




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        __snake_case  :



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		List[Any]=13		,	_UpperCAmelCase      :		Optional[int]=7		,	_UpperCAmelCase      :		List[str]=True		,	_UpperCAmelCase      :		Union[str, Any]=True		,	_UpperCAmelCase      :		Optional[Any]=99		,	_UpperCAmelCase      :		List[str]=32		,	_UpperCAmelCase      :		Tuple=5		,	_UpperCAmelCase      :		Tuple=4		,	_UpperCAmelCase      :		str=37		,	_UpperCAmelCase      :		int="gelu"		,	_UpperCAmelCase      :		List[Any]=0.1		,	_UpperCAmelCase      :		Any=0.1		,	_UpperCAmelCase      :		Union[str, Any]=50		,	_UpperCAmelCase      :		int=0.02		,	_UpperCAmelCase      :		int=True		,	_UpperCAmelCase      :		Optional[Any]=None		,	)  ->    List[str]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size      )

								_lowerCAmelCase    :    Optional[int]										=      None
								if self.use_input_mask:
												_lowerCAmelCase    :    Optional[Any]										=      random_attention_mask([self.batch_size, self.seq_length]      )

								if self.use_labels:
												_lowerCAmelCase    :    Tuple										=      ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size      )

								_lowerCAmelCase    :    Tuple										=      self.get_config()

								return config, input_ids, input_mask, token_labels



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Dict:





								'''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=_UpperCAmelCase		,	initializer_range=self.initializer_range		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Union[str, Any]:





								'''simple docstring'''




								(
								    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    
								)    :    List[Any]										=      self.prepare_config_and_inputs()

								_lowerCAmelCase    :    str										=      True
								_lowerCAmelCase    :    int										=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
								_lowerCAmelCase    :    Tuple										=      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       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		Any		,	**_UpperCAmelCase      :		Tuple		,	)  ->    Any:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		Any		,	**_UpperCAmelCase      :		List[str]		,	)  ->    Union[str, Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		int		,	**_UpperCAmelCase      :		str		,	)  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      True
								_lowerCAmelCase    :    Any										=      True
								_lowerCAmelCase    :    Optional[int]										=      BertGenerationDecoder(config=_UpperCAmelCase      ).to(_UpperCAmelCase      ).eval()

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

								# create hypothetical multiple next token and extent to next_input_ids
								_lowerCAmelCase    :    Dict										=      ids_tensor((self.batch_size, 3)		,	config.vocab_size      )
								_lowerCAmelCase    :    List[Any]										=      ids_tensor((self.batch_size, 3)		,	vocab_size=2      )

								# append to next input_ids and
								_lowerCAmelCase    :    Dict										=      torch.cat([input_ids, next_tokens]		,	dim=-1      )
								_lowerCAmelCase    :    str										=      torch.cat([input_mask, next_mask]		,	dim=-1      )

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

								# select random slice
								_lowerCAmelCase    :    List[Any]										=      ids_tensor((1,)		,	output_from_past.shape[-1]      ).item()
								_lowerCAmelCase    :    Optional[Any]										=      output_from_no_past[:, -3:, random_slice_idx].detach()
								_lowerCAmelCase    :    int										=      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(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1E-3      )      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Union[str, Any]		,	*_UpperCAmelCase      :		Optional[Any]		,	)  ->    str:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[int]:





								'''simple docstring'''




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






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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      BertGenerationEncoderTester(self      )
								_lowerCAmelCase    :    Dict										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	hidden_size=37      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Dict:





								'''simple docstring'''




								self.config_tester.run_common_tests()



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    str										=      self.model_tester.prepare_config_and_inputs()
								_lowerCAmelCase    :    Dict										=      """bert"""
								self.model_tester.create_and_check_model(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Union[str, Any]:





								'''simple docstring'''




								(
								    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    (
								    _lowerCAmelCase
								)					,    
								)    :    Union[str, Any]										=      self.model_tester.prepare_config_and_inputs_for_decoder()

								_lowerCAmelCase    :    str										=      None

								self.model_tester.create_and_check_model_as_decoder(
								    _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    List[str]:





								'''simple docstring'''




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



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    List[str]:





								'''simple docstring'''




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






@require_torch
class        __snake_case  (unittest.TestCase		):



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""      )
								_lowerCAmelCase    :    Union[str, Any]										=      torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]      )
								with torch.no_grad():
												_lowerCAmelCase    :    Any										=      model(_UpperCAmelCase      )[0]
								_lowerCAmelCase    :    Dict										=      torch.Size([1, 8, 1024]      )
								self.assertEqual(output.shape		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      torch.tensor(
								    [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]      )
								self.assertTrue(torch.allclose(output[:, :3, :3]		,	_UpperCAmelCase		,	atol=1E-4      )      )






@require_torch
class        __snake_case  (unittest.TestCase		):



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""      )
								_lowerCAmelCase    :    Tuple										=      torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]      )
								with torch.no_grad():
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase      )[0]
								_lowerCAmelCase    :    Optional[int]										=      torch.Size([1, 8, 5_0358]      )
								self.assertEqual(output.shape		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      torch.tensor(
								    [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]      )
								self.assertTrue(torch.allclose(output[:, :3, :3]		,	_UpperCAmelCase		,	atol=1E-4      )      )







def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      [0] * len(UpperCamelCase_       )

				for i in range(1      ,    len(UpperCamelCase_       )       ):
								# use last results for better performance - dynamic programming
								_lowerCAmelCase    :    Union[str, Any]										=      prefix_result[i - 1]
								while j > 0 and input_string[i] != input_string[j]:
												_lowerCAmelCase    :    str										=      prefix_result[j - 1]

								if input_string[i] == input_string[j]:
												j += 1
								_lowerCAmelCase    :    Optional[int]										=      j

				return prefix_result




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				return max(prefix_function(UpperCamelCase_       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

