerCAmelCase ,   size=(size['''height'''], size['''width''']) ,   data_format=_lowerCAmelCase ,   **_lowerCAmelCase     )



  def  _snake_case			(      self							:				Optional[Any] ,   SCREAMING_SNAKE_CASE_							:				np.ndarray ,   SCREAMING_SNAKE_CASE_							:				Union[int, float] ,   SCREAMING_SNAKE_CASE_							:				Optional[Union[str, ChannelDimension]] = None ,   **SCREAMING_SNAKE_CASE_							:				Dict ,   )       ->						Dict:







       '''simple docstring'''



       return rescale(_lowerCAmelCase ,   scale=_lowerCAmelCase ,   data_format=_lowerCAmelCase ,   **_lowerCAmelCase     )



  def  _snake_case			(      self							:				List[str] ,   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_							:				List[Any] ,   )       ->						List[Any]:







       '''simple docstring'''



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




  def  _snake_case			(      self							:				List[Any] ,   SCREAMING_SNAKE_CASE_							:				ImageInput ,   SCREAMING_SNAKE_CASE_							:				bool = None ,   SCREAMING_SNAKE_CASE_							:				Dict[str, int] = None ,   SCREAMING_SNAKE_CASE_							:				List[str]=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_							:				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_							:				Optional[Any] ,   )       ->						str:







       '''simple docstring'''



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

       A:						List[str]						=						size if size is not None else self.size
       A:						str						=						get_size_dict(_lowerCAmelCase     )
       A:						int						=						crop_size if crop_size is not None else self.crop_size
       A:						int						=						get_size_dict(_lowerCAmelCase ,   param_name='''crop_size'''     )

       A:						Optional[Any]						=						make_list_of_images(_lowerCAmelCase     )

       if not valid_images(_lowerCAmelCase     ):
            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 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.'''     )

       # All transformations expect numpy arrays.
       A:						Tuple						=						[to_numpy_array(_lowerCAmelCase     ) for image in images]

       if do_resize:
            A:						Optional[Any]						=						[self.resize(image=_lowerCAmelCase ,   size=_lowerCAmelCase ,   resample=_lowerCAmelCase     ) for image in images]

       if do_center_crop:
            A:						Optional[int]						=						[self.center_crop(image=_lowerCAmelCase ,   size=_lowerCAmelCase     ) for image in images]

       if do_rescale:
            A:						Optional[Any]						=						[self.rescale(image=_lowerCAmelCase ,   scale=_lowerCAmelCase     ) for image in images]

       if do_normalize:
            A:						Tuple						=						[self.normalize(image=_lowerCAmelCase ,   mean=_lowerCAmelCase ,   std=_lowerCAmelCase     ) for image in images]

       A:						int						=						[to_channel_dimension_format(_lowerCAmelCase ,   _lowerCAmelCase     ) for image in images]

       A:						List[str]						=						{"pixel_values": images}
       return BatchFeature(data=_lowerCAmelCase ,   tensor_type=_lowerCAmelCase     )












from typing import TYPE_CHECKING

from ....utils import _LazyModule


_UpperCAmelCase		:  Dict	=   {"tokenization_tapex": ["TapexTokenizer"]}


if TYPE_CHECKING:
     from .tokenization_tapex import TapexTokenizer


else:
     import sys

     _UpperCAmelCase		:  Optional[Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure)



