





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
      import torch

@require_torch
@require_optimum
@slow
class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	"""hf-internal-testing/tiny-random-t5"""
         A_     :				List[str]			      =	AutoTokenizer.from_pretrained(a__       )
         A_     :				int			      =	AutoModelForSeqaSeqLM.from_pretrained(a__       )

         A_     :				Dict			      =	tokenizer("""This is me"""      ,       return_tensors="""pt"""       )

         A_     :				Any			      =	model.to_bettertransformer()

         self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()       )       )

         A_     :				Optional[Any]			      =	model.generate(**a__       )

         A_     :				Any			      =	model.reverse_bettertransformer()

         self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()       )       )

         with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(a__       )

            A_     :				Dict			      =	AutoModelForSeqaSeqLM.from_pretrained(a__       )

            self.assertFalse(
                any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()       )       )

            A_     :				Dict			      =	model_reloaded.generate(**a__       )
            self.assertTrue(torch.allclose(a__      ,       a__       )       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	"""hf-internal-testing/tiny-random-t5"""
         A_     :				Optional[int]			      =	AutoModelForSeqaSeqLM.from_pretrained(a__       )

         A_     :				Tuple			      =	model.to_bettertransformer()

         with tempfile.TemporaryDirectory() as tmpdirname:
            with self.assertRaises(a__       ):
               model.save_pretrained(a__       )

            A_     :				Any			      =	model.reverse_bettertransformer()
            model.save_pretrained(a__       )













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """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:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """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

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






