


'''simple docstring'''







import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil



@pytest.fixture
def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       tmp_path / "text.txt"
				__magic_name__				:					Optional[Any]						       =       textwrap.dedent(
				    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "		)
				with open(UpperCamelCase__     ,   "w"     ,   encoding="utf-8"		) as f:
								f.write(UpperCamelCase__		)
				return str(UpperCamelCase__		)



@pytest.fixture
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Any						       =       tmp_path / "text_with_image.txt"
				with open(UpperCamelCase__     ,   "w"     ,   encoding="utf-8"		) as f:
								f.write(UpperCamelCase__		)
				return str(UpperCamelCase__		)



@pytest.mark.parametrize("keep_linebreaks"     ,   [True, False]		)
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				with open(UpperCamelCase__     ,   encoding="utf-8"		) as f:
								__magic_name__				:					Any						       =       f.read().splitlines(keepends=UpperCamelCase__		)
				__magic_name__				:					Any						       =       Text(keep_linebreaks=UpperCamelCase__     ,   encoding="utf-8"		)
				__magic_name__				:					Any						       =       text._generate_tables([[text_file]]		)
				__magic_name__				:					List[Any]						       =       pa.concat_tables([table for _, table in generator]		).to_pydict()["text"]
				assert generated_content == expected_content



@require_pil
def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				with open(UpperCamelCase__     ,   encoding="utf-8"		) as f:
								__magic_name__				:					int						       =       f.read().splitlines()[0]
				__magic_name__				:					Optional[Any]						       =       Text(encoding="utf-8"     ,   features=Features({"image": Image()}		)		)
				__magic_name__				:					Any						       =       text._generate_tables([[text_file_with_image]]		)
				__magic_name__				:					List[Any]						       =       pa.concat_tables([table for _, table in generator]		)
				assert pa_table.schema.field("image"		).type == Image()()
				__magic_name__				:					Optional[int]						       =       pa_table.to_pydict()["image"]
				assert generated_content == [{"path": image_file, "bytes": None}]




@pytest.mark.parametrize("sample_by"     ,   ["line", "paragraph", "document"]		)
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				with open(UpperCamelCase__     ,   encoding="utf-8"		) as f:
								__magic_name__				:					Tuple						       =       f.read()
				if sample_by == "line":
								__magic_name__				:					str						       =       expected_content.splitlines()
				elif sample_by == "paragraph":
								__magic_name__				:					Optional[Any]						       =       expected_content.split("\n\n"		)
				elif sample_by == "document":
								__magic_name__				:					List[str]						       =       [expected_content]
				__magic_name__				:					Tuple						       =       Text(sample_by=UpperCamelCase__     ,   encoding="utf-8"     ,   chunksize=100		)
				__magic_name__				:					List[str]						       =       text._generate_tables([[text_file]]		)
				__magic_name__				:					Union[str, Any]						       =       pa.concat_tables([table for _, table in generator]		).to_pydict()["text"]
				assert generated_content == expected_content



'''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


_SCREAMING_SNAKE_CASE       :   int        = logging.get_logger(__name__)






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      ["pixel_values"]







						def __init__(  self:	Union[str, Any]      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Dict[str, int] = None      ,							__UpperCamelCase:	PILImageResampling = PILImageResampling.BICUBIC      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Union[int, float] = 1 / 255      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							**__UpperCamelCase:	Union[str, Any]      ,							)							->	None:
										super().__init__(**__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else {"height": 256, "width": 256}
										__magic_name__				:					Optional[int]						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					Union[str, Any]						       =       size if size is not None else {"height": 224, "width": 224}
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase     )

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







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	Optional[int] = None      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	List[str]      ,							)							->	np.ndarray:
										__magic_name__				:					List[str]						       =       self.size if size is None else size
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Dict						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )

