

import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import BertGenerationDecoder, BertGenerationEncoder

class 				a  :


						def __init__(       self						,					A_						,					A_=13						,					A_=7						,					A_=True						,					A_=True						,					A_=99						,					A_=32						,					A_=5						,					A_=4						,					A_=37						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=50						,					A_=0.02						,					A_=True						,					A_=None						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    parent
										_UpperCAmelCase       :		Optional[int] 		=    batch_size
										_UpperCAmelCase       :		Union[str, Any] 		=    seq_length
										_UpperCAmelCase       :		Dict 		=    is_training
										_UpperCAmelCase       :		List[str] 		=    use_input_mask
										_UpperCAmelCase       :		str 		=    vocab_size
										_UpperCAmelCase       :		List[Any] 		=    hidden_size
										_UpperCAmelCase       :		Optional[int] 		=    num_hidden_layers
										_UpperCAmelCase       :		Dict 		=    num_attention_heads
										_UpperCAmelCase       :		Dict 		=    intermediate_size
										_UpperCAmelCase       :		Dict 		=    hidden_act
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[int] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Optional[int] 		=    max_position_embeddings
										_UpperCAmelCase       :		Any 		=    initializer_range
										_UpperCAmelCase       :		Optional[int] 		=    use_labels
										_UpperCAmelCase       :		Dict 		=    scope


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										_UpperCAmelCase       :		List[Any] 		=    None
										if self.use_input_mask:
														_UpperCAmelCase       :		Tuple 		=    random_attention_mask([self.batch_size, self.seq_length]	)

										if self.use_labels:
														_UpperCAmelCase       :		Any 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										_UpperCAmelCase       :		Tuple 		=    self.get_config()

										return config, input_ids, input_mask, token_labels


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return BertGenerationConfig(
										    vocab_size=self.vocab_size						,					hidden_size=self.hidden_size						,					num_hidden_layers=self.num_hidden_layers						,					num_attention_heads=self.num_attention_heads						,					intermediate_size=self.intermediate_size						,					hidden_act=self.hidden_act						,					hidden_dropout_prob=self.hidden_dropout_prob						,					attention_probs_dropout_prob=self.attention_probs_dropout_prob						,					max_position_embeddings=self.max_position_embeddings						,					is_decoder=A_						,					initializer_range=self.initializer_range						,					)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										(
										    (
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							
										)       :		Tuple 		=    self.prepare_config_and_inputs()

										_UpperCAmelCase       :		Tuple 		=    True
										_UpperCAmelCase       :		Union[str, Any] 		=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
										_UpperCAmelCase       :		Dict 		=    ids_tensor([self.batch_size, self.seq_length]						,					vocab_size=2	)

										return (
										    config,
										    input_ids,
										    input_mask,
										    token_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    BertGenerationEncoder(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Dict 		=    model(A_						,					attention_mask=A_	)
										_UpperCAmelCase       :		List[Any] 		=    model(A_	)
										self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    True
										_UpperCAmelCase       :		List[Any] 		=    BertGenerationEncoder(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Tuple 		=    model(
										    A_						,					attention_mask=A_						,					encoder_hidden_states=A_						,					encoder_attention_mask=A_						,					)
										_UpperCAmelCase       :		Optional[Any] 		=    model(
										    A_						,					attention_mask=A_						,					encoder_hidden_states=A_						,					)
										self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    True
										_UpperCAmelCase       :		int 		=    True
										_UpperCAmelCase       :		Union[str, Any] 		=    BertGenerationDecoder(config=A_	).to(A_	).eval()

										# first forward pass
										_UpperCAmelCase       :		List[str] 		=    model(
										    A_						,					attention_mask=A_						,					encoder_hidden_states=A_						,					encoder_attention_mask=A_						,					use_cache=A_						,					)
										_UpperCAmelCase       :		Dict 		=    outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										_UpperCAmelCase       :		Optional[int] 		=    ids_tensor((self.batch_size, 3)						,					config.vocab_size	)
										_UpperCAmelCase       :		List[str] 		=    ids_tensor((self.batch_size, 3)						,					vocab_size=2	)

