





"""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_	:		List[Any]         =   logging.get_logger(__name__)

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  ["""pixel_values"""]

      def __init__(	self   , snake_case_ = True   , snake_case_ = None   , snake_case_ = True   , snake_case_ = None   , snake_case_ = PILImageResampling.BICUBIC   , snake_case_ = True   , snake_case_ = 1 / 2_5_5   , snake_case_ = True   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(**snake_case_    )
        A_    :			List[Any]									=    crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
        A_    :			str									=    get_size_dict(snake_case_   , param_name='crop_size'    )
        A_    :			Any									=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
        A_    :			Any									=    get_size_dict(snake_case_    )

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        A_    :			Optional[int]									=    self.size if size is None else size
        A_    :			Optional[Any]									=    get_size_dict(snake_case_    )
        A_    :			Union[str, Any]									=    get_size_dict(snake_case_   , param_name='crop_size'    )

        A_		,			A_    :			Union[str, Any]									=    get_image_size(snake_case_    )
        A_    :			str									=    min(snake_case_   , snake_case_    )
        A_    :			Tuple									=    (size['height'] / crop_size['height']) * min_dim
        A_    :			Any									=    (size['width'] / crop_size['width']) * min_dim
        return center_crop(snake_case_   , size=(cropped_height, cropped_width)   , data_format=snake_case_   , **snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = PIL.Image.BICUBIC   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        A_    :			int									=    get_size_dict(snake_case_    )
        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(
            snake_case_   , size=(size['height'], size['width'])   , resample=snake_case_   , data_format=snake_case_   , **snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = ChannelDimension.FIRST   , **snake_case_   , ):

        """simple docstring"""

        A_    :			List[Any]									=    do_center_crop if do_center_crop is not None else self.do_center_crop
        A_    :			List[str]									=    crop_size if crop_size is not None else self.crop_size
        A_    :			str									=    get_size_dict(snake_case_   , param_name='crop_size'    )
        A_    :			Union[str, Any]									=    do_resize if do_resize is not None else self.do_resize
        A_    :			Any									=    size if size is not None else self.size
        A_    :			Dict									=    get_size_dict(snake_case_    )
        A_    :			int									=    resample if resample is not None else self.resample
        A_    :			Any									=    do_rescale if do_rescale is not None else self.do_rescale
        A_    :			List[str]									=    rescale_factor if rescale_factor is not None else self.rescale_factor
        A_    :			List[Any]									=    do_normalize if do_normalize is not None else self.do_normalize
        A_    :			int									=    image_mean if image_mean is not None else self.image_mean
        A_    :			Tuple									=    image_std if image_std is not None else self.image_std

        A_    :			Tuple									=    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_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.
        A_    :			Dict									=    [to_numpy_array(snake_case_    ) for image in images]

        if do_center_crop:
          A_    :			Tuple									=    [self.center_crop(snake_case_   , snake_case_   , size=snake_case_    ) for image in images]

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

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

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

        A_    :			Optional[int]									=    [to_channel_dimension_format(snake_case_   , snake_case_    ) for image in images]

        A_    :			str									=    {'pixel_values': images}
        return BatchFeature(data=snake_case_   , tensor_type=snake_case_    )






"""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 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , snake_case_ = None   , snake_case_ = False   , snake_case_ = False   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        super().__init__(
            snake_case_   , split=snake_case_   , features=snake_case_   , cache_dir=snake_case_   , keep_in_memory=snake_case_   , streaming=snake_case_   , num_proc=snake_case_   , **snake_case_   , )
        A_    :			int									=    path_or_paths if isinstance(snake_case_   , snake_case_    ) else {self.split: path_or_paths}
        A_    :			List[str]									=    Csv(
            cache_dir=snake_case_   , data_files=snake_case_   , features=snake_case_   , **snake_case_   , )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

          self.builder.download_and_prepare(
              download_config=snake_case_   , download_mode=snake_case_   , verification_mode=snake_case_   , base_path=snake_case_   , num_proc=self.num_proc   , )
          A_    :			List[Any]									=    self.builder.as_dataset(
              split=self.split   , verification_mode=snake_case_   , in_memory=self.keep_in_memory    )
        return dataset


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

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

        A_    :			Optional[int]									=    dataset
        A_    :			List[str]									=    path_or_buf
        A_    :			Optional[int]									=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        A_    :			Optional[int]									=    num_proc
        A_    :			Optional[int]									=    'utf-8'
        A_    :			int									=    to_csv_kwargs

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.to_csv_kwargs.pop('path_or_buf'   , snake_case_    )
        A_    :			str									=    self.to_csv_kwargs.pop('header'   , snake_case_    )
        A_    :			Optional[Any]									=    self.to_csv_kwargs.pop('index'   , snake_case_    )

        if isinstance(self.path_or_buf   , (str, bytes, os.PathLike)    ):
          with open(self.path_or_buf   , 'wb+'    ) as buffer:
            A_    :			Optional[Any]									=    self._write(file_obj=snake_case_   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        else:
          A_    :			int									=    self._write(file_obj=self.path_or_buf   , header=snake_case_   , index=snake_case_   , **self.to_csv_kwargs    )
        return written

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_		,			A_    :			Union[str, Any]									=    args

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , **snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    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'   , ):
            A_    :			Optional[int]									=    self._batch_csv((offset, header, index, to_csv_kwargs)    )
            written += file_obj.write(snake_case_    )

        else:
          A_		,			A_    :			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   , snake_case_   , snake_case_    )]   , )   , 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(snake_case_    )

        return written