


"""simple docstring"""


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 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   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__=0.02 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     )      ->							Any:
    lowercase__       :       List[Any]				=						parent
    lowercase__       :       Tuple				=						batch_size
    lowercase__       :       Optional[int]				=						seq_length
    lowercase__       :       Union[str, Any]				=						is_training
    lowercase__       :       Tuple				=						use_input_mask
    lowercase__       :       Dict				=						vocab_size
    lowercase__       :       Any				=						hidden_size
    lowercase__       :       Optional[Any]				=						num_hidden_layers
    lowercase__       :       str				=						num_attention_heads
    lowercase__       :       int				=						intermediate_size
    lowercase__       :       int				=						hidden_act
    lowercase__       :       Optional[Any]				=						hidden_dropout_prob
    lowercase__       :       Any				=						attention_probs_dropout_prob
    lowercase__       :       Any				=						max_position_embeddings
    lowercase__       :       List[Any]				=						initializer_range
    lowercase__       :       int				=						use_labels
    lowercase__       :       int				=						scope


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       Any				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

    lowercase__       :       Dict				=						None
    if self.use_input_mask:
     lowercase__       :       str				=						random_attention_mask([self.batch_size, self.seq_length]						)

    if self.use_labels:
     lowercase__       :       int				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

    lowercase__       :       List[str]				=						self.get_config()

    return config, input_ids, input_mask, token_labels


   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    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						)      ->							Optional[Any]:
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       str				=						self.prepare_config_and_inputs()

    lowercase__       :       str				=						True
    lowercase__       :       List[Any]				=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
    lowercase__       :       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 ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     **lowerCamelCase__ ,     )      ->							Any:
    lowercase__       :       Dict				=						BertGenerationEncoder(config=lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       List[Any]				=						model(lowerCamelCase__ ,     attention_mask=lowerCamelCase__						)
    lowercase__       :       List[str]				=						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__ ,     )      ->							Dict:
    lowercase__       :       List[str]				=						True
    lowercase__       :       int				=						BertGenerationEncoder(config=lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       Optional[Any]				=						model(
        lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     encoder_hidden_states=lowerCamelCase__ ,     encoder_attention_mask=lowerCamelCase__ ,     )
    lowercase__       :       Optional[Any]				=						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:
    lowercase__       :       Dict				=						True
    lowercase__       :       Union[str, Any]				=						True
    lowercase__       :       Optional[int]				=						BertGenerationDecoder(config=lowerCamelCase__						).to(lowerCamelCase__						).eval()

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

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

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

    lowercase__       :       Optional[Any]				=						model(
        lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     encoder_hidden_states=lowerCamelCase__ ,     encoder_attention_mask=lowerCamelCase__ ,     output_hidden_states=lowerCamelCase__ ,     )["""hidden_states"""][0]
    lowercase__       :       int				=						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__       :       Union[str, Any]				=						ids_tensor((1,) ,     output_from_past.shape[-1]						).item()
    lowercase__       :       Optional[Any]				=						output_from_no_past[:, -3:, random_slice_idx].detach()
    lowercase__       :       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__ ,     )      ->							Union[str, Any]:
    lowercase__       :       Optional[int]				=						BertGenerationDecoder(lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       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						)      ->							List[Any]:
    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       str				=						self.prepare_config_and_inputs()
    lowercase__       :       int				=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
    return config, inputs_dict

@require_torch
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Union[str, Any]									=    (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   _a   :       Optional[int]									=    (BertGenerationDecoder,) if is_torch_available() else ()
   _a   :       str									=    (
       {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
       if is_torch_available()
       else {}
   )


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       int				=						BertGenerationEncoderTester(self						)
    lowercase__       :       Union[str, Any]				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     hidden_size=37						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    self.config_tester.run_common_tests()


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Dict				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       Any				=						self.model_tester.prepare_config_and_inputs()
    lowercase__       :       Optional[int]				=						"""bert"""
    self.model_tester.create_and_check_model(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Optional[Any]				=						self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Tuple				=						self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    # This regression test was failing with PyTorch < 1.3
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       Any				=						self.model_tester.prepare_config_and_inputs_for_decoder()

    lowercase__       :       Dict				=						None

    self.model_tester.create_and_check_model_as_decoder(
        lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     )


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       str				=						self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase__						)







   @slow
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    lowercase__       :       List[Any]				=						BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""						)
    self.assertIsNotNone(lowerCamelCase__						)

@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       Any				=						BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""						)
    lowercase__       :       Dict				=						torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]						)
    with torch.no_grad():
     lowercase__       :       int				=						model(lowerCamelCase__						)[0]
    lowercase__       :       List[str]				=						torch.Size([1, 8, 1024]						)
    self.assertEqual(output.shape ,     lowerCamelCase__						)
    lowercase__       :       Optional[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] ,     lowerCamelCase__ ,     atol=1E-4						)						)

@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       List[str]				=						BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""						)
    lowercase__       :       str				=						torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]						)
    with torch.no_grad():
     lowercase__       :       List[str]				=						model(lowerCamelCase__						)[0]
    lowercase__       :       Optional[Any]				=						torch.Size([1, 8, 5_0358]						)
    self.assertEqual(output.shape ,     lowerCamelCase__						)
    lowercase__       :       str				=						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] ,     lowerCamelCase__ ,     atol=1E-4						)						)



"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)