



"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

A:     List[str]										=	TypeVar("KEY")
A:     str										=	TypeVar("VAL")



@dataclass(frozen=_A   ,    slots=_A				)
class    SCREAMING_SNAKE_CASE__						(      Generic[KEY, VAL]				):
   __lowerCAmelCase						:     Dict		=		42
   __lowerCAmelCase						:     Tuple		=		42



class    SCREAMING_SNAKE_CASE__						(      _Item				):

   def __init__(     self		)						->   None:





    '''simple docstring'''




    super().__init__(UpperCamelCase__   ,  UpperCamelCase__		)




   def __bool__(     self		)						->   bool:





    '''simple docstring'''




    return False
A:     str										=	_DeletedItem()



class    SCREAMING_SNAKE_CASE__						(      MutableMapping[KEY, VAL]				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = 8   ,  _SCREAMING_SNAKE_CASE = 0.75		)						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			initial_block_size
    UpperCAmelCase			:      Optional[int]			=			[None] * initial_block_size
    assert 0.0 < capacity_factor < 1.0
    UpperCAmelCase			:      Optional[int]			=			capacity_factor
    UpperCAmelCase			:      Tuple			=			0

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    return hash(UpperCamelCase__		) % len(self._buckets		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    return (ind + 1) % len(self._buckets		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   bool:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self._buckets[ind]
    if not stored:
     UpperCAmelCase			:      Dict			=			_Item(UpperCamelCase__   ,  UpperCamelCase__		)
     self._len += 1
     return True
    elif stored.key == key:
     UpperCAmelCase			:      Optional[int]			=			_Item(UpperCamelCase__   ,  UpperCamelCase__		)
     return True
    else:
     return False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   bool:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			len(self._buckets		) * self._capacity_factor
    return len(self		) >= int(UpperCamelCase__		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   bool:





    '''simple docstring'''




    if len(self._buckets		) <= self._initial_block_size:
     return False
    UpperCAmelCase			:      Optional[Any]			=			len(self._buckets		) * self._capacity_factor / 2
    return len(self		) < limit

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self._buckets
    UpperCAmelCase			:      Dict			=			[None] * new_size
    UpperCAmelCase			:      List[Any]			=			0
    for item in old_buckets:
     if item:
      self._add_item(item.key   ,  item.val		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   None:





    '''simple docstring'''




    self._resize(len(self._buckets		) * 2		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   None:





    '''simple docstring'''




    self._resize(len(self._buckets		) // 2		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Iterator[int]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self._get_bucket_index(UpperCamelCase__		)
    for _ in range(len(self._buckets		)		):
     yield ind
     UpperCAmelCase			:      Any			=			self._get_next_ind(UpperCamelCase__		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    for ind in self._iterate_buckets(UpperCamelCase__		):
     if self._try_set(UpperCamelCase__   ,  UpperCamelCase__   ,  UpperCamelCase__		):
      break

   def __setitem__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    if self._is_full():
     self._size_up()

    self._add_item(UpperCamelCase__   ,  UpperCamelCase__		)

   def __delitem__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    for ind in self._iterate_buckets(UpperCamelCase__		):
     UpperCAmelCase			:      Union[str, Any]			=			self._buckets[ind]
     if item is None:
      raise KeyError(UpperCamelCase__		)
     if item is _deleted:
      continue
     if item.key == key:
      UpperCAmelCase			:      Optional[Any]			=			_deleted
      self._len -= 1
      break
    if self._is_sparse():
     self._size_down()

   def __getitem__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   VAL:





    '''simple docstring'''




    for ind in self._iterate_buckets(UpperCamelCase__		):
     UpperCAmelCase			:      Union[str, Any]			=			self._buckets[ind]
     if item is None:
      break
     if item is _deleted:
      continue
     if item.key == key:
      return item.val
    raise KeyError(UpperCamelCase__		)

   def __len__(     self		)						->   int:





    '''simple docstring'''




    return self._len

   def __iter__(     self		)						->   Iterator[KEY]:





    '''simple docstring'''




    yield from (item.key for item in self._buckets if item)




   def __repr__(     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			""" ,""".join(
        F"{item.key}: {item.val}" for item in self._buckets if item		)
    return F"HashMap({val_string})"





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:     Union[str, 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:     List[str]										=	["SpeechT5Tokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Dict										=	[
          "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:     Dict										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
