

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowerCamelCase       :     Dict           =							logging.get_logger(__name__)


if is_vision_available():
     import PIL





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  ["""pixel_values"""]



   def __init__(							self							:  Tuple,	__A							:  bool = True,	__A							:  Dict[str, int] = None,	__A							:  PILImageResampling = PILImageResampling.BICUBIC,	__A							:  bool = True,	__A							:  Dict[str, int] = None,	__A							:  bool = True,	__A							:  Union[int, float] = 1 / 2_5_5,	__A							:  bool = True,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  bool = True,	**__A							:  Union[str, Any],	):
     super().__init__(**__A		)
     UpperCAmelCase :							Optional[int]								=				size if size is not None else {'''shortest_edge''': 2_2_4}
     UpperCAmelCase :							str								=				get_size_dict(__A,	default_to_square=__A		)
     UpperCAmelCase :							List[Any]								=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
     UpperCAmelCase :							Any								=				get_size_dict(__A		)

     UpperCAmelCase :							List[Any]								=				do_resize
     UpperCAmelCase :							Tuple								=				size
     UpperCAmelCase :							List[str]								=				resample
     UpperCAmelCase :							List[Any]								=				do_center_crop
     UpperCAmelCase :							Dict								=				crop_size
     UpperCAmelCase :							List[str]								=				do_rescale
     UpperCAmelCase :							Tuple								=				rescale_factor
     UpperCAmelCase :							Optional[Any]								=				do_normalize
     UpperCAmelCase :							List[str]								=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     UpperCAmelCase :							int								=				image_std if image_std is not None else OPENAI_CLIP_STD
     UpperCAmelCase :							Dict								=				do_convert_rgb



   def  __magic_name__					(							self							:  Dict,	__A							:  np.ndarray,	__A							:  Dict[str, int],	__A							:  PILImageResampling = PILImageResampling.BICUBIC,	__A							:  Optional[Union[str, ChannelDimension]] = None,	**__A							:  Union[str, Any],	):
     UpperCAmelCase :							Any								=				get_size_dict(__A,	default_to_square=__A		)
     UpperCAmelCase :							int								=				get_resize_output_image_size(
         __A,	size=(size['''height'''], size['''width''']),	default_to_square=__A		)
     return resize(__A,	size=__A,	resample=__A,	data_format=__A,	**__A		)



   def  __magic_name__					(							self							:  Optional[int],	__A							:  np.ndarray,	__A							:  Dict[str, int],	__A							:  Optional[Union[str, ChannelDimension]] = None,	**__A							:  Union[str, Any],	):
     UpperCAmelCase :							str								=				get_size_dict(__A		)
     return center_crop(__A,	size=(size['''height'''], size['''width''']),	data_format=__A,	**__A		)



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



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





   def  __magic_name__					(							self							:  List[Any],	__A							:  ImageInput,	__A							:  bool = None,	__A							:  Dict[str, int] = None,	__A							:  PILImageResampling = None,	__A							:  bool = None,	__A							:  int = None,	__A							:  bool = None,	__A							:  float = None,	__A							:  bool = None,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  Optional[Union[float, List[float]]] = None,	__A							:  bool = None,	__A							:  Optional[Union[str, TensorType]] = None,	__A							:  Optional[ChannelDimension] = ChannelDimension.FIRST,	**__A							:  Optional[int],	):
     UpperCAmelCase :							Union[str, Any]								=				do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase :							Optional[Any]								=				size if size is not None else self.size
     UpperCAmelCase :							Dict								=				get_size_dict(__A,	default_to_square=__A		)
     UpperCAmelCase :							List[Any]								=				resample if resample is not None else self.resample
     UpperCAmelCase :							Any								=				do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase :							int								=				crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase :							int								=				get_size_dict(__A		)
     UpperCAmelCase :							List[Any]								=				do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase :							Tuple								=				rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase :							Optional[int]								=				do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase :							int								=				image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase :							List[Any]								=				image_std if image_std is not None else self.image_std
     UpperCAmelCase :							Any								=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     UpperCAmelCase :							List[str]								=				make_list_of_images(__A		)

     if not valid_images(__A		):
       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:
       raise ValueError('''Size 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.'''		)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
       UpperCAmelCase :							Tuple								=				[convert_to_rgb(__A		) for image in images]

     # All transformations expect numpy arrays.
     UpperCAmelCase :							int								=				[to_numpy_array(__A		) for image in images]

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

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

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

     if do_normalize:
       UpperCAmelCase :							Union[str, Any]								=				[self.normalize(image=__A,	mean=__A,	std=__A		) for image in images]

     UpperCAmelCase :							Dict								=				[to_channel_dimension_format(__A,	__A		) for image in images]

