


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_video_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import VivitImageProcessor





class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    def __init__(	self				:   List[Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Tuple=7       ,   __snake_case				:   int=3       ,   __snake_case				:   str=10       ,   __snake_case				:   Union[str, Any]=18       ,   __snake_case				:   Dict=30       ,   __snake_case				:   Union[str, Any]=400       ,   __snake_case				:   Optional[Any]=True       ,   __snake_case				:   Dict=None       ,   __snake_case				:   List[str]=True       ,   __snake_case				:   List[Any]=[0.5, 0.5, 0.5]       ,   __snake_case				:   int=[0.5, 0.5, 0.5]       ,   __snake_case				:   List[Any]=None       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   size if size is not None else {'''shortest_edge''': 18}
       UpperCAmelCase_							:      Any						=   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       UpperCAmelCase_							:      Optional[Any]						=   parent
       UpperCAmelCase_							:      Optional[Any]						=   batch_size
       UpperCAmelCase_							:      Optional[int]						=   num_channels
       UpperCAmelCase_							:      List[str]						=   num_frames
       UpperCAmelCase_							:      str						=   image_size
       UpperCAmelCase_							:      Dict						=   min_resolution
       UpperCAmelCase_							:      List[Any]						=   max_resolution
       UpperCAmelCase_							:      Any						=   do_resize
       UpperCAmelCase_							:      int						=   size
       UpperCAmelCase_							:      Tuple						=   do_normalize
       UpperCAmelCase_							:      List[str]						=   image_mean
       UpperCAmelCase_							:      Dict						=   image_std
       UpperCAmelCase_							:      str						=   crop_size





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }





@require_torch
@require_vision
class  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Optional[Any]			= VivitImageProcessor if is_vision_available() else None





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   VivitImageProcessingTester(self	)





    @property
    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       return self.image_processor_tester.prepare_image_processor_dict()





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[Any]						=   self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(__snake_case       ,   '''image_mean'''	)	)
       self.assertTrue(hasattr(__snake_case       ,   '''image_std'''	)	)
       self.assertTrue(hasattr(__snake_case       ,   '''do_normalize'''	)	)
       self.assertTrue(hasattr(__snake_case       ,   '''do_resize'''	)	)
       self.assertTrue(hasattr(__snake_case       ,   '''do_center_crop'''	)	)
       self.assertTrue(hasattr(__snake_case       ,   '''size'''	)	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size       ,   {'''shortest_edge''': 18}	)
       self.assertEqual(image_processor.crop_size       ,   {'''height''': 18, '''width''': 18}	)

       UpperCAmelCase_							:      str						=   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 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       # Initialize image_processing
       UpperCAmelCase_							:      List[Any]						=   self.image_processing_class(**self.image_processor_dict	)
       # create random PIL videos
       UpperCAmelCase_							:      Optional[int]						=   prepare_video_inputs(self.image_processor_tester       ,   equal_resolution=__snake_case	)
       for video in video_inputs:
          self.assertIsInstance(__snake_case       ,   __snake_case	)
          self.assertIsInstance(video[0]       ,   Image.Image	)

       # Test not batched input
       UpperCAmelCase_							:      List[Any]						=   image_processing(video_inputs[0]       ,   return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_videos.shape       ,   (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )       ,   )

       # Test batched
       UpperCAmelCase_							:      int						=   image_processing(__snake_case       ,   return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_videos.shape       ,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )       ,   )





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       # Initialize image_processing
       UpperCAmelCase_							:      Any						=   self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       UpperCAmelCase_							:      Optional[int]						=   prepare_video_inputs(self.image_processor_tester       ,   equal_resolution=__snake_case       ,   numpify=__snake_case	)
       for video in video_inputs:
          self.assertIsInstance(__snake_case       ,   __snake_case	)
          self.assertIsInstance(video[0]       ,   np.ndarray	)

       # Test not batched input
       UpperCAmelCase_							:      str						=   image_processing(video_inputs[0]       ,   return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_videos.shape       ,   (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )       ,   )

       # Test batched
       UpperCAmelCase_							:      Tuple						=   image_processing(__snake_case       ,   return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_videos.shape       ,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )       ,   )





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       # Initialize image_processing
       UpperCAmelCase_							:      Optional[Any]						=   self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       UpperCAmelCase_							:      List[Any]						=   prepare_video_inputs(self.image_processor_tester       ,   equal_resolution=__snake_case       ,   torchify=__snake_case	)
       for video in video_inputs:
          self.assertIsInstance(__snake_case       ,   __snake_case	)
          self.assertIsInstance(video[0]       ,   torch.Tensor	)

       # Test not batched input
       UpperCAmelCase_							:      Tuple						=   image_processing(video_inputs[0]       ,   return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_videos.shape       ,   (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )       ,   )

       # Test batched
       UpperCAmelCase_							:      Dict						=   image_processing(__snake_case       ,   return_tensors='''pt'''	).pixel_values
       self.assertEqual(
           encoded_videos.shape       ,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               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      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)