
from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCamelCase_   	=	logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase					)
class       a		(   __UpperCAmelCase					):







							def __init__(						self  :  List[Any]      ,				*snake_case__  :  Dict      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								super().__init__(*snake_case__      ,				**snake_case__ )
								requires_backends(self      ,				"vision" )
								self.check_model_type(snake_case__ )







							def __call__(						self  :  int      ,				snake_case__  :  Union[str, List[str], "Image.Image", List["Image.Image"]]      ,				**snake_case__  :  int ):
								"""simple docstring"""







								return super().__call__(snake_case__      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Dict      ,				**snake_case__  :  Optional[int] ):
								"""simple docstring"""







								return {}, {}, {}







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							load_image(snake_case__ )
								__lowerCAmelCase         =							image.size
								__lowerCAmelCase         =							self.image_processor(images=snake_case__      ,				return_tensors=self.framework )
								return model_inputs







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.model(**snake_case__ )
								return model_outputs







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							model_outputs.predicted_depth
								__lowerCAmelCase         =							torch.nn.functional.interpolate(
								    predicted_depth.unsqueeze(1 )      ,				size=self.image_size[::-1]      ,				mode="bicubic"      ,				align_corners=snake_case__ )
								__lowerCAmelCase         =							prediction.squeeze().cpu().numpy()
								__lowerCAmelCase         =							(output * 255 / np.max(snake_case__ )).astype("uint8" )
								__lowerCAmelCase         =							Image.fromarray(snake_case__ )
								__lowerCAmelCase         =							{}
								__lowerCAmelCase         =							predicted_depth
								__lowerCAmelCase         =							depth
								return output_dict


import fire

from utils import calculate_rouge, save_json






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     List[Any]=None			,							**UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()]
	__lowerCAmelCase         =							[x.strip() for x in open(UpperCamelCase  ).readlines()][: len(UpperCamelCase  )]
	__lowerCAmelCase         =							calculate_rouge(UpperCamelCase			,							UpperCamelCase			,							**UpperCamelCase  )
	if save_path is not None:
		save_json(UpperCamelCase			,							UpperCamelCase			,							indent=UpperCamelCase  )
	return metrics  # these print nicely


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






