
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 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=1_3    ,    __SCREAMING_SNAKE_CASE=7    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=9_9    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=5    ,    __SCREAMING_SNAKE_CASE=4    ,    __SCREAMING_SNAKE_CASE=3_7    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=5_0    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=None    ,    )	-> Dict:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : str											=					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__     : Optional[int]											=					ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size	)

           UpperCamelCase__     : Dict											=					self.get_config()

           return config, input_ids, input_mask, token_labels






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """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=__SCREAMING_SNAKE_CASE    ,    initializer_range=self.initializer_range    ,    )






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




           """simple docstring"""







           (
               (
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,
           )     : str											=					self.prepare_config_and_inputs()

           UpperCamelCase__     : Tuple											=					True
           UpperCamelCase__     : str											=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
           UpperCamelCase__     : Any											=					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    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Optional[Any]:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					True
           UpperCamelCase__     : Union[str, Any]											=					BertGenerationEncoder(config=__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Dict											=					model(
               __SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE    ,    )
           UpperCamelCase__     : List[str]											=					model(
               __SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,    )
           self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> str:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					True
           UpperCamelCase__     : str											=					True
           UpperCamelCase__     : Dict											=					BertGenerationDecoder(config=__SCREAMING_SNAKE_CASE	).to(__SCREAMING_SNAKE_CASE	).eval()

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

           # create hypothetical multiple next token and extent to next_input_ids
           UpperCamelCase__     : str											=					ids_tensor((self.batch_size, 3)    ,    config.vocab_size	)
           UpperCamelCase__     : Dict											=					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__     : Dict											=					torch.cat([input_mask, next_mask]    ,    dim=-1	)

           UpperCamelCase__     : str											=					model(
               __SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE    ,    output_hidden_states=__SCREAMING_SNAKE_CASE    ,    )['''hidden_states'''][0]
           UpperCamelCase__     : Union[str, Any]											=					model(
               __SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE    ,    encoder_attention_mask=__SCREAMING_SNAKE_CASE    ,    past_key_values=__SCREAMING_SNAKE_CASE    ,    output_hidden_states=__SCREAMING_SNAKE_CASE    ,    )['''hidden_states'''][0]

           # select random slice
           UpperCamelCase__     : List[str]											=					ids_tensor((1,)    ,    output_from_past.shape[-1]	).item()
           UpperCamelCase__     : Optional[int]											=					output_from_no_past[:, -3:, random_slice_idx].detach()
           UpperCamelCase__     : Optional[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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-3	)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    *__SCREAMING_SNAKE_CASE    ,    )	-> int:




           """simple docstring"""







           UpperCamelCase__     : str											=					BertGenerationDecoder(__SCREAMING_SNAKE_CASE	)
           model.to(__SCREAMING_SNAKE_CASE	)
           model.eval()
           UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE    ,    attention_mask=__SCREAMING_SNAKE_CASE    ,    labels=__SCREAMING_SNAKE_CASE	)
           self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.vocab_size)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Optional[int]											=					self.prepare_config_and_inputs()
           UpperCamelCase__     : List[str]											=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




@require_torch
class 					_lowerCamelCase			(   UpperCamelCase_						,				UpperCamelCase_						,				UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		 =	(BertGenerationDecoder,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		 =	(
         {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
         if is_torch_available()
         else {}
     )






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : str											=					BertGenerationEncoderTester(self	)
           UpperCamelCase__     : Optional[int]											=					ConfigTester(self    ,    config_class=__SCREAMING_SNAKE_CASE    ,    hidden_size=3_7	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           self.config_tester.run_common_tests()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : List[str]											=					self.model_tester.prepare_config_and_inputs()
           UpperCamelCase__     : Any											=					'''bert'''
           self.model_tester.create_and_check_model(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : str											=					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           (
               (
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,(
               UpperCamelCase__
           )					,
           )     : Tuple											=					self.model_tester.prepare_config_and_inputs_for_decoder()

           UpperCamelCase__     : List[str]											=					None

           self.model_tester.create_and_check_model_as_decoder(
               __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    )






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




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_for_causal_lm(*__SCREAMING_SNAKE_CASE	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
           UpperCamelCase__     : Any											=					torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]	)
           with torch.no_grad():
                 UpperCamelCase__     : List[Any]											=					model(__SCREAMING_SNAKE_CASE	)[0]
           UpperCamelCase__     : List[str]											=					torch.Size([1, 8, 1_0_2_4]	)
           self.assertEqual(output.shape    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					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]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-4	)	)




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''	)
           UpperCamelCase__     : str											=					torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]	)
           with torch.no_grad():
                 UpperCamelCase__     : Any											=					model(__SCREAMING_SNAKE_CASE	)[0]
           UpperCamelCase__     : List[Any]											=					torch.Size([1, 8, 5_0_3_5_8]	)
           self.assertEqual(output.shape    ,    __SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[int]											=					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]    ,    __SCREAMING_SNAKE_CASE    ,    atol=1e-4	)	)




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):

      def get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		) -> str:
            UpperCamelCase__     : Union[str, Any]											=					[]
            UpperCamelCase__     : Union[str, Any]											=					min(len(_stra		)			,		len(_stra		)		) // 2
            for i, l in enumerate(_stra		):
                  UpperCamelCase__     : Optional[int]											=					int(max(0			,		i - limit		)		)
                  UpperCamelCase__     : List[str]											=					int(min(i + limit + 1			,		len(_stra		)		)		)
                  if l in _stra[left:right]:
                        matched.append(UpperCamelCase__		)
                        UpperCamelCase__     : List[str]											=					f'''{_stra[0:_stra.index(UpperCamelCase__		)]} {_stra[_stra.index(UpperCamelCase__		) + 1:]}'''

            return "".join(UpperCamelCase__		)

      # matching characters
      UpperCamelCase__     : Any											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : Union[str, Any]											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : List[Any]											=					len(UpperCamelCase__		)

      # transposition
      UpperCamelCase__     : str											=					(
          len([(ca, ca) for ca, ca in zip(UpperCamelCase__			,		UpperCamelCase__		) if ca != ca]		) // 2
      )

      if not match_count:
            UpperCamelCase__     : Union[str, Any]											=					0.0
      else:
            UpperCamelCase__     : Optional[int]											=					(
                1
                / 3
                * (
                    match_count / len(UpperCamelCase__		)
                    + match_count / len(UpperCamelCase__		)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      UpperCamelCase__     : Dict											=					0
      for ca, ca in zip(stra[:4]			,		stra[:4]		):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(jaro_winkler("hello", "world"))


