


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class  lowerCAmelCase__(    snake_case__    ,    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Optional[int]			= VQModel
    A_  :     Optional[int]			= 'sample'





    @property
    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Union[str, Any]=(32, 32)	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   4
       UpperCAmelCase_							:      List[str]						=   3

       UpperCAmelCase_							:      Tuple						=   floats_tensor((batch_size, num_channels) + sizes	).to(__snake_case	)

       return {"sample": image}





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       return (3, 32, 32)





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       return (3, 32, 32)





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   {
           '''block_out_channels''': [32, 64],
           '''in_channels''': 3,
           '''out_channels''': 3,
           '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
           '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
           '''latent_channels''': 3,
       }
       UpperCAmelCase_							:      Union[str, Any]						=   self.dummy_input
       return init_dict, inputs_dict





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       pass





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       pass





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_		,      UpperCAmelCase_							:      Union[str, Any]						=   VQModel.from_pretrained('''fusing/vqgan-dummy'''       ,   output_loading_info=__snake_case	)
       self.assertIsNotNone(__snake_case	)
       self.assertEqual(len(loading_info['''missing_keys''']	)       ,   0	)

       model.to(__snake_case	)
       UpperCAmelCase_							:      Dict						=   model(**self.dummy_input	)

       assert image is not None, "Make sure output is not None"





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   VQModel.from_pretrained('''fusing/vqgan-dummy'''	)
       model.to(__snake_case	).eval()

       torch.manual_seed(0	)
       if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0	)

       UpperCAmelCase_							:      Union[str, Any]						=   torch.randn(1       ,   model.config.in_channels       ,   model.config.sample_size       ,   model.config.sample_size	)
       UpperCAmelCase_							:      List[Any]						=   image.to(__snake_case	)
       with torch.no_grad():
          UpperCAmelCase_							:      Union[str, Any]						=   model(__snake_case	).sample

       UpperCAmelCase_							:      Tuple						=   output[0, -1, -3:, -3:].flatten().cpu()
       # fmt: off
       UpperCAmelCase_							:      Dict						=   torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]	)
       # fmt: on
       self.assertTrue(torch.allclose(__snake_case       ,   __snake_case       ,   atol=1E-3	)	)



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)