

'''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,
    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, logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["""pixel_values"""]


						def __init__(       self						,					A_ = True						,					A_ = None						,					A_ = PILImageResampling.BILINEAR						,					A_ = True						,					A_ = None						,					A_ = True						,					A_ = 1 / 255						,					A_ = True						,					A_ = None						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		List[str] 		=    size if size is not None else {"shortest_edge": 256}
										_UpperCAmelCase       :		List[Any] 		=    get_size_dict(A_						,					default_to_square=A_	)
										_UpperCAmelCase       :		List[str] 		=    crop_size if crop_size is not None else {"height": 224, "width": 224}
										_UpperCAmelCase       :		List[Any] 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		Optional[int] 		=    do_resize
										_UpperCAmelCase       :		List[str] 		=    size
										_UpperCAmelCase       :		Tuple 		=    resample
										_UpperCAmelCase       :		Dict 		=    do_center_crop
										_UpperCAmelCase       :		List[Any] 		=    crop_size
										_UpperCAmelCase       :		List[Any] 		=    do_rescale
										_UpperCAmelCase       :		Dict 		=    rescale_factor
										_UpperCAmelCase       :		Tuple 		=    do_normalize
										_UpperCAmelCase       :		Any 		=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase       :		List[Any] 		=    image_std if image_std is not None else IMAGENET_STANDARD_STD


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BICUBIC						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    get_size_dict(A_						,					default_to_square=A_	)
										if "shortest_edge" not in size:
														raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'	)
										_UpperCAmelCase       :		str 		=    get_resize_output_image_size(A_						,					size=size["shortest_edge"]						,					default_to_square=A_	)
										return resize(A_						,					size=A_						,					resample=A_						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    get_size_dict(A_	)
										return center_crop(A_						,					size=(size["height"], size["width"])						,					data_format=A_						,					**A_	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


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





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = ChannelDimension.FIRST						,					**A_						,					):







										'''simple docstring'''


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

										_UpperCAmelCase       :		Optional[int] 		=    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."	)

										# All transformations expect numpy arrays.
										_UpperCAmelCase       :		List[Any] 		=    [to_numpy_array(A_	) for image in images]

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

										if do_center_crop:
														_UpperCAmelCase       :		Dict 		=    [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       :		Tuple 		=    [self.normalize(image=A_						,					mean=A_						,					std=A_	) for image in images]

										_UpperCAmelCase       :		Optional[Any] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

										_UpperCAmelCase       :		str 		=    {"pixel_values": images}
										return BatchFeature(data=A_						,					tensor_type=A_	)





import fire

from utils import calculate_rouge, save_json

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  Any )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()]
				_UpperCAmelCase       :		str 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()][: len(lowerCAmelCase )]
				_UpperCAmelCase       :		str 		=    calculate_rouge(lowerCAmelCase  ,      lowerCAmelCase  ,      **lowerCAmelCase )
				if save_path is not None:
								save_json(lowerCAmelCase  ,      lowerCAmelCase  ,      indent=lowerCAmelCase )
				return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)