										# append to next input_ids and
										_UpperCAmelCase       :		List[str] 		=    torch.cat([input_ids, next_tokens]						,					dim=-1	)
										_UpperCAmelCase       :		Union[str, Any] 		=    torch.cat([input_mask, next_mask]						,					dim=-1	)

										_UpperCAmelCase       :		Optional[Any] 		=    model(
										    A_						,					attention_mask=A_						,					encoder_hidden_states=A_						,					encoder_attention_mask=A_						,					output_hidden_states=A_						,					)["hidden_states"][0]
										_UpperCAmelCase       :		List[Any] 		=    model(
										    A_						,					attention_mask=A_						,					encoder_hidden_states=A_						,					encoder_attention_mask=A_						,					past_key_values=A_						,					output_hidden_states=A_						,					)["hidden_states"][0]

										# select random slice
										_UpperCAmelCase       :		Dict 		=    ids_tensor((1,)						,					output_from_past.shape[-1]	).item()
										_UpperCAmelCase       :		List[str] 		=    output_from_no_past[:, -3:, random_slice_idx].detach()
										_UpperCAmelCase       :		str 		=    output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(A_						,					A_						,					atol=1e-3	)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					*A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    BertGenerationDecoder(A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Optional[Any] 		=    model(A_						,					attention_mask=A_						,					labels=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    self.prepare_config_and_inputs()
										_UpperCAmelCase       :		Any 		=    {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_torch
class 				a  (	UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
						_lowercase    						=			(BertGenerationDecoder,) if is_torch_available() else ()
						_lowercase    						=			(
						    {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
						    if is_torch_available()
						    else {}
						)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    BertGenerationEncoderTester(self	)
										_UpperCAmelCase       :		str 		=    ConfigTester(self						,					config_class=A_						,					hidden_size=37	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    self.model_tester.prepare_config_and_inputs()
										_UpperCAmelCase       :		Union[str, Any] 		=    "bert"
										self.model_tester.create_and_check_model(A_						,					A_						,					A_						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										(
										    (
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							(
										    _UpperCAmelCase
										)		,							
										)       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs_for_decoder()

										_UpperCAmelCase       :		Optional[Any] 		=    None

										self.model_tester.create_and_check_model_as_decoder(
										    A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_for_causal_lm(*A_	)





						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"	)
										self.assertIsNotNone(A_	)







@require_torch
class 				a  (	unittest.TestCase				):


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"	)
										_UpperCAmelCase       :		int 		=    torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]	)
										with torch.no_grad():
														_UpperCAmelCase       :		Optional[Any] 		=    model(A_	)[0]
										_UpperCAmelCase       :		List[str] 		=    torch.Size([1, 8, 1024]	)
										self.assertEqual(output.shape						,					A_	)
										_UpperCAmelCase       :		Optional[int] 		=    torch.tensor(
										    [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]]	)
										self.assertTrue(torch.allclose(output[:, :3, :3]						,					A_						,					atol=1e-4	)	)







@require_torch
class 				a  (	unittest.TestCase				):


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"	)
										_UpperCAmelCase       :		Dict 		=    torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]	)
										with torch.no_grad():
														_UpperCAmelCase       :		Union[str, Any] 		=    model(A_	)[0]
										_UpperCAmelCase       :		Optional[Any] 		=    torch.Size([1, 8, 50358]	)
										self.assertEqual(output.shape						,					A_	)
										_UpperCAmelCase       :		Any 		=    torch.tensor(
										    [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]]	)
										self.assertTrue(torch.allclose(output[:, :3, :3]						,					A_						,					atol=1e-4	)	)





import fire

from utils import calculate_rouge, save_json

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  Any )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()]
				_UpperCAmelCase       :		str 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()][: len(lowerCAmelCase )]
				_UpperCAmelCase       :		str 		=    calculate_rouge(lowerCAmelCase  ,      lowerCAmelCase  ,      **lowerCAmelCase )
				if save_path is not None:
								save_json(lowerCAmelCase  ,      lowerCAmelCase  ,      indent=lowerCAmelCase )
				return metrics  # these print nicely


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


