



import unittest

import numpy as np

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

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 DPTImageProcessor
class 			_snake_case     (   unittest.TestCase):




				def __init__(  self   :  int,   __lowercase   :  List[Any],   __lowercase   :  List[str]=7,   __lowercase   :  int=3,   __lowercase   :  List[Any]=18,   __lowercase   :  List[str]=30,   __lowercase   :  Dict=400,   __lowercase   :  List[Any]=True,   __lowercase   :  Dict=None,   __lowercase   :  List[Any]=True,   __lowercase   :  int=[0.5, 0.5, 0.5],   __lowercase   :  Dict=[0.5, 0.5, 0.5],   ):
							lowercase__   					=			size if size is not None else {"height": 18, "width": 18}
							lowercase__   					=			parent
							lowercase__   					=			batch_size
							lowercase__   					=			num_channels
							lowercase__   					=			image_size
							lowercase__   					=			min_resolution
							lowercase__   					=			max_resolution
							lowercase__   					=			do_resize
							lowercase__   					=			size
							lowercase__   					=			do_normalize
							lowercase__   					=			image_mean
							lowercase__   					=			image_std




				def 							A__ (  self   :  Optional[Any]  ):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}
@require_torch
@require_vision
class 			_snake_case     (   lowercase__      ,		unittest.TestCase):
				UpperCamelCase__			:   List[Any]       						=DPTImageProcessor if is_vision_available() else None




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			DPTImageProcessingTester(self  )




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__ (  self   :  Optional[int]  ):
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(__lowercase,   "image_mean"  )  )
							self.assertTrue(hasattr(__lowercase,   "image_std"  )  )
							self.assertTrue(hasattr(__lowercase,   "do_normalize"  )  )
							self.assertTrue(hasattr(__lowercase,   "do_resize"  )  )
							self.assertTrue(hasattr(__lowercase,   "size"  )  )




				def 							A__ (  self   :  Dict  ):
							lowercase__   					=			self.image_processing_class.from_dict(self.image_processor_dict  )
							self.assertEqual(image_processor.size,   {"height": 18, "width": 18}  )

							lowercase__   					=			self.image_processing_class.from_dict(self.image_processor_dict,   size=42  )
							self.assertEqual(image_processor.size,   {"height": 42, "width": 42}  )




				def 							A__ (  self   :  str  ):
							# Initialize image_processing
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowercase__   					=			prepare_image_inputs(self.image_processor_tester,   equal_resolution=__lowercase  )
							for image in image_inputs:
										self.assertIsInstance(__lowercase,   Image.Image  )

							# Test not batched input
							lowercase__   					=			image_processing(image_inputs[0],   return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_images.shape,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),   )

							# Test batched
							lowercase__   					=			image_processing(__lowercase,   return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_images.shape,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),   )




				def 							A__ (  self   :  Any  ):
							# Initialize image_processing
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowercase__   					=			prepare_image_inputs(self.image_processor_tester,   equal_resolution=__lowercase,   numpify=__lowercase  )
							for image in image_inputs:
										self.assertIsInstance(__lowercase,   np.ndarray  )

							# Test not batched input
							lowercase__   					=			image_processing(image_inputs[0],   return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_images.shape,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),   )

							# Test batched
							lowercase__   					=			image_processing(__lowercase,   return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_images.shape,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),   )




				def 							A__ (  self   :  Dict  ):
							# Initialize image_processing
							lowercase__   					=			self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowercase__   					=			prepare_image_inputs(self.image_processor_tester,   equal_resolution=__lowercase,   torchify=__lowercase  )
							for image in image_inputs:
										self.assertIsInstance(__lowercase,   torch.Tensor  )

							# Test not batched input
							lowercase__   					=			image_processing(image_inputs[0],   return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_images.shape,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),   )

							# Test batched
							lowercase__   					=			image_processing(__lowercase,   return_tensors="pt"  ).pixel_values
							self.assertEqual(
							    encoded_images.shape,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),   )




import fire

from utils import calculate_rouge, save_json



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


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




