


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


__UpperCamelCase				:  Union[str, Any]      					=							logging.get_logger(__name__)


if is_vision_available():
       import PIL





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Any			= ['pixel_values']





    def __init__(	self				:   Optional[int]       ,   __snake_case				:   bool = True       ,   __snake_case				:   Dict[str, int] = None       ,   __snake_case				:   PILImageResampling = PILImageResampling.BICUBIC       ,   __snake_case				:   bool = True       ,   __snake_case				:   Dict[str, int] = None       ,   __snake_case				:   bool = True       ,   __snake_case				:   Union[int, float] = 1 / 255       ,   __snake_case				:   bool = True       ,   __snake_case				:   Optional[Union[float, List[float]]] = None       ,   __snake_case				:   Optional[Union[float, List[float]]] = None       ,   __snake_case				:   bool = True       ,   **__snake_case				:   Union[str, Any]       ,   ):





       '''simple docstring'''
       super().__init__(**__snake_case	)
       UpperCAmelCase_							:      Union[str, Any]						=   size if size is not None else {'''shortest_edge''': 224}
       UpperCAmelCase_							:      Any						=   get_size_dict(__snake_case       ,   default_to_square=__snake_case	)
       UpperCAmelCase_							:      Optional[Any]						=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       UpperCAmelCase_							:      int						=   get_size_dict(__snake_case       ,   default_to_square=__snake_case       ,   param_name='''crop_size'''	)

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





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   np.ndarray       ,   __snake_case				:   Dict[str, int]       ,   __snake_case				:   PILImageResampling = PILImageResampling.BICUBIC       ,   __snake_case				:   Optional[Union[str, ChannelDimension]] = None       ,   **__snake_case				:   Optional[int]       ,   ):





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





    def 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   np.ndarray       ,   __snake_case				:   Dict[str, int]       ,   __snake_case				:   Optional[Union[str, ChannelDimension]] = None       ,   **__snake_case				:   Tuple       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   get_size_dict(__snake_case	)
       if "height" not in size or "width" not in size:
          raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
       return center_crop(__snake_case       ,   size=(size['''height'''], size['''width'''])       ,   data_format=__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   np.ndarray       ,   __snake_case				:   Union[int, float]       ,   __snake_case				:   Optional[Union[str, ChannelDimension]] = None       ,   **__snake_case				:   Optional[Any]       ,   ):





       '''simple docstring'''
       return rescale(__snake_case       ,   scale=__snake_case       ,   data_format=__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[int]       ,   __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				:   Optional[Any]       ,   ):





       '''simple docstring'''
       return normalize(__snake_case       ,   mean=__snake_case       ,   std=__snake_case       ,   data_format=__snake_case       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   ImageInput       ,   __snake_case				:   bool = None       ,   __snake_case				:   Dict[str, int] = None       ,   __snake_case				:   PILImageResampling = None       ,   __snake_case				:   bool = None       ,   __snake_case				:   int = 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				:   bool = None       ,   __snake_case				:   Optional[Union[str, TensorType]] = None       ,   __snake_case				:   Optional[ChannelDimension] = ChannelDimension.FIRST       ,   **__snake_case				:   Tuple       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase_							:      Tuple						=   size if size is not None else self.size
       UpperCAmelCase_							:      Any						=   get_size_dict(__snake_case       ,   param_name='''size'''       ,   default_to_square=__snake_case	)
       UpperCAmelCase_							:      Optional[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_							:      Any						=   crop_size if crop_size is not None else self.crop_size
       UpperCAmelCase_							:      Dict						=   get_size_dict(__snake_case       ,   param_name='''crop_size'''       ,   default_to_square=__snake_case	)
       UpperCAmelCase_							:      Dict						=   do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase_							:      int						=   rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_							:      str						=   do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_							:      List[str]						=   image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase_							:      Tuple						=   image_std if image_std is not None else self.image_std
       UpperCAmelCase_							:      Optional[Any]						=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       UpperCAmelCase_							:      int						=   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:
          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_							:      Optional[int]						=   [convert_to_rgb(__snake_case	) for image in images]

       # All transformations expect numpy arrays.
       UpperCAmelCase_							:      Dict						=   [to_numpy_array(__snake_case	) for image in images]

       if do_resize:
          UpperCAmelCase_							:      Optional[int]						=   [self.resize(image=__snake_case       ,   size=__snake_case       ,   resample=__snake_case	) for image in images]

       if do_center_crop:
          UpperCAmelCase_							:      List[Any]						=   [self.center_crop(image=__snake_case       ,   size=__snake_case	) for image in images]

       if do_rescale:
          UpperCAmelCase_							:      Union[str, Any]						=   [self.rescale(image=__snake_case       ,   scale=__snake_case	) for image in images]

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

       UpperCAmelCase_							:      str						=   [to_channel_dimension_format(__snake_case       ,   __snake_case	) for image in images]

       UpperCAmelCase_							:      Union[str, Any]						=   {'''pixel_values''': images}
       return BatchFeature(data=__snake_case       ,   tensor_type=__snake_case	)







import fire

from utils import calculate_rouge, save_json
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       **__lowercase       ):
   UpperCAmelCase_							:      Tuple						=   [x.strip() for x in open(__lowercase       ).readlines()]
   UpperCAmelCase_							:      Dict						=   [x.strip() for x in open(__lowercase       ).readlines()][: len(__lowercase       )]
   UpperCAmelCase_							:      int						=   calculate_rouge(__lowercase			,       __lowercase			,       **__lowercase       )
   if save_path is not None:
      save_json(__lowercase			,       __lowercase			,       indent=__lowercase       )
   return metrics  # these print nicely


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