

import unittest

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

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 (
       RemBertConfig,
       RemBertForCausalLM,
       RemBertForMaskedLM,
       RemBertForMultipleChoice,
       RemBertForQuestionAnswering,
       RemBertForSequenceClassification,
       RemBertForTokenClassification,
       RemBertModel,
   )
   from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class _UpperCamelCase							:
   '''simple docstring'''







   def __init__(     self							:	List[str] , a							:	str , a							:	Optional[Any]=13 , a							:	int=7 , a							:	Optional[Any]=True , a							:	Any=True , a							:	int=True , a							:	Tuple=True , a							:	Tuple=99 , a							:	Optional[Any]=32 , a							:	str=18 , a							:	Dict=43 , a							:	Tuple=5 , a							:	Optional[Any]=4 , a							:	List[Any]=37 , a							:	Optional[int]="gelu" , a							:	Optional[int]=0.1 , a							:	int=0.1 , a							:	Optional[Any]=512 , a							:	Union[str, Any]=16 , a							:	str=2 , a							:	List[str]=0.02 , a							:	Tuple=3 , a							:	Optional[Any]=4 , a							:	Union[str, Any]=None , )							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    parent
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    batch_size
    SCREAMING_SNAKE_CASE				: Tuple			       =    seq_length
    SCREAMING_SNAKE_CASE				: List[str]			       =    is_training
    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    use_input_mask
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    use_token_type_ids
    SCREAMING_SNAKE_CASE				: int			       =    use_labels
    SCREAMING_SNAKE_CASE				: int			       =    vocab_size
    SCREAMING_SNAKE_CASE				: Dict			       =    hidden_size
    SCREAMING_SNAKE_CASE				: List[str]			       =    input_embedding_size
    SCREAMING_SNAKE_CASE				: Dict			       =    output_embedding_size
    SCREAMING_SNAKE_CASE				: List[str]			       =    num_hidden_layers
    SCREAMING_SNAKE_CASE				: str			       =    num_attention_heads
    SCREAMING_SNAKE_CASE				: Any			       =    intermediate_size
    SCREAMING_SNAKE_CASE				: str			       =    hidden_act
    SCREAMING_SNAKE_CASE				: str			       =    hidden_dropout_prob
    SCREAMING_SNAKE_CASE				: Dict			       =    attention_probs_dropout_prob
    SCREAMING_SNAKE_CASE				: Any			       =    max_position_embeddings
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    type_vocab_size
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    type_sequence_label_size
    SCREAMING_SNAKE_CASE				: str			       =    initializer_range
    SCREAMING_SNAKE_CASE				: Tuple			       =    num_labels
    SCREAMING_SNAKE_CASE				: List[str]			       =    num_choices
    SCREAMING_SNAKE_CASE				: Tuple			       =    scope
   def 							__UpperCamelCase	(     self							:	Dict		)							->    List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ids_tensor([self.batch_size, self.seq_length] , self.vocab_size		)

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

    SCREAMING_SNAKE_CASE				: List[str]			       =    None
    if self.use_token_type_ids:
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size		)

    SCREAMING_SNAKE_CASE				: str			       =    None
    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    None
    SCREAMING_SNAKE_CASE				: int			       =    None
    if self.use_labels:
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    ids_tensor([self.batch_size] , self.type_sequence_label_size		)
     SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    ids_tensor([self.batch_size, self.seq_length] , self.num_labels		)
     SCREAMING_SNAKE_CASE				: Dict			       =    ids_tensor([self.batch_size] , self.num_choices		)

    SCREAMING_SNAKE_CASE				: str			       =    RemBertConfig(
        vocab_size=self.vocab_size , hidden_size=self.hidden_size , input_embedding_size=self.input_embedding_size , output_embedding_size=self.output_embedding_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 , type_vocab_size=self.type_vocab_size , is_decoder=__SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , )

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Any:

    """simple docstring"""

    (
        (
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,      
    )				: int			       =    self.prepare_config_and_inputs()

