from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase			:							str     =logging.get_logger(__name__)

lowerCAmelCase			:							Any     ={
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'xlm-roberta'



      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				Any=3_0522    ,  _UpperCamelCase     :				Optional[Any]=768    ,  _UpperCamelCase     :				Any=12    ,  _UpperCamelCase     :				Optional[int]=12    ,  _UpperCamelCase     :				Optional[Any]=3072    ,  _UpperCamelCase     :				Any="gelu"    ,  _UpperCamelCase     :				Tuple=0.1    ,  _UpperCamelCase     :				Tuple=0.1    ,  _UpperCamelCase     :				Tuple=512    ,  _UpperCamelCase     :				str=2    ,  _UpperCamelCase     :				Dict=0.0_2    ,  _UpperCamelCase     :				List[str]=1E-1_2    ,  _UpperCamelCase     :				Optional[int]=1    ,  _UpperCamelCase     :				Union[str, Any]=0    ,  _UpperCamelCase     :				str=2    ,  _UpperCamelCase     :				Tuple="absolute"    ,  _UpperCamelCase     :				Union[str, Any]=True    ,  _UpperCamelCase     :				Tuple=None    ,  **_UpperCamelCase     :				Dict    ,  )				->List[Any]:


          """simple docstring"""


          super().__init__(pad_token_id=_UpperCamelCase    ,  bos_token_id=_UpperCamelCase    ,  eos_token_id=_UpperCamelCase    ,  **_UpperCamelCase)

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





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          if self.task == "multiple-choice":
              _lowerCamelCase     :		Union[str, Any] 						=						{0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
              _lowerCamelCase     :		Union[str, Any] 						=						{0: """batch""", 1: """sequence"""}
          return OrderedDict(
              [
                  ("""input_ids""", dynamic_axis),
                  ("""attention_mask""", dynamic_axis),
              ])





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		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
          _lowerCamelCase     :		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

          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		str 						=						torch.tensor(
              [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
          #  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():
              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))




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


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
          _lowerCamelCase     :		Optional[Any] 						=						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

          _lowerCamelCase     :		str 						=						torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
              [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
          #  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():
              _lowerCamelCase     :		int 						=						model(_UpperCamelCase)["""last_hidden_state"""].detach()
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1]    ,  _UpperCamelCase    ,  atol=1E-3))




