



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor





class  __magic_name__   (							unittest.TestCase						):

		'''simple docstring'''





		def __init__(						self      :     str				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     List[Any]=7				,		UpperCamelCase__      :     int=3				,		UpperCamelCase__      :     Any=18				,		UpperCamelCase__      :     Dict=30				,		UpperCamelCase__      :     Optional[Any]=4_00				,		UpperCamelCase__      :     Tuple=True				,		UpperCamelCase__      :     Dict=32				,		UpperCamelCase__      :     Any=True				,		)				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     parent
								UpperCAmelCase         =     batch_size
								UpperCAmelCase         =     num_channels
								UpperCAmelCase         =     image_size
								UpperCAmelCase         =     min_resolution
								UpperCAmelCase         =     max_resolution
								UpperCAmelCase         =     do_resize
								UpperCAmelCase         =     size_divisor
								UpperCAmelCase         =     do_rescale







		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
								'''simple docstring'''







								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}





@require_torch
@require_vision
class  __magic_name__   (							A__,  unittest.TestCase						):

		'''simple docstring'''





		lowercase      :       int          =GLPNImageProcessor if is_vision_available() else None






		def SCREAMING_SNAKE_CASE_			(						self      :     Any      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     GLPNImageProcessingTester(self      )






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     Dict      )				->					Dict:
								'''simple docstring'''







								return self.image_processor_tester.prepare_image_processor_dict()






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase__				,		"do_rescale"      )      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					Union[str, Any]:
								'''simple docstring'''







								pass






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								UpperCAmelCase         =     prepare_image_inputs(self.image_processor_tester				,		equal_resolution=UpperCamelCase__      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase__				,		Image.Image      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								UpperCAmelCase         =     image_processing(image_inputs[0]				,		return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Tuple:
								'''simple docstring'''







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								UpperCAmelCase         =     prepare_image_inputs(self.image_processor_tester				,		equal_resolution=UpperCamelCase__				,		numpify=UpperCamelCase__      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase__				,		np.ndarray      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								UpperCAmelCase         =     image_processing(image_inputs[0]				,		return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								UpperCAmelCase         =     prepare_image_inputs(self.image_processor_tester				,		equal_resolution=UpperCamelCase__				,		torchify=UpperCamelCase__      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase__				,		torch.Tensor      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								UpperCAmelCase         =     image_processing(image_inputs[0]				,		return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )








import fire

from utils import calculate_rouge, save_json

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=None    ,   **lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()]
						UpperCAmelCase         =     [x.strip() for x in open(lowerCamelCase_    ).readlines()][: len(lowerCamelCase_    )]
						UpperCAmelCase         =     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)



