
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 PoolFormerImageProcessor

class       a		(   unittest.TestCase					):







							def __init__(						self  :  int      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Tuple=7      ,				snake_case__  :  Union[str, Any]=3      ,				snake_case__  :  List[str]=30      ,				snake_case__  :  Union[str, Any]=400      ,				snake_case__  :  List[Any]=True      ,				snake_case__  :  Tuple=None      ,				snake_case__  :  Optional[int]=0.9      ,				snake_case__  :  int=None      ,				snake_case__  :  List[str]=True      ,				snake_case__  :  Dict=[0.5, 0.5, 0.5]      ,				snake_case__  :  int=[0.5, 0.5, 0.5]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							size if size is not None else {"shortest_edge": 30}
								__lowerCAmelCase         =							crop_size if crop_size is not None else {"height": 30, "width": 30}
								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							batch_size
								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							min_resolution
								__lowerCAmelCase         =							max_resolution
								__lowerCAmelCase         =							do_resize_and_center_crop
								__lowerCAmelCase         =							size
								__lowerCAmelCase         =							crop_pct
								__lowerCAmelCase         =							crop_size
								__lowerCAmelCase         =							do_normalize
								__lowerCAmelCase         =							image_mean
								__lowerCAmelCase         =							image_std







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







								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}

@require_torch
@require_vision
class       a		(   snake_case__      ,	unittest.TestCase					):
							lowercase_		:						Dict   							=  PoolFormerImageProcessor if is_vision_available() else None







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







								__lowerCAmelCase         =							PoolFormerImageProcessingTester(self )







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return self.image_processor_tester.prepare_image_processor_dict()







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







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								self.assertTrue(hasattr(UpperCAmelCase_      ,				"do_resize_and_center_crop" ) )
								self.assertTrue(hasattr(UpperCAmelCase_      ,				"size" ) )
								self.assertTrue(hasattr(UpperCAmelCase_      ,				"crop_pct" ) )
								self.assertTrue(hasattr(UpperCAmelCase_      ,				"do_normalize" ) )
								self.assertTrue(hasattr(UpperCAmelCase_      ,				"image_mean" ) )
								self.assertTrue(hasattr(UpperCAmelCase_      ,				"image_std" ) )







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class.from_dict(self.image_processor_dict )
								self.assertEqual(image_processor.size      ,				{"shortest_edge": 30} )
								self.assertEqual(image_processor.crop_size      ,				{"height": 30, "width": 30} )

								__lowerCAmelCase         =							self.image_processing_class.from_dict(self.image_processor_dict      ,				size=42      ,				crop_size=84 )
								self.assertEqual(image_processor.size      ,				{"shortest_edge": 42} )
								self.assertEqual(image_processor.crop_size      ,				{"height": 84, "width": 84} )







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







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

								# Test not batched input
								__lowerCAmelCase         =							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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )      ,				)

								# Test batched
								__lowerCAmelCase         =							image_processing(UpperCAmelCase_      ,				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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )      ,				)







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







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								# create random numpy tensors
								__lowerCAmelCase         =							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
								__lowerCAmelCase         =							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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )      ,				)

								# Test batched
								__lowerCAmelCase         =							image_processing(UpperCAmelCase_      ,				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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )      ,				)







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processing_class(**self.image_processor_dict )
								# create random PyTorch tensors
								__lowerCAmelCase         =							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
								__lowerCAmelCase         =							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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )      ,				)

								# Test batched
								__lowerCAmelCase         =							image_processing(UpperCAmelCase_      ,				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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )      ,				)


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)






