






from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap






def     _snake_case    (__lowercase):
   return getitem, k






def     _snake_case    (__lowercase    ,      __lowercase):
   return setitem, k, v






def     _snake_case    (__lowercase):
   return delitem, k






def     _snake_case    (__lowercase    ,      __lowercase    ,      *__lowercase):
   try:
      return fun(__lowercase    ,      *__lowercase), None
   except Exception as e:
      return None, e


snake_case__      :	Optional[Any]       	=      (
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

snake_case__      :	Union[str, Any]       	=      [
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

snake_case__      :	List[str]       	=      [
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

snake_case__      :	Union[str, Any]       	=      [
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

snake_case__      :	Optional[int]       	=      [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

snake_case__      :	Tuple       	=      [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]






@pytest.mark.parametrize(
    'operations'    ,      (
        pytest.param(_add_items    ,      id='add items'),
        pytest.param(_overwrite_items    ,      id='overwrite items'),
        pytest.param(_delete_items    ,      id='delete items'),
        pytest.param(_access_absent_items    ,      id='access absent items'),
        pytest.param(_add_with_resize_up    ,      id='add with resize up'),
        pytest.param(_add_with_resize_down    ,      id='add with resize down'),
    )    ,      )
def     _snake_case    (__lowercase):
   UpperCamelCase_									=     HashMap(initial_block_size=4)
   UpperCamelCase_									=     {}
   for _, (fun, *args) in enumerate(__lowercase):
      UpperCamelCase_		,      UpperCamelCase_									=     _run_operation(__lowercase    ,      __lowercase    ,      *__lowercase)
      UpperCamelCase_		,      UpperCamelCase_									=     _run_operation(__lowercase    ,      __lowercase    ,      *__lowercase)
      assert my_res == py_res
      assert str(__lowercase) == str(__lowercase)
      assert set(__lowercase) == set(__lowercase)
      assert len(__lowercase) == len(__lowercase)
      assert set(my.items()) == set(py.items())






def     _snake_case    ():
   def is_public(__lowercase) -> bool:
      return not name.startswith('_')

   UpperCamelCase_									=     {name for name in dir({}) if is_public(__lowercase)}
   UpperCamelCase_									=     {name for name in dir(HashMap()) if is_public(__lowercase)}

   assert dict_public_names > hash_public_names













from typing import TYPE_CHECKING

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


snake_case__      :	List[str]       	=      {
    """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:
 snake_case__      :	List[str]       	=      ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__      :	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

 snake_case__      :	Optional[int]       	=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





