





'''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     lowerCAmelCase__ (				unittest.TestCase    ):

       '''simple docstring'''

       @slow
       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						XLMRobertaModel.from_pretrained('''xlm-roberta-base'''						)
              UpperCAmelCase  =						torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]						)
              # The dog is cute and lives in the garden house

              UpperCAmelCase  =						torch.Size((1, 12, 768)						)  # batch_size, sequence_length, embedding_vector_dim
              UpperCAmelCase  =						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():
                     UpperCAmelCase  =						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 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						XLMRobertaModel.from_pretrained('''xlm-roberta-large'''						)
              UpperCAmelCase  =						torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]						)
              # The dog is cute and lives in the garden house

              UpperCAmelCase  =						torch.Size((1, 12, 1024)						)  # batch_size, sequence_length, embedding_vector_dim
              UpperCAmelCase  =						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():
                     UpperCAmelCase  =						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_sentencepiece_available,
    is_torch_available,
)


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






