





'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCAmelCase      :  Any      							=logging.get_logger(__name__)
class    a_     (       _lowerCAmelCase							):
     __A  				=    ["pixel_values"]





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





        """simple docstring"""




        super().__init__(**lowercase		)
        lowercase_    :Dict					    =				crop_size if crop_size is not None else {"height": 256, "width": 256}
        lowercase_    :str					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Optional[Any]					    =				size if size is not None else {"height": 224, "width": 224}
        lowercase_    :Union[str, Any]					    =				get_size_dict(lowercase		)

        lowercase_    :Union[str, Any]					    =				do_center_crop
        lowercase_    :str					    =				crop_size
        lowercase_    :List[str]					    =				do_resize
        lowercase_    :str					    =				size
        lowercase_    :Tuple					    =				resample
        lowercase_    :int					    =				do_rescale
        lowercase_    :Union[str, Any]					    =				rescale_factor
        lowercase_    :Tuple					    =				do_normalize
        lowercase_    :List[Any]					    =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_    :Optional[int]					    =				image_std if image_std is not None else IMAGENET_DEFAULT_STD





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





        """simple docstring"""




        lowercase_    :Dict					    =				self.size if size is None else size
        lowercase_    :Dict					    =				get_size_dict(lowercase		)
        lowercase_    :List[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)

        lowercase_    ,							lowercase_    :List[str]					    =				get_image_size(lowercase		)
        lowercase_    :List[str]					    =				min(lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				(size["height"] / crop_size["height"]) * min_dim
        lowercase_    :int					    =				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowercase			,   size=(cropped_height, cropped_width)			,   data_format=lowercase			,   **lowercase		)





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





        """simple docstring"""




        lowercase_    :Optional[int]					    =				get_size_dict(lowercase		)
        if "height" not in size or "width" not in size:
           raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'		)
        return resize(
            lowercase			,   size=(size["height"], size["width"])			,   resample=lowercase			,   data_format=lowercase			,   **lowercase		)





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[int, float]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      Union[str, Any]			,   ):





        """simple docstring"""




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





     def 							lowercase__   (				self	:      List[str]			,   lowercase	:      np.ndarray			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Union[float, List[float]]			,   lowercase	:      Optional[Union[str, ChannelDimension]] = None			,   **lowercase	:      str			,   ):





        """simple docstring"""




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







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      ImageInput			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[Dict[str, int]] = None			,   lowercase	:      PILImageResampling = None			,   lowercase	:      Optional[bool] = None			,   lowercase	:      Optional[float] = None			,   lowercase	:      Optional[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	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_    :Union[str, Any]					    =				crop_size if crop_size is not None else self.crop_size
        lowercase_    :Optional[Any]					    =				get_size_dict(lowercase			,   param_name="crop_size"		)
        lowercase_    :Union[str, Any]					    =				do_resize if do_resize is not None else self.do_resize
        lowercase_    :Any					    =				size if size is not None else self.size
        lowercase_    :List[Any]					    =				get_size_dict(lowercase		)
        lowercase_    :Optional[Any]					    =				resample if resample is not None else self.resample
        lowercase_    :Dict					    =				do_rescale if do_rescale is not None else self.do_rescale
        lowercase_    :List[Any]					    =				rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_    :str					    =				do_normalize if do_normalize is not None else self.do_normalize
        lowercase_    :Dict					    =				image_mean if image_mean is not None else self.image_mean
        lowercase_    :List[Any]					    =				image_std if image_std is not None else self.image_std

        lowercase_    :Any					    =				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_center_crop and crop_size is None:
           raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."		)

        if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."		)

        # All transformations expect numpy arrays.
        lowercase_    :Optional[int]					    =				[to_numpy_array(lowercase		) for image in images]

        if do_center_crop:
           lowercase_    :str					    =				[self.center_crop(lowercase			,   lowercase			,   size=lowercase		) for image in images]

        if do_resize:
           lowercase_    :List[str]					    =				[self.resize(image=lowercase			,   size=lowercase			,   resample=lowercase		) for image in images]

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

        if do_normalize:
           lowercase_    :Optional[Any]					    =				[self.normalize(image=lowercase			,   mean=lowercase			,   std=lowercase		) for image in images]

        lowercase_    :Optional[Any]					    =				[to_channel_dimension_format(lowercase			,   lowercase		) for image in images]

        lowercase_    :Union[str, Any]					    =				{"pixel_values": images}
        return BatchFeature(data=lowercase			,   tensor_type=lowercase		)









