
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      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      XLMRobertaModel.from_pretrained("""xlm-roberta-base"""      )
           lowercase							:     Dict       	=      torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]      )
           # The dog is cute and lives in the garden house

           lowercase							:     List[Any]       	=      torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
           lowercase							:     str       	=      torch.tensor(
               [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]      )
           #  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():
                  lowercase							:     List[str]       	=      model(snake_case      )["""last_hidden_state"""].detach()
           self.assertEqual(output.shape		,snake_case      )
           # compare the actual values for a slice of last dim
           self.assertTrue(torch.allclose(output[:, :, -1]		,snake_case		,atol=1e-3      )      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      XLMRobertaModel.from_pretrained("""xlm-roberta-large"""      )
           lowercase							:     List[Any]       	=      torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]      )
           # The dog is cute and lives in the garden house

           lowercase							:     Any       	=      torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
           lowercase							:     Dict       	=      torch.tensor(
               [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]      )
           #  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():
                  lowercase							:     Dict       	=      model(snake_case      )["""last_hidden_state"""].detach()
           self.assertEqual(output.shape		,snake_case      )
           # compare the actual values for a slice of last dim
           self.assertTrue(torch.allclose(output[:, :, -1]		,snake_case		,atol=1e-3      )      )


import logging

from transformers.configuration_utils import PretrainedConfig


__a  			=					logging.getLogger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'masked_bert'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			str=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Any=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="topK"       ,							SCREAMING_SNAKE_CASE_  :			Dict="constant"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.0       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_act
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					layer_norm_eps
					lowercase_       =					pruning_method
					lowercase_       =					mask_init
					lowercase_       =					mask_scale




