





'''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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__SCREAMING_SNAKE_CASE     :Optional[int]       =				logging.get_logger(__name__)







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Tuple           =							["""pixel_values"""]




 def __init__(					self       :    List[str]	,			snake_case_       :    bool = True	,			snake_case_       :    Dict[str, int] = None	,			snake_case_       :    float = None	,			snake_case_       :    PILImageResampling = PILImageResampling.BILINEAR	,			snake_case_       :    bool = True	,			snake_case_       :    Union[int, float] = 1 / 2_5_5	,			snake_case_       :    bool = True	,			snake_case_       :    Optional[Union[float, List[float]]] = None	,			snake_case_       :    Optional[Union[float, List[float]]] = None	,			**snake_case_       :    Tuple	,			):
      super().__init__(**snake_case_    )
      _UpperCAmelCase     							=   size if size is not None else {"shortest_edge": 3_8_4}
      _UpperCAmelCase     							=   get_size_dict(snake_case_	,			default_to_square=snake_case_    )

      _UpperCAmelCase     							=   do_resize
      _UpperCAmelCase     							=   size
      # Default value set here for backwards compatibility where the value in config is None
      _UpperCAmelCase     							=   crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
      _UpperCAmelCase     							=   resample
      _UpperCAmelCase     							=   do_rescale
      _UpperCAmelCase     							=   rescale_factor
      _UpperCAmelCase     							=   do_normalize
      _UpperCAmelCase     							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _UpperCAmelCase     							=   image_std if image_std is not None else IMAGENET_STANDARD_STD




 def      lowercase	(					self       :    Any	,			snake_case_       :    np.ndarray	,			snake_case_       :    Dict[str, int]	,			snake_case_       :    float	,			snake_case_       :    PILImageResampling = PILImageResampling.BICUBIC	,			snake_case_       :    Optional[Union[str, ChannelDimension]] = None	,			**snake_case_       :    Optional[int]	,			):
      _UpperCAmelCase     							=   get_size_dict(snake_case_	,			default_to_square=snake_case_    )
      if "shortest_edge" not in size:
           raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
      _UpperCAmelCase     							=   size["shortest_edge"]

      if shortest_edge < 3_8_4:
           # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
           _UpperCAmelCase     							=   int(shortest_edge / crop_pct    )
           _UpperCAmelCase     							=   get_resize_output_image_size(snake_case_	,			size=snake_case_	,			default_to_square=snake_case_    )
           _UpperCAmelCase     							=   resize(image=snake_case_	,			size=snake_case_	,			resample=snake_case_	,			data_format=snake_case_	,			**snake_case_    )
           # then crop to (shortest_edge, shortest_edge)
           return center_crop(image=snake_case_	,			size=(shortest_edge, shortest_edge)	,			data_format=snake_case_	,			**snake_case_    )
      else:
           # warping (no cropping) when evaluated at 384 or larger
           return resize(
               snake_case_	,			size=(shortest_edge, shortest_edge)	,			resample=snake_case_	,			data_format=snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    np.ndarray	,			snake_case_       :    Union[int, float]	,			snake_case_       :    Optional[Union[str, ChannelDimension]] = None	,			**snake_case_       :    Tuple	,			):
      return rescale(snake_case_	,			scale=snake_case_	,			data_format=snake_case_	,			**snake_case_    )




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




 def      lowercase	(					self       :    Any	,			snake_case_       :    ImageInput	,			snake_case_       :    bool = None	,			snake_case_       :    Dict[str, int] = None	,			snake_case_       :    float = None	,			snake_case_       :    PILImageResampling = None	,			snake_case_       :    bool = None	,			snake_case_       :    float = None	,			snake_case_       :    bool = None	,			snake_case_       :    Optional[Union[float, List[float]]] = None	,			snake_case_       :    Optional[Union[float, List[float]]] = None	,			snake_case_       :    Optional[Union[str, TensorType]] = None	,			snake_case_       :    ChannelDimension = ChannelDimension.FIRST	,			**snake_case_       :    str	,			):
      _UpperCAmelCase     							=   do_resize if do_resize is not None else self.do_resize
      _UpperCAmelCase     							=   crop_pct if crop_pct is not None else self.crop_pct
      _UpperCAmelCase     							=   resample if resample is not None else self.resample
      _UpperCAmelCase     							=   do_rescale if do_rescale is not None else self.do_rescale
      _UpperCAmelCase     							=   rescale_factor if rescale_factor is not None else self.rescale_factor
      _UpperCAmelCase     							=   do_normalize if do_normalize is not None else self.do_normalize
      _UpperCAmelCase     							=   image_mean if image_mean is not None else self.image_mean
      _UpperCAmelCase     							=   image_std if image_std is not None else self.image_std

      _UpperCAmelCase     							=   size if size is not None else self.size
      _UpperCAmelCase     							=   get_size_dict(snake_case_	,			default_to_square=snake_case_    )

      _UpperCAmelCase     							=   make_list_of_images(snake_case_    )

      if not valid_images(snake_case_    ):
           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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
           raise ValueError("crop_pct must be specified if size < 384."    )

      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.
      _UpperCAmelCase     							=   [to_numpy_array(snake_case_    ) for image in images]

      if do_resize:
           _UpperCAmelCase     							=   [self.resize(image=snake_case_	,			size=snake_case_	,			crop_pct=snake_case_	,			resample=snake_case_    ) for image in images]

      if do_rescale:
           _UpperCAmelCase     							=   [self.rescale(image=snake_case_	,			scale=snake_case_    ) for image in images]

      if do_normalize:
           _UpperCAmelCase     							=   [self.normalize(image=snake_case_	,			mean=snake_case_	,			std=snake_case_    ) for image in images]

      _UpperCAmelCase     							=   [to_channel_dimension_format(snake_case_	,			snake_case_    ) for image in images]

      _UpperCAmelCase     							=   {"pixel_values": images}
      return BatchFeature(data=snake_case_	,			tensor_type=snake_case_    )











'''simple docstring'''




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class  SCREAMING_SNAKE_CASE    (datasets.BuilderConfig							):
     lowerCAmelCase					=       None




class  SCREAMING_SNAKE_CASE    (datasets.ArrowBasedBuilder							):
     lowerCAmelCase					=       PandasConfig


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return datasets.DatasetInfo(features=self.config.features)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
          __A						: Dict      =       dl_manager.download_and_extract(self.config.data_files)
          if isinstance(_UpperCAmelCase						,	(str, list, tuple)):
               __A						: Union[str, Any]      =       data_files
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Optional[int]      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,	gen_kwargs={'files': files})]
          __A						: Tuple      =       []
          for split_name, files in data_files.items():
               if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
                    __A						: Dict      =       [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __A						: Optional[Any]      =       [dl_manager.iter_files(_UpperCAmelCase) for file in files]
               splits.append(datasets.SplitGenerator(name=_UpperCAmelCase						,	gen_kwargs={'files': files}))
          return splits


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          if self.config.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __A						: List[str]      =       table_cast(_UpperCAmelCase						,	self.config.features.arrow_schema)
          return pa_table


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          for i, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase)):
               with open(_UpperCAmelCase						,	'rb') as f:
                    __A						: Optional[int]      =       pa.Table.from_pandas(pd.read_pickle(_UpperCAmelCase))
                    yield i, self._cast_table(_UpperCAmelCase)