
import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil




@pytest.fixture
def    __UpperCamelCase     (				lowerCAmelCase__							:				Dict  ):
 __a :  Dict						=  tmp_path / '''file.csv'''
 __a :  Dict						=  textwrap.dedent(
     '''\
        header1,header2
        1,2
        10,20
        '''  )
 with open(lowerCAmelCase__  ,   '''w'''  ) as f:
  f.write(lowerCAmelCase__  )
 return str(lowerCAmelCase__  )




@pytest.fixture
def    __UpperCamelCase     (				lowerCAmelCase__							:				Union[str, Any]  ):
 __a :  List[Any]						=  tmp_path / '''malformed_file.csv'''
 __a :  List[Any]						=  textwrap.dedent(
     '''\
        header1,header2
        1,2
        10,20,
        '''  )
 with open(lowerCAmelCase__  ,   '''w'''  ) as f:
  f.write(lowerCAmelCase__  )
 return str(lowerCAmelCase__  )




@pytest.fixture
def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				Dict  ):
 __a :  List[str]						=  tmp_path / '''csv_with_image.csv'''
 __a :  Dict						=  textwrap.dedent(
     f"\\n        image\n        {image_file}\n        "  )
 with open(lowerCAmelCase__  ,   '''w'''  ) as f:
  f.write(lowerCAmelCase__  )
 return str(lowerCAmelCase__  )




@pytest.fixture
def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ):
 __a :  Dict						=  tmp_path / '''csv_with_label.csv'''
 __a :  Any						=  textwrap.dedent(
     '''\
        label
        good
        bad
        good
        '''  )
 with open(lowerCAmelCase__  ,   '''w'''  ) as f:
  f.write(lowerCAmelCase__  )
 return str(lowerCAmelCase__  )




@pytest.fixture
def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ):
 __a :  Union[str, Any]						=  tmp_path / '''csv_with_int_list.csv'''
 __a :  str						=  textwrap.dedent(
     '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''  )
 with open(lowerCAmelCase__  ,   '''w'''  ) as f:
  f.write(lowerCAmelCase__  )
 return str(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Dict  ):
 __a :  List[str]						=  Csv()
 __a :  Optional[Any]						=  csv._generate_tables([[csv_file, malformed_csv_file]]  )
 with pytest.raises(lowerCAmelCase__  ,   match='''Error tokenizing data'''  ):
  for _ in generator:
   pass
 assert any(
     record.levelname == '''ERROR'''
     and '''Failed to read file''' in record.message
     and os.path.basename(lowerCAmelCase__  ) in record.message
     for record in caplog.records  )




@require_pil
def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ):
 with open(lowerCAmelCase__  ,   encoding='''utf-8'''  ) as f:
  __a :  Optional[int]						=  f.read().splitlines()[1]
 __a :  List[Any]						=  Csv(encoding='''utf-8'''  ,   features=Features({'''image''': Image()}  )  )
 __a :  Dict						=  csv._generate_tables([[csv_file_with_image]]  )
 __a :  Optional[int]						=  pa.concat_tables([table for _, table in generator]  )
 assert pa_table.schema.field('''image'''  ).type == Image()()
 __a :  List[Any]						=  pa_table.to_pydict()['''image''']
 assert generated_content == [{"path": image_file, "bytes": None}]




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ):
 with open(lowerCAmelCase__  ,   encoding='''utf-8'''  ) as f:
  __a :  Optional[int]						=  f.read().splitlines()[1:]
 __a :  List[Any]						=  Csv(encoding='''utf-8'''  ,   features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']  )}  )  )
 __a :  Tuple						=  csv._generate_tables([[csv_file_with_label]]  )
 __a :  Tuple						=  pa.concat_tables([table for _, table in generator]  )
 assert pa_table.schema.field('''label'''  ).type == ClassLabel(names=['''good''', '''bad''']  )()
 __a :  Optional[Any]						=  pa_table.to_pydict()['''label''']
 assert generated_content == [ClassLabel(names=['''good''', '''bad''']  ).straint(lowerCAmelCase__  ) for label in labels]




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ):
 __a :  Union[str, Any]						=  Csv(encoding='''utf-8'''  ,   sep=''','''  ,   converters={'''int_list''': lambda lowerCAmelCase__  : [int(lowerCAmelCase__  ) for i in x.split()]}  )
 __a :  Tuple						=  csv._generate_tables([[csv_file_with_int_list]]  )
 __a :  Any						=  pa.concat_tables([table for _, table in generator]  )
 assert pa.types.is_list(pa_table.schema.field('''int_list'''  ).type  )
 __a :  List[str]						=  pa_table.to_pydict()['''int_list''']
 assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]


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


lowercase__       =logging.get_logger(__name__)



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      ["pixel_values"]
   def __init__(self :					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_   )
    __a :  Optional[Any]						=  size if size is not None else {'''shortest_edge''': 3_8_4}
    __a :  str						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  Tuple						=  do_resize
    __a :  List[str]						=  size
    # Default value set here for backwards compatibility where the value in config is None
    __a :  List[str]						=  crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
    __a :  Tuple						=  resample
    __a :  Any						=  do_rescale
    __a :  Optional[Any]						=  rescale_factor
    __a :  int						=  do_normalize
    __a :  List[str]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __a :  Union[str, Any]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD
   def       lowerCAmelCase			(self :					int					,				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_ :					str					,				):
    __a :  str						=  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()}"   )
    __a :  Any						=  size['''shortest_edge''']

    if shortest_edge < 3_8_4:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     __a :  Union[str, Any]						=  int(shortest_edge / crop_pct   )
     __a :  Tuple						=  get_resize_output_image_size(snake_case_					,				size=snake_case_					,				default_to_square=snake_case_   )
     __a :  List[str]						=  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       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					np.ndarray					,				snake_case_ :					Union[int, float]					,				snake_case_ :					Optional[Union[str, ChannelDimension]] = None					,				**snake_case_ :					Union[str, Any]					,				):
    return rescale(snake_case_					,				scale=snake_case_					,				data_format=snake_case_					,				**snake_case_   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				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       lowerCAmelCase			(self :					Dict					,				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_ :					Dict					,				):
    __a :  List[Any]						=  do_resize if do_resize is not None else self.do_resize
    __a :  Dict						=  crop_pct if crop_pct is not None else self.crop_pct
    __a :  Tuple						=  resample if resample is not None else self.resample
    __a :  Optional[int]						=  do_rescale if do_rescale is not None else self.do_rescale
    __a :  Optional[int]						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    __a :  int						=  do_normalize if do_normalize is not None else self.do_normalize
    __a :  Optional[int]						=  image_mean if image_mean is not None else self.image_mean
    __a :  Optional[int]						=  image_std if image_std is not None else self.image_std

    __a :  Dict						=  size if size is not None else self.size
    __a :  List[str]						=  get_size_dict(snake_case_					,				default_to_square=snake_case_   )

    __a :  List[str]						=  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.
    __a :  List[Any]						=  [to_numpy_array(snake_case_   ) for image in images]

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

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

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

    __a :  str						=  [to_channel_dimension_format(snake_case_					,				snake_case_   ) for image in images]

    __a :  Tuple						=  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_					,				tensor_type=snake_case_   )