     UpperCAmelCase :							Dict								=				{'''pixel_values''': images}
     return BatchFeature(data=__A,	tensor_type=__A		)








import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		List[str]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory'''       ,				[False, True]    )
def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Optional[int]:
  UpperCAmelCase :							Optional[int]								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							Union[str, Any]								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize(
    '''features'''       ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]       ,				)
def     a__  (	UpperCAmelCase							:    Optional[int]       ,				UpperCAmelCase							:    int       ,				UpperCAmelCase							:    int    )						->		Optional[Any]:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							Optional[Any]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							List[Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[Any]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							Any								=				CsvDatasetReader(UpperCAmelCase       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    str    )						->		Any:
  UpperCAmelCase :							List[str]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Any								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							int								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase       ,				split=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )
  assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type'''       ,				[str, list]    )
def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Any    )						->		str:
  if issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							Tuple								=				csv_path
  elif issubclass(UpperCAmelCase       ,				UpperCAmelCase    ):
    UpperCAmelCase :							List[str]								=				[csv_path]
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  UpperCAmelCase :							str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Tuple								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_dataset(UpperCAmelCase       ,				UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    str       ,				UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    int=("train",)    )						->		Optional[int]:
  assert isinstance(UpperCAmelCase       ,				UpperCAmelCase    )
  for split in splits:
    UpperCAmelCase :							Optional[Any]								=				dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory'''       ,				[False, True]    )
def     a__  (	UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Tuple    )						->		Tuple:
  UpperCAmelCase :							Any								=				tmp_path / '''cache'''
  UpperCAmelCase :							Dict								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    UpperCAmelCase :							str								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase       ,				keep_in_memory=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize(
    '''features'''       ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]       ,				)
def     a__  (	UpperCAmelCase							:    int       ,				UpperCAmelCase							:    Tuple       ,				UpperCAmelCase							:    Any    )						->		Optional[int]:
  UpperCAmelCase :							Tuple								=				tmp_path / '''cache'''
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  UpperCAmelCase :							List[str]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							Union[str, Any]								=				features.copy() if features else default_expected_features
  UpperCAmelCase :							List[str]								=				(
      Features({feature: Value(UpperCAmelCase    ) for feature, dtype in features.items()}    ) if features is not None else None
  )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				features=UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase    )

@pytest.mark.parametrize('''split'''       ,				[None, NamedSplit('''train'''    ), '''train''', '''test''']    )
def     a__  (	UpperCAmelCase							:    Union[str, Any]       ,				UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[Any]    )						->		Dict:
  if split:
    UpperCAmelCase :							Union[str, Any]								=				{split: csv_path}
  else:
    UpperCAmelCase :							List[str]								=				{'''train''': csv_path, '''test''': csv_path}
  UpperCAmelCase :							str								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
  UpperCAmelCase :							str								=				CsvDatasetReader(UpperCAmelCase       ,				cache_dir=UpperCAmelCase    ).read()
  _check_csv_datasetdict(UpperCAmelCase       ,				UpperCAmelCase       ,				splits=list(path.keys()    )    )
  assert all(dataset[split].split == split for split in path.keys()    )

def     a__  (	UpperCAmelCase							:    Optional[Any]    )						->		int:
  with open(UpperCAmelCase       ,				encoding='''utf-8'''    ) as csvfile:
    yield from csv.reader(UpperCAmelCase    )

def     a__  (	UpperCAmelCase							:    List[str]       ,				UpperCAmelCase							:    Union[str, Any]    )						->		Union[str, Any]:
  UpperCAmelCase :							int								=				tmp_path / '''cache'''
  UpperCAmelCase :							int								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=1    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							int								=				iter_csv_file(UpperCAmelCase    )

  for rowa, rowa in zip(UpperCAmelCase       ,				UpperCAmelCase    ):
    assert rowa == rowa

def     a__  (	UpperCAmelCase							:    Any       ,				UpperCAmelCase							:    Dict    )						->		Any:
  UpperCAmelCase :							List[Any]								=				tmp_path / '''cache'''
  UpperCAmelCase :							Tuple								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[str]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=2    ).write()

  UpperCAmelCase :							List[Any]								=				iter_csv_file(UpperCAmelCase    )
  UpperCAmelCase :							Union[str, Any]								=				iter_csv_file(UpperCAmelCase    )

  for rowa, rowa in zip(UpperCAmelCase       ,				UpperCAmelCase    ):
    assert rowa == rowa






def     a__  (	UpperCAmelCase							:    List[Any]       ,				UpperCAmelCase							:    Optional[int]    )						->		Optional[Any]:
  UpperCAmelCase :							Dict								=				tmp_path / '''cache'''
  UpperCAmelCase :							List[Any]								=				os.path.join(UpperCAmelCase       ,				'''tmp.csv'''    )
  UpperCAmelCase :							List[Any]								=				CsvDatasetReader({'''train''': csv_path}       ,				cache_dir=UpperCAmelCase    ).read()
  with pytest.raises(UpperCAmelCase    ):
    CsvDatasetWriter(dataset['''train''']       ,				UpperCAmelCase       ,				num_proc=0    )