										__magic_name__       ,     __magic_name__				:					Dict						       =       get_image_size(__UpperCamelCase     )
										__magic_name__				:					int						       =       min(__UpperCamelCase      ,							__UpperCamelCase     )
										__magic_name__				:					Dict						       =       (size["height"] / crop_size["height"]) * min_dim
										__magic_name__				:					str						       =       (size["width"] / crop_size["width"]) * min_dim
										return center_crop(__UpperCamelCase      ,							size=(cropped_height, cropped_width)      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	int      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Dict[str, int]      ,							__UpperCamelCase:	PILImageResampling = PIL.Image.BICUBIC      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	Any      ,							)							->	np.ndarray:
										__magic_name__				:					Optional[Any]						       =       get_size_dict(__UpperCamelCase     )
										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(
										    __UpperCamelCase      ,							size=(size["height"], size["width"])      ,							resample=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Optional[int]      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Union[int, float]      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	List[Any]      ,							)							->	List[Any]:
										return rescale(__UpperCamelCase      ,							scale=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Any      ,							__UpperCamelCase:	np.ndarray      ,							__UpperCamelCase:	Union[float, List[float]]      ,							__UpperCamelCase:	Union[float, List[float]]      ,							__UpperCamelCase:	Optional[Union[str, ChannelDimension]] = None      ,							**__UpperCamelCase:	str      ,							)							->	np.ndarray:
										return normalize(__UpperCamelCase      ,							mean=__UpperCamelCase      ,							std=__UpperCamelCase      ,							data_format=__UpperCamelCase      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	str      ,							__UpperCamelCase:	ImageInput      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Dict[str, int]] = None      ,							__UpperCamelCase:	PILImageResampling = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[float] = None      ,							__UpperCamelCase:	Optional[bool] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[float, List[float]]] = None      ,							__UpperCamelCase:	Optional[Union[str, TensorType]] = None      ,							__UpperCamelCase:	ChannelDimension = ChannelDimension.FIRST      ,							**__UpperCamelCase:	Dict      ,							)							->	PIL.Image.Image:
										__magic_name__				:					Optional[Any]						       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										__magic_name__				:					Optional[Any]						       =       crop_size if crop_size is not None else self.crop_size
										__magic_name__				:					Tuple						       =       get_size_dict(__UpperCamelCase      ,							param_name="crop_size"     )
										__magic_name__				:					int						       =       do_resize if do_resize is not None else self.do_resize
										__magic_name__				:					Optional[Any]						       =       size if size is not None else self.size
										__magic_name__				:					Any						       =       get_size_dict(__UpperCamelCase     )
										__magic_name__				:					Optional[int]						       =       resample if resample is not None else self.resample
										__magic_name__				:					int						       =       do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__				:					int						       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__				:					Any						       =       do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__				:					Dict						       =       image_mean if image_mean is not None else self.image_mean
										__magic_name__				:					Tuple						       =       image_std if image_std is not None else self.image_std

										__magic_name__				:					List[Any]						       =       make_list_of_images(__UpperCamelCase     )

										if not valid_images(__UpperCamelCase     ):
														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.
										__magic_name__				:					Optional[Any]						       =       [to_numpy_array(__UpperCamelCase     ) for image in images]

										if do_center_crop:
														__magic_name__				:					Union[str, Any]						       =       [self.center_crop(__UpperCamelCase      ,							__UpperCamelCase      ,							size=__UpperCamelCase     ) for image in images]

										if do_resize:
														__magic_name__				:					List[Any]						       =       [self.resize(image=__UpperCamelCase      ,							size=__UpperCamelCase      ,							resample=__UpperCamelCase     ) for image in images]

										if do_rescale:
														__magic_name__				:					str						       =       [self.rescale(image=__UpperCamelCase      ,							scale=__UpperCamelCase     ) for image in images]

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

										__magic_name__				:					List[Any]						       =       [to_channel_dimension_format(__UpperCamelCase      ,							__UpperCamelCase     ) for image in images]

										__magic_name__				:					Optional[int]						       =       {"pixel_values": images}
										return BatchFeature(data=__UpperCamelCase      ,							tensor_type=__UpperCamelCase     )