





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad

class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	10
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	[1, 2, 3, 4]
         A_     :				Any			      =	[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(a__      ,       self.block_size      ,       0       )      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         A_     :				Any			      =	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(a__      ,       self.block_size      ,       0       )      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
         A_     :				Dict			      =	[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(a__      ,       self.block_size      ,       0       )      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	"""It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
         A_      ,				A_     :				str			      =	process_story(a__       )
         self.assertEqual(a__      ,       []       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	""""""
         A_      ,				A_     :				List[Any]			      =	process_story(a__       )
         self.assertEqual(a__      ,       []       )
         self.assertEqual(a__      ,       []       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	(
             """It was the year of Our Lord one thousand seven hundred and """
             """seventy-five\n\nSpiritual revelations were conceded to England """
             """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
         )
         A_      ,				A_     :				List[str]			      =	process_story(a__       )

         A_     :				List[Any]			      =	[
             """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
             """Spiritual revelations were conceded to England at that favoured period, as at this.""",
         ]
         self.assertEqual(a__      ,       a__       )

         A_     :				int			      =	["""It was the best of times."""]
         self.assertEqual(a__      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	torch.tensor([1, 2, 3, 4]       )
         A_     :				Optional[int]			      =	torch.tensor([1, 1, 1, 1]       )
         np.testing.assert_array_equal(build_mask(a__      ,       0       ).numpy()      ,       expected.numpy()       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	torch.tensor([1, 2, 3, 4, 23, 23, 23]       )
         A_     :				List[str]			      =	torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
         np.testing.assert_array_equal(build_mask(a__      ,       23       ).numpy()      ,       expected.numpy()       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	torch.tensor([8, 2, 3, 4, 1, 1, 1]       )
         A_     :				Optional[Any]			      =	torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
         np.testing.assert_array_equal(build_mask(a__      ,       1       ).numpy()      ,       expected.numpy()       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	101
         A_     :				Optional[Any]			      =	torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]       )
         A_     :				List[Any]			      =	torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]       )

         A_     :				Optional[int]			      =	compute_token_type_ids(a__      ,       a__       )
         np.testing.assert_array_equal(a__      ,       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__)