'''simple docstring'''


import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class    a_     (       _lowerCAmelCase							):





     def __init__(				self	:      Dict			,   lowercase	:      NestedDataStructureLike[PathLike]			,   lowercase	:      Optional[NamedSplit] = None			,   lowercase	:      Optional[Features] = None			,   lowercase	:      str = None			,   lowercase	:      bool = False			,   lowercase	:      bool = False			,   lowercase	:      Optional[int] = None			,   **lowercase	:      Any			,   ):





        """simple docstring"""




        super().__init__(
            lowercase			,   split=lowercase			,   features=lowercase			,   cache_dir=lowercase			,   keep_in_memory=lowercase			,   streaming=lowercase			,   num_proc=lowercase			,   **lowercase			,   )
        lowercase_    :Any					    =				path_or_paths if isinstance(lowercase			,   lowercase		) else {self.split: path_or_paths}
        lowercase_    :Any					    =				Csv(
            cache_dir=lowercase			,   data_files=lowercase			,   features=lowercase			,   **lowercase			,   )







     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




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

           self.builder.download_and_prepare(
               download_config=lowercase			,   download_mode=lowercase			,   verification_mode=lowercase			,   base_path=lowercase			,   num_proc=self.num_proc			,   )
           lowercase_    :int					    =				self.builder.as_dataset(
               split=self.split			,   verification_mode=lowercase			,   in_memory=self.keep_in_memory		)
        return dataset







class    a_     :





     def __init__(				self	:      Dict			,   lowercase	:      Dataset			,   lowercase	:      Union[PathLike, BinaryIO]			,   lowercase	:      Optional[int] = None			,   lowercase	:      Optional[int] = None			,   **lowercase	:      Any			,   ):





        """simple docstring"""




        if num_proc is not None and num_proc <= 0:
           raise ValueError(F'num_proc {num_proc} must be an integer > 0.'		)

        lowercase_    :str					    =				dataset
        lowercase_    :List[Any]					    =				path_or_buf
        lowercase_    :Any					    =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        lowercase_    :int					    =				num_proc
        lowercase_    :Optional[Any]					    =				"utf-8"
        lowercase_    :Dict					    =				to_csv_kwargs





     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :int					    =				self.to_csv_kwargs.pop("path_or_buf"			,   lowercase		)
        lowercase_    :List[str]					    =				self.to_csv_kwargs.pop("header"			,   lowercase		)
        lowercase_    :Optional[Any]					    =				self.to_csv_kwargs.pop("index"			,   lowercase		)

        if isinstance(self.path_or_buf			,   (str, bytes, os.PathLike)		):
           with open(self.path_or_buf			,   "wb+"		) as buffer:
              lowercase_    :List[Any]					    =				self._write(file_obj=lowercase			,   header=lowercase			,   index=lowercase			,   **self.to_csv_kwargs		)
        else:
           lowercase_    :Any					    =				self._write(file_obj=self.path_or_buf			,   header=lowercase			,   index=lowercase			,   **self.to_csv_kwargs		)
        return written





     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    ,							lowercase_    ,							lowercase_    ,							lowercase_    :Optional[Any]					    =				args

        lowercase_    :Dict					    =				query_table(
            table=self.dataset.data			,   key=slice(lowercase			,   offset + self.batch_size		)			,   indices=self.dataset._indices			,   )
        lowercase_    :List[str]					    =				batch.to_pandas().to_csv(
            path_or_buf=lowercase			,   header=header if (offset == 0) else False			,   index=lowercase			,   **lowercase		)
        return csv_str.encode(self.encoding		)







     def 							lowercase__   (				self	:      Dict			,   lowercase	:      BinaryIO			,   lowercase	:      Dict			,   lowercase	:      int			,   **lowercase	:      Any		):





        """simple docstring"""




        lowercase_    :Dict					    =				0

        if self.num_proc is None or self.num_proc == 1:
           for offset in logging.tqdm(
               range(0			,   len(self.dataset		)			,   self.batch_size		)			,   unit="ba"			,   disable=not logging.is_progress_bar_enabled()			,   desc="Creating CSV from Arrow format"			,   ):
              lowercase_    :Optional[Any]					    =				self._batch_csv((offset, header, index, to_csv_kwargs)		)
              written += file_obj.write(lowercase		)

        else:
           lowercase_    ,							lowercase_    :Optional[int]					    =				len(self.dataset		), self.batch_size
           with multiprocessing.Pool(self.num_proc		) as pool:
              for csv_str in logging.tqdm(
                  pool.imap(
                      self._batch_csv			,   [(offset, header, index, to_csv_kwargs) for offset in range(0			,   lowercase			,   lowercase		)]			,   )			,   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,   unit="ba"			,   disable=not logging.is_progress_bar_enabled()			,   desc="Creating CSV from Arrow format"			,   ):
                 written += file_obj.write(lowercase		)

        return written


