






'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel







@require_sentencepiece
@require_tokenizers
@require_torch
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							@slow
							def 							_A	(		self			:str				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       XLMRobertaModel.from_pretrained("xlm-roberta-base"				)
										snake_case_				:					Dict   =       torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]				)
										# The dog is cute and lives in the garden house

										snake_case_				:					List[str]   =       torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
										snake_case_				:					Union[str, Any]   =       torch.tensor(
										    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]				)
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
													snake_case_				:					Optional[int]   =       model(lowerCAmelCase__				)["last_hidden_state"].detach()
										self.assertEqual(output.shape       ,							lowerCAmelCase__				)
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]       ,							lowerCAmelCase__       ,							atol=1E-3				)				)






							@slow
							def 							_A	(		self			:Optional[int]				)				->  int:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       XLMRobertaModel.from_pretrained("xlm-roberta-large"				)
										snake_case_				:					Union[str, Any]   =       torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]				)
										# The dog is cute and lives in the garden house

										snake_case_				:					str   =       torch.Size((1, 12, 1_024)				)  # batch_size, sequence_length, embedding_vector_dim
										snake_case_				:					Optional[Any]   =       torch.tensor(
										    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]				)
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
													snake_case_				:					List[Any]   =       model(lowerCAmelCase__				)["last_hidden_state"].detach()
										self.assertEqual(output.shape       ,							lowerCAmelCase__				)
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]       ,							lowerCAmelCase__       ,							atol=1E-3				)				)












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


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




