
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 							lowerCAmelCase_						(    unittest.TestCase							):




			'''simple docstring'''


			@slow
			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							Tuple											=XLMRobertaModel.from_pretrained('xlm-roberta-base'  )
										SCREAMING_SNAKE_CASE_	:							Dict											=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

										SCREAMING_SNAKE_CASE_	:							Dict											=torch.Size((1, 12, 768)  )  # batch_size, sequence_length, embedding_vector_dim
										SCREAMING_SNAKE_CASE_	:							Dict											=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():
																	SCREAMING_SNAKE_CASE_	:							Dict											=model(__SCREAMING_SNAKE_CASE  )['last_hidden_state'].detach()
										self.assertEqual(output.shape    ,							__SCREAMING_SNAKE_CASE  )
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]    ,							__SCREAMING_SNAKE_CASE    ,							atol=1E-3  )  )







			@slow
			def      __lowerCamelCase    (	self  ):
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=XLMRobertaModel.from_pretrained('xlm-roberta-large'  )
										SCREAMING_SNAKE_CASE_	:							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

										SCREAMING_SNAKE_CASE_	:							str											=torch.Size((1, 12, 1_024)  )  # batch_size, sequence_length, embedding_vector_dim
										SCREAMING_SNAKE_CASE_	:							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():
																	SCREAMING_SNAKE_CASE_	:							int											=model(__SCREAMING_SNAKE_CASE  )['last_hidden_state'].detach()
										self.assertEqual(output.shape    ,							__SCREAMING_SNAKE_CASE  )
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]    ,							__SCREAMING_SNAKE_CASE    ,							atol=1E-3  )  )






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