    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    True
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
    SCREAMING_SNAKE_CASE				: int			       =    ids_tensor([self.batch_size, self.seq_length] , vocab_size=2		)

    return (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    )
   def 							__UpperCamelCase	(     self							:	Any , a							:	Optional[Any] , a							:	List[str] , a							:	Tuple , a							:	Optional[int] , a							:	Union[str, Any] , a							:	str , a							:	Tuple		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    RemBertModel(config=__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE		)
    SCREAMING_SNAKE_CASE				: int			       =    model(__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE		)
    SCREAMING_SNAKE_CASE				: int			       =    model(__SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)		)
   def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	Union[str, Any] , a							:	Optional[Any] , a							:	str , a							:	Any , a							:	Tuple , a							:	Any , a							:	Optional[int] , a							:	int , a							:	int , )							->    List[str]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[str]			       =    True
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    RemBertModel(__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: Tuple			       =    model(
        __SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , encoder_attention_mask=__SCREAMING_SNAKE_CASE , )
    SCREAMING_SNAKE_CASE				: Tuple			       =    model(
        __SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , encoder_hidden_states=__SCREAMING_SNAKE_CASE , )
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)		)
   def 							__UpperCamelCase	(     self							:	Tuple , a							:	Tuple , a							:	Tuple , a							:	Dict , a							:	List[Any] , a							:	Any , a							:	List[Any] , a							:	List[Any] , a							:	int , a							:	Any , )							->    Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Any			       =    RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: List[str]			       =    model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)		)
   def 							__UpperCamelCase	(     self							:	List[str] , a							:	str , a							:	Optional[Any] , a							:	Union[str, Any] , a							:	str , a							:	Union[str, Any] , a							:	Dict , a							:	Tuple		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[Any]			       =    RemBertForMaskedLM(config=__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: Tuple			       =    model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)		)
   def 							__UpperCamelCase	(     self							:	str , a							:	List[str] , a							:	int , a							:	Dict , a							:	str , a							:	List[str] , a							:	Dict , a							:	List[str] , a							:	str , a							:	Optional[Any] , )							->    int:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: str			       =    True
    SCREAMING_SNAKE_CASE				: List[Any]			       =    True
    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    RemBertForCausalLM(config=__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()

    # first forward pass
    SCREAMING_SNAKE_CASE				: List[str]			       =    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 , )
    SCREAMING_SNAKE_CASE				: int			       =    outputs.past_key_values

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

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

    SCREAMING_SNAKE_CASE				: List[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]
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    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
    SCREAMING_SNAKE_CASE				: Any			       =    ids_tensor((1,) , output_from_past.shape[-1]		).item()
    SCREAMING_SNAKE_CASE				: str			       =    output_from_no_past[:, -3:, random_slice_idx].detach()
    SCREAMING_SNAKE_CASE				: 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(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3		)		)
   def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	List[str] , a							:	Optional[Any] , a							:	Optional[int] , a							:	Tuple , a							:	int , a							:	Union[str, Any] , a							:	Optional[int]		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    RemBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: str			       =    model(
        __SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , start_positions=__SCREAMING_SNAKE_CASE , end_positions=__SCREAMING_SNAKE_CASE , )
    self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length)		)
    self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length)		)
   def 							__UpperCamelCase	(     self							:	int , a							:	str , a							:	int , a							:	int , a							:	List[Any] , a							:	Tuple , a							:	Optional[int] , a							:	Optional[int]		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[str]			       =    self.num_labels
    SCREAMING_SNAKE_CASE				: List[str]			       =    RemBertForSequenceClassification(__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: str			       =    model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels)		)
   def 							__UpperCamelCase	(     self							:	int , a							:	str , a							:	Union[str, Any] , a							:	List[str] , a							:	Dict , a							:	Any , a							:	int , a							:	List[str]		)							->    Union[str, Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[str]			       =    self.num_labels
    SCREAMING_SNAKE_CASE				: Dict			       =    RemBertForTokenClassification(config=__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels)		)
   def 							__UpperCamelCase	(     self							:	str , a							:	Optional[Any] , a							:	Dict , a							:	Tuple , a							:	Tuple , a							:	Optional[int] , a							:	Any , a							:	str		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Dict			       =    self.num_choices
    SCREAMING_SNAKE_CASE				: int			       =    RemBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE		)
    model.to(__SCREAMING_SNAKE_CASE		)
    model.eval()
    SCREAMING_SNAKE_CASE				: List[Any]			       =    input_ids.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
    SCREAMING_SNAKE_CASE				: str			       =    token_type_ids.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
    SCREAMING_SNAKE_CASE				: List[str]			       =    input_mask.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
    SCREAMING_SNAKE_CASE				: Tuple			       =    model(
        __SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE , )
    self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices)		)






