'''simple docstring'''




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       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       int     				=					XLMRobertaModel.from_pretrained('xlm-roberta-base'				)
        lowercase_					:       Union[str, Any]     				=					torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]				)
        # The dog is cute and lives in the garden house

        lowercase_					:       str     				=					torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
        lowercase_					:       Union[str, Any]     				=					torch.tensor(
            [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]				)
        #  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_					:       Dict     				=					model(_a				)['last_hidden_state'].detach()
        self.assertEqual(output.shape       ,				_a				)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]       ,				_a       ,				atol=1e-3				)				)





   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Union[str, Any]     				=					XLMRobertaModel.from_pretrained('xlm-roberta-large'				)
        lowercase_					:       Union[str, Any]     				=					torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]				)
        # The dog is cute and lives in the garden house

        lowercase_					:       Dict     				=					torch.Size((1, 12, 1_024)				)  # batch_size, sequence_length, embedding_vector_dim
        lowercase_					:       Dict     				=					torch.tensor(
            [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]				)
        #  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_					:       List[str]     				=					model(_a				)['last_hidden_state'].detach()
        self.assertEqual(output.shape       ,				_a				)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]       ,				_a       ,				atol=1e-3				)				)





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A								=   {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BigBirdPegasusForCausalLM',
     'BigBirdPegasusForConditionalGeneration',
     'BigBirdPegasusForQuestionAnswering',
     'BigBirdPegasusForSequenceClassification',
     'BigBirdPegasusModel',
     'BigBirdPegasusPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

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




