


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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							lowercase__	     =						XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
							lowercase__	     =						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

							lowercase__	     =						torch.Size((1, 12, 768)						)  # batch_size, sequence_length, embedding_vector_dim
							lowercase__	     =						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__	     =						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     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
							lowercase__	     =						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

							lowercase__	     =						torch.Size((1, 12, 1024)						)  # batch_size, sequence_length, embedding_vector_dim
							lowercase__	     =						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__	     =						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						)						)






from typing import TYPE_CHECKING

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


_lowercase    	=		{
    "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:
   _lowercase    	=		[
       "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

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
