




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
  from ..ta.tokenization_ta import TaTokenizer
else:
  from ...utils.dummy_sentencepiece_objects import TaTokenizer

_lowerCamelCase	:		str							       =   TaTokenizer

if is_tokenizers_available():
  from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
  from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_lowerCamelCase	:		str							       =   TaTokenizerFast

_lowerCamelCase	:		int							       =   {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Optional[int]							       =   [
      """MT5EncoderModel""",
      """MT5ForConditionalGeneration""",
      """MT5ForQuestionAnswering""",
      """MT5Model""",
      """MT5PreTrainedModel""",
      """MT5Stack""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Any							       =   ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Any							       =   ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
  from .configuration_mta import MTaConfig, MTaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mta import (
        MTaEncoderModel,
        MTaForConditionalGeneration,
        MTaForQuestionAnswering,
        MTaModel,
        MTaPreTrainedModel,
        MTaStack,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
  import sys

  _lowerCamelCase	:		Union[str, Any]							       =   _LazyModule(
      __name__,
      globals()["""__file__"""],
      _import_structure,
      extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
      module_spec=__spec__,
  )








from __future__ import annotations







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       tuple[float, list[float]]:



       """simple docstring"""

       A__       =    list(range(len(lowercase_						)						)						)
       A__       =    [v / w for v, w in zip(lowercase_ ,				lowercase_						)]
       index.sort(key=lambda lowercase_						: ratio[i] ,				reverse=lowercase_						)

       A__       =    0
       A__       =    [0] * len(lowercase_						)
       for i in index:
              if weight[i] <= capacity:
                     A__       =    1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     A__       =    capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()


