




'''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


A			   =logging.get_logger(__name__)


class       _a  (     __a			):
       __a    :					Union[str, Any]           = ["""pixel_values"""]
       def __init__(     self						:     List[str]       ,    lowercase						:     bool = True       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     PILImageResampling = PIL.Image.NEAREST       ,    lowercase						:     bool = False       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     Union[int, float] = 1 / 255       ,    lowercase						:     bool = False       ,    lowercase						:     bool = True       ,    lowercase						:     bool = True       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     bool = True       ,    **lowercase						:     Optional[int]       ,    ):



           '''simple docstring'''

           super().__init__(**lowercase    )
           UpperCAmelCase      	=		size if size is not None else {'''height''': 346, '''width''': 346}
           UpperCAmelCase      	=		get_size_dict(lowercase    )
           UpperCAmelCase      	=		crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
           UpperCAmelCase      	=		get_size_dict(lowercase       ,    param_name='''crop_size'''    )

           UpperCAmelCase      	=		do_resize
           UpperCAmelCase      	=		size
           UpperCAmelCase      	=		resample
           UpperCAmelCase      	=		do_center_crop
           UpperCAmelCase      	=		crop_size
           UpperCAmelCase      	=		do_rescale
           UpperCAmelCase      	=		rescale_factor
           UpperCAmelCase      	=		rescale_offset
           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
           UpperCAmelCase      	=		include_top
       def    A				(     self						:     List[str]       ,    lowercase						:     np.ndarray       ,    lowercase						:     Dict[str, int]       ,    lowercase						:     PILImageResampling = PIL.Image.NEAREST       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     Union[str, Any]       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		get_size_dict(lowercase    )
           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(
               lowercase       ,    size=(size['''height'''], size['''width'''])       ,    resample=lowercase       ,    data_format=lowercase       ,    **lowercase    )
       def    A				(     self						:     str       ,    lowercase						:     np.ndarray       ,    lowercase						:     Dict[str, int]       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     str       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		get_size_dict(lowercase    )
           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(lowercase       ,    size=(size['''height'''], size['''width'''])       ,    data_format=lowercase       ,    **lowercase    )
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     np.ndarray       ,    lowercase						:     Union[int, float]       ,    lowercase						:     bool = True       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     int       ,    ):



           '''simple docstring'''

           if offset:
               UpperCAmelCase      	=		(image - 127.5) * scale
               if data_format is not None:
                   UpperCAmelCase      	=		to_channel_dimension_format(lowercase       ,    lowercase    )
               UpperCAmelCase      	=		rescaled_image.astype(np.floataa    )
           else:
               UpperCAmelCase      	=		rescale(lowercase       ,    scale=lowercase       ,    data_format=lowercase       ,    **lowercase    )
           return rescaled_image
       def    A				(     self						:     Union[str, Any]       ,    lowercase						:     np.ndarray       ,    lowercase						:     Union[float, List[float]]       ,    lowercase						:     Union[float, List[float]]       ,    lowercase						:     Optional[Union[str, ChannelDimension]] = None       ,    **lowercase						:     Optional[int]       ,    ):



           '''simple docstring'''

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







       def    A				(     self						:     List[str]       ,    lowercase						:     ImageInput       ,    lowercase						:     bool = None       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     Tuple=None       ,    lowercase						:     bool = None       ,    lowercase						:     Dict[str, int] = None       ,    lowercase						:     bool = None       ,    lowercase						:     float = None       ,    lowercase						:     bool = None       ,    lowercase						:     bool = None       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     Optional[Union[float, List[float]]] = None       ,    lowercase						:     bool = None       ,    lowercase						:     Optional[Union[str, TensorType]] = None       ,    lowercase						:     ChannelDimension = ChannelDimension.FIRST       ,    **lowercase						:     Tuple       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		do_resize if do_resize is not None else self.do_resize
           UpperCAmelCase      	=		resample if resample is not None else self.resample
           UpperCAmelCase      	=		do_center_crop if do_center_crop is not None else self.do_center_crop
           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      	=		rescale_offset if rescale_offset is not None else self.rescale_offset
           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      	=		include_top if include_top is not None else self.include_top

           UpperCAmelCase      	=		size if size is not None else self.size
           UpperCAmelCase      	=		get_size_dict(lowercase    )
           UpperCAmelCase      	=		crop_size if crop_size is not None else self.crop_size
           UpperCAmelCase      	=		get_size_dict(lowercase       ,    param_name='''crop_size'''    )

           UpperCAmelCase      	=		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_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.
           UpperCAmelCase      	=		[to_numpy_array(lowercase    ) for image in images]

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

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

           if do_rescale:
               UpperCAmelCase      	=		[self.rescale(image=lowercase       ,    scale=lowercase       ,    offset=lowercase    ) for image in images]

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

           if include_top:
               UpperCAmelCase      	=		[self.normalize(image=lowercase       ,    mean=[0, 0, 0]       ,    std=lowercase    ) for image in images]

           UpperCAmelCase      	=		[to_channel_dimension_format(lowercase       ,    lowercase    ) for image in images]

           UpperCAmelCase      	=		{'''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  (     __a			):
       def __init__(     self						:     Any       ,    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						:     str       ,    ):



           '''simple docstring'''

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







       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           if self.streaming:
               UpperCAmelCase      	=		self.builder.as_streaming_dataset(split=self.split    )
           # Build regular (map-style) dataset
           else:
               UpperCAmelCase      	=		None
               UpperCAmelCase      	=		None
               UpperCAmelCase      	=		None
               UpperCAmelCase      	=		None

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






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



           '''simple docstring'''

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

           UpperCAmelCase      	=		dataset
           UpperCAmelCase      	=		path_or_buf
           UpperCAmelCase      	=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           UpperCAmelCase      	=		num_proc
           UpperCAmelCase      	=		'''utf-8'''
           UpperCAmelCase      	=		to_csv_kwargs
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.to_csv_kwargs.pop('''path_or_buf'''       ,    lowercase    )
           UpperCAmelCase      	=		self.to_csv_kwargs.pop('''header'''       ,    lowercase    )
           UpperCAmelCase      	=		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:
                   UpperCAmelCase      	=		self._write(file_obj=lowercase       ,    header=lowercase       ,    index=lowercase       ,    **self.to_csv_kwargs    )
           else:
               UpperCAmelCase      	=		self._write(file_obj=self.path_or_buf       ,    header=lowercase       ,    index=lowercase       ,    **self.to_csv_kwargs    )
           return written
       def    A				(     self						:     List[str]       ,    lowercase						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase	,					UpperCAmelCase	,					UpperCAmelCase	,					UpperCAmelCase      	=		args

           UpperCAmelCase      	=		query_table(
               table=self.dataset.data       ,    key=slice(lowercase       ,    offset + self.batch_size    )       ,    indices=self.dataset._indices       ,    )
           UpperCAmelCase      	=		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    A				(     self						:     List[str]       ,    lowercase						:     BinaryIO       ,    lowercase						:     int       ,    lowercase						:     List[str]       ,    **lowercase						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		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'''       ,    ):
                   UpperCAmelCase      	=		self._batch_csv((offset, header, index, to_csv_kwargs)    )
                   written += file_obj.write(lowercase    )

           else:
               UpperCAmelCase	,					UpperCAmelCase      	=		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




