



'''simple docstring'''





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

_UpperCamelCase											=		TaTokenizer

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

_UpperCamelCase											=		TaTokenizerFast

_UpperCamelCase											=		{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		["""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

   _UpperCamelCase											=		_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )












import numpy as np
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      int(np.ceil((x_end - xa) / h				)				)
  lowerCAmelCase__										=      np.zeros((n + 1,)				)
  lowerCAmelCase__										=      ya
  lowerCAmelCase__										=      xa

  for k in range(__lowerCAmelCase				):
    lowerCAmelCase__										=      f(__lowerCAmelCase   , y[k]				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + 0.5 * h   , y[k] + 0.5 * h * ka				)
    lowerCAmelCase__										=      f(x + h   , y[k] + h * ka				)
    lowerCAmelCase__										=      y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
    x += h

  return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()

