


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel







@require_sentencepiece
@require_tokenizers
@require_torch
class 		_snake_case  (				unittest.TestCase							):




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	XLMRobertaModel.from_pretrained('''xlm-roberta-base'''   )
       a						:str										=	torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]   )
       # The dog is cute and lives in the garden house

       a						:Optional[Any]										=	torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
       a						:Union[str, Any]										=	torch.tensor(
           [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
             a						:Optional[int]										=	model(_lowerCamelCase   )['''last_hidden_state'''].detach()
       self.assertEqual(output.shape ,  _lowerCamelCase   )
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1] ,  _lowerCamelCase ,  atol=1e-3   )   )




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	XLMRobertaModel.from_pretrained('''xlm-roberta-large'''   )
       a						:Optional[int]										=	torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]   )
       # The dog is cute and lives in the garden house

       a						:List[Any]										=	torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
       a						:Tuple										=	torch.tensor(
           [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
             a						:Dict										=	model(_lowerCamelCase   )['''last_hidden_state'''].detach()
       self.assertEqual(output.shape ,  _lowerCamelCase   )
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1] ,  _lowerCamelCase ,  atol=1e-3   )   )









from ...configuration_utils import PretrainedConfig







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'bert-generation'




 def __init__(		self ,  _lowerCamelCase=5_0358 ,  _lowerCamelCase=1024 ,  _lowerCamelCase=24 ,  _lowerCamelCase=16 ,  _lowerCamelCase=4096 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=0 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase="absolute" ,  _lowerCamelCase=True ,  **_lowerCamelCase ,  ):
       super().__init__(pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )

       a						:Optional[int]										=	vocab_size
       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Any										=	num_attention_heads
       a						:List[Any]										=	hidden_act
       a						:Tuple										=	intermediate_size
       a						:Any										=	hidden_dropout_prob
       a						:int										=	attention_probs_dropout_prob
       a						:Dict										=	max_position_embeddings
       a						:int										=	initializer_range
       a						:Union[str, Any]										=	layer_norm_eps
       a						:str										=	position_embedding_type
       a						:int										=	use_cache