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

    """simple docstring"""

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






@require_torch
class _UpperCamelCase							(							__lowerCAmelCase							,		__lowerCAmelCase							,		unittest.TestCase				):
   '''simple docstring'''







   lowerCamelCase__          =(
       (
           RemBertModel,
           RemBertForMaskedLM,
           RemBertForCausalLM,
           RemBertForMultipleChoice,
           RemBertForQuestionAnswering,
           RemBertForSequenceClassification,
           RemBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase__          =(RemBertForCausalLM,) if is_torch_available() else ()
   lowerCamelCase__          =(
       {
           '''feature-extraction''': RemBertModel,
           '''fill-mask''': RemBertForMaskedLM,
           '''question-answering''': RemBertForQuestionAnswering,
           '''text-classification''': RemBertForSequenceClassification,
           '''text-generation''': RemBertForCausalLM,
           '''token-classification''': RemBertForTokenClassification,
           '''zero-shot''': RemBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   def 							__UpperCamelCase	(     self							:	List[Any]		)							->    Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[Any]			       =    RemBertModelTester(self		)
    SCREAMING_SNAKE_CASE				: Dict			       =    ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , hidden_size=37		)
   def 							__UpperCamelCase	(     self							:	Any		)							->    str:

    """simple docstring"""

    self.config_tester.run_common_tests()
   def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Union[str, Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     SCREAMING_SNAKE_CASE				: int			       =    type
     self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	int		)							->    Tuple:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	int		)							->    Dict:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	List[Any]		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	List[Any]		)							->    str:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Any			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	Any		)							->    Union[str, Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Any			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[Any]			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	str		)							->    Dict:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.model_tester.prepare_config_and_inputs_for_decoder()
    self.model_tester.create_and_check_model_as_decoder(*__SCREAMING_SNAKE_CASE		)
   def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    int:

    """simple docstring"""

    (
        (
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,(
        SCREAMING_SNAKE_CASE      
    )       ,      
    )				: Optional[int]			       =    self.model_tester.prepare_config_and_inputs_for_decoder()

    SCREAMING_SNAKE_CASE				: 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 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , )






   @slow
   def 							__UpperCamelCase	(     self							:	str		)							->    int:

    """simple docstring"""

    for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     SCREAMING_SNAKE_CASE				: Any			       =    RemBertModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(__SCREAMING_SNAKE_CASE		)






@require_torch
class _UpperCamelCase							(							unittest.TestCase				):
   '''simple docstring'''







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

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    RemBertModel.from_pretrained("google/rembert"		)
    SCREAMING_SNAKE_CASE				: Any			       =    torch.tensor([[312, 5_6498, 313, 2125, 313]]		)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    torch.tensor([[0, 0, 0, 1, 1]]		)
    with torch.no_grad():
     SCREAMING_SNAKE_CASE				: Tuple			       =    model(__SCREAMING_SNAKE_CASE , token_type_ids=__SCREAMING_SNAKE_CASE , output_hidden_states=__SCREAMING_SNAKE_CASE		)

    SCREAMING_SNAKE_CASE				: Tuple			       =    1152

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.Size((1, 5, hidden_size)		)
    self.assertEqual(output["last_hidden_state"].shape , __SCREAMING_SNAKE_CASE		)

    SCREAMING_SNAKE_CASE				: Tuple			       =    torch.tensor(
        [
            [
                [0.0754, -0.2022, 0.1904],
                [-0.3354, -0.3692, -0.4791],
                [-0.2314, -0.6729, -0.0749],
                [-0.0396, -0.3105, -0.4234],
                [-0.1571, -0.0525, 0.5353],
            ]
        ]		)

    # Running on the original tf implementation gives slightly different results here.
    # Not clear why this variations is present
    # TODO: Find reason for discrepancy
    # expected_original_implementation = [[
    #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
    #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
    #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
    #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
    #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
    # ]]

    self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4		)		)


from typing import TYPE_CHECKING

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


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

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