


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:
       def __init__(   self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=99, lowerCamelCase=32, lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=37, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=50, lowerCamelCase=0.0_2, lowerCamelCase=True, lowerCamelCase=None, )			->  Tuple:




           """simple docstring"""

           _lowercase			:				Tuple 						=						parent
           _lowercase			:				Optional[Any] 						=						batch_size
           _lowercase			:				List[str] 						=						seq_length
           _lowercase			:				Optional[Any] 						=						is_training
           _lowercase			:				Union[str, Any] 						=						use_input_mask
           _lowercase			:				str 						=						vocab_size
           _lowercase			:				Union[str, Any] 						=						hidden_size
           _lowercase			:				Tuple 						=						num_hidden_layers
           _lowercase			:				Union[str, Any] 						=						num_attention_heads
           _lowercase			:				Optional[Any] 						=						intermediate_size
           _lowercase			:				List[Any] 						=						hidden_act
           _lowercase			:				Union[str, Any] 						=						hidden_dropout_prob
           _lowercase			:				Dict 						=						attention_probs_dropout_prob
           _lowercase			:				str 						=						max_position_embeddings
           _lowercase			:				Any 						=						initializer_range
           _lowercase			:				List[str] 						=						use_labels
           _lowercase			:				Tuple 						=						scope
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

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

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

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

           _lowercase			:				Optional[Any] 						=						self.get_config()

           return config, input_ids, input_mask, token_labels
       def      UpperCamelCase	(   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=lowerCamelCase, initializer_range=self.initializer_range, )
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

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

           _lowercase			:				Any 						=						True
           _lowercase			:				str 						=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           _lowercase			:				List[str] 						=						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, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase, )			->  Any:




           """simple docstring"""

           _lowercase			:				Dict 						=						BertGenerationEncoder(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				List[Any] 						=						model(lowerCamelCase, attention_mask=lowerCamelCase)
           _lowercase			:				List[Any] 						=						model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase, )			->  Any:




           """simple docstring"""

           _lowercase			:				int 						=						True
           _lowercase			:				Any 						=						BertGenerationEncoder(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				List[Any] 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, encoder_hidden_states=lowerCamelCase, encoder_attention_mask=lowerCamelCase, )
           _lowercase			:				Optional[int] 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, encoder_hidden_states=lowerCamelCase, )
           self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase, )			->  int:




           """simple docstring"""

           _lowercase			:				str 						=						True
           _lowercase			:				str 						=						True
           _lowercase			:				Union[str, Any] 						=						BertGenerationDecoder(config=lowerCamelCase).to(lowerCamelCase).eval()

           # first forward pass
           _lowercase			:				Tuple 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, encoder_hidden_states=lowerCamelCase, encoder_attention_mask=lowerCamelCase, use_cache=lowerCamelCase, )
           _lowercase			:				Optional[Any] 						=						outputs.past_key_values

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

           # append to next input_ids and
           _lowercase			:				Optional[Any] 						=						torch.cat([input_ids, next_tokens], dim=-1)
           _lowercase			:				Any 						=						torch.cat([input_mask, next_mask], dim=-1)

           _lowercase			:				Union[str, Any] 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, encoder_hidden_states=lowerCamelCase, encoder_attention_mask=lowerCamelCase, output_hidden_states=lowerCamelCase, )['hidden_states'][0]
           _lowercase			:				Tuple 						=						model(
               lowerCamelCase, attention_mask=lowerCamelCase, encoder_hidden_states=lowerCamelCase, encoder_attention_mask=lowerCamelCase, past_key_values=lowerCamelCase, output_hidden_states=lowerCamelCase, )['hidden_states'][0]

           # select random slice
           _lowercase			:				str 						=						ids_tensor((1,), output_from_past.shape[-1]).item()
           _lowercase			:				List[Any] 						=						output_from_no_past[:, -3:, random_slice_idx].detach()
           _lowercase			:				List[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(lowerCamelCase, lowerCamelCase, atol=1E-3))
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, *lowerCamelCase, )			->  List[str]:




           """simple docstring"""

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




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




           """simple docstring"""

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






@require_torch
class _lowerCamelCase(		_a,					_a,					_a,					unittest.TestCase  ):
       lowercase_					:      str       					=     (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
       lowercase_					:      Optional[Any]       					=     (BertGenerationDecoder,) if is_torch_available() else ()
       lowercase_					:      List[Any]       					=     (
           {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
           if is_torch_available()
           else {}
       )
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						BertGenerationEncoderTester(self)
           _lowercase			:				int 						=						ConfigTester(self, config_class=lowerCamelCase, hidden_size=37)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           self.config_tester.run_common_tests()
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				str 						=						self.model_tester.prepare_config_and_inputs()
           _lowercase			:				Optional[Any] 						=						'bert'
           self.model_tester.create_and_check_model(lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				str 						=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Any 						=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

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

           _lowercase			:				Optional[Any] 						=						None

           self.model_tester.create_and_check_model_as_decoder(
               lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, )
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				int 						=						self.model_tester.prepare_config_and_inputs_for_decoder()
           self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase)




       @slow
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
           self.assertIsNotNone(lowerCamelCase)






@require_torch
class _lowerCamelCase(		unittest.TestCase  ):
       @slow
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
           _lowercase			:				List[str] 						=						torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]])
           with torch.no_grad():
               _lowercase			:				Optional[Any] 						=						model(lowerCamelCase)[0]
           _lowercase			:				Dict 						=						torch.Size([1, 8, 10_24])
           self.assertEqual(output.shape, lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						torch.tensor(
               [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]])
           self.assertTrue(torch.allclose(output[:, :3, :3], lowerCamelCase, atol=1E-4))






@require_torch
class _lowerCamelCase(		unittest.TestCase  ):
       @slow
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
           _lowercase			:				List[Any] 						=						torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]])
           with torch.no_grad():
               _lowercase			:				Dict 						=						model(lowerCamelCase)[0]
           _lowercase			:				int 						=						torch.Size([1, 8, 5_03_58])
           self.assertEqual(output.shape, lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						torch.tensor(
               [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]])
           self.assertTrue(torch.allclose(output[:, :3, :3], lowerCamelCase, atol=1E-4))






from __future__ import annotations

import numpy as np



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    return np.maximum(0				,					lowerCamelCase_					)


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


