from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


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




class 	__snake_case	( snake_case__		):





      '''simple docstring'''






      _snake_case    		=       ['pixel_values']



      def __init__(					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Any = True    ,  _UpperCamelCase     :				List[Any] = 1 / 255    ,  _UpperCamelCase     :				Tuple = True    ,  _UpperCamelCase     :				Any = 8    ,  **_UpperCamelCase     :				Tuple    ,  )				->Tuple:


          """simple docstring"""


          super().__init__(**lowercase_)

          _lowerCamelCase     :		int 						=						do_rescale
          _lowerCamelCase     :		Dict 						=						rescale_factor
          _lowerCamelCase     :		Any 						=						do_pad
          _lowerCamelCase     :		Dict 						=						pad_size



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				List[Any] = None    ,  **_UpperCamelCase     :				Dict)				->Dict:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				List[Any] = None)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						get_image_size(lowercase_)
          _lowerCamelCase     :		Optional[Any] 						=						(old_height // size + 1) * size - old_height
          _lowerCamelCase     :		Any 						=						(old_width // size + 1) * size - old_width

          return pad(lowercase_    ,  ((0, pad_height), (0, pad_width))    ,  mode="""symmetric"""    ,  data_format=lowercase_)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Optional[Any] = None    ,  _UpperCamelCase     :				Union[str, Any] = None    ,  _UpperCamelCase     :				Optional[Any] = None    ,  _UpperCamelCase     :				Tuple = None    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				Optional[Any] = ChannelDimension.FIRST    ,  **_UpperCamelCase     :				Union[str, Any]    ,  )				->int:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						do_rescale if do_rescale is not None else self.do_rescale
          _lowerCamelCase     :		List[str] 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowerCamelCase     :		int 						=						do_pad if do_pad is not None else self.do_pad
          _lowerCamelCase     :		int 						=						pad_size if pad_size is not None else self.pad_size

          _lowerCamelCase     :		Optional[int] 						=						make_list_of_images(lowercase_)

          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.""")

          if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

          # All transformations expect numpy arrays.
          _lowerCamelCase     :		Union[str, Any] 						=						[to_numpy_array(lowercase_) for image in images]

          if do_rescale:
              _lowerCamelCase     :		List[Any] 						=						[self.rescale(image=lowercase_    ,  scale=lowercase_) for image in images]

          if do_pad:
              _lowerCamelCase     :		str 						=						[self.pad(lowercase_    ,  size=lowercase_) for image in images]

          _lowerCamelCase     :		int 						=						[to_channel_dimension_format(lowercase_    ,  lowercase_) for image in images]

          _lowerCamelCase     :		Dict 						=						{"pixel_values": images}
          return BatchFeature(data=lowercase_    ,  tensor_type=lowercase_)





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				NestedDataStructureLike[PathLike]    ,  _UpperCamelCase     :				Optional[NamedSplit] = None    ,  _UpperCamelCase     :				Optional[Features] = None    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				Optional[int] = None    ,  **_UpperCamelCase     :				Tuple    ,  )				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              _UpperCamelCase    ,  split=_UpperCamelCase    ,  features=_UpperCamelCase    ,  cache_dir=_UpperCamelCase    ,  keep_in_memory=_UpperCamelCase    ,  streaming=_UpperCamelCase    ,  num_proc=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[Any] 						=						path_or_paths if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else {self.split: path_or_paths}
          _lowerCamelCase     :		Any 						=						Text(
              cache_dir=_UpperCamelCase    ,  data_files=_UpperCamelCase    ,  features=_UpperCamelCase    ,  **_UpperCamelCase    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          if self.streaming:
              _lowerCamelCase     :		Tuple 						=						self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
              _lowerCamelCase     :		List[Any] 						=						None
              _lowerCamelCase     :		Any 						=						None
              _lowerCamelCase     :		List[str] 						=						None
              _lowerCamelCase     :		Dict 						=						None

              self.builder.download_and_prepare(
                  download_config=_UpperCamelCase    ,  download_mode=_UpperCamelCase    ,  verification_mode=_UpperCamelCase    ,  base_path=_UpperCamelCase    ,  num_proc=self.num_proc    ,  )
              _lowerCamelCase     :		Optional[int] 						=						self.builder.as_dataset(
                  split=self.split    ,  verification_mode=_UpperCamelCase    ,  in_memory=self.keep_in_memory)
          return dataset




