





from typing import List

import numpy as np



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	{key: len(_lowerCAmelCase     ) for key, value in gen_kwargs.items() if isinstance(_lowerCAmelCase  ,_lowerCAmelCase     )}
   if len(set(lists_lengths.values()     )     ) > 1:
      raise RuntimeError(
          (
              """Sharding is ambiguous for this dataset: """
              + """we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n"""
              + """\n""".join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()     )
              + """\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, """
              + """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
          )     )
   A_     :				str			      =	max(lists_lengths.values()  ,default=0     )
   return max(1  ,_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Optional[int]			      =	[]
   for group_idx in range(_lowerCAmelCase     ):
      A_     :				Tuple			      =	num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
      if num_shards_to_add == 0:
         break
      A_     :				Tuple			      =	shards_indices_per_group[-1].stop if shards_indices_per_group else 0
      A_     :				List[str]			      =	range(_lowerCAmelCase  ,start + num_shards_to_add     )
      shards_indices_per_group.append(_lowerCAmelCase     )
   return shards_indices_per_group



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Tuple			      =	_number_of_shards_in_gen_kwargs(_lowerCAmelCase     )
   if num_shards == 1:
      return [dict(_lowerCAmelCase     )]
   else:
      A_     :				Union[str, Any]			      =	_distribute_shards(num_shards=_lowerCAmelCase  ,max_num_jobs=_lowerCAmelCase     )
      return [
          {
              key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
              if isinstance(_lowerCAmelCase  ,_lowerCAmelCase     )
              else value
              for key, value in gen_kwargs.items()
          }
          for group_idx in range(len(_lowerCAmelCase     )     )
      ]



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   return {
       key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
       if isinstance(gen_kwargs_list[0][key]  ,_lowerCAmelCase     )
       else gen_kwargs_list[0][key]
       for key in gen_kwargs_list[0]
   }







def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				str			      =	{len(_lowerCAmelCase     ) for value in gen_kwargs.values() if isinstance(_lowerCAmelCase  ,_lowerCAmelCase     )}
   A_     :				Any			      =	{}
   for size in list_sizes:
      A_     :				Union[str, Any]			      =	list(range(_lowerCAmelCase     )     )
      rng.shuffle(indices_per_size[size]     )
   # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
   A_     :				Optional[Any]			      =	dict(_lowerCAmelCase     )
   for key, value in shuffled_kwargs.items():
      if isinstance(_lowerCAmelCase  ,_lowerCAmelCase     ):
         A_     :				Any			      =	[value[i] for i in indices_per_size[len(_lowerCAmelCase     )]]
   return shuffled_kwargs













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__)






