
'''simple docstring'''

from __future__ import annotations


def 		_UpperCAmelCase			(					_lowerCamelCase  :    list					, _lowerCamelCase  :    list						)      ->  list:
  _lowerCAmelCase    :					str							=   [None] * (len(_lowerCamelCase						) + len(_lowerCamelCase						))

  _lowerCAmelCase    :					Dict							=   0  # pointer to current index for left Half
  _lowerCAmelCase    :					str							=   0  # pointer to current index for the right Half
  _lowerCAmelCase    :					Any							=   0  # pointer to current index for the sorted array Half

  while pointera < len(_lowerCamelCase						) and pointera < len(_lowerCamelCase						):
    if left_half[pointera] < right_half[pointera]:
      _lowerCAmelCase    :					Optional[Any]							=   left_half[pointera]
      pointera += 1
      index += 1
    else:
      _lowerCAmelCase    :					Union[str, Any]							=   right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(_lowerCamelCase						):
    _lowerCAmelCase    :					Optional[int]							=   left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(_lowerCamelCase						):
    _lowerCAmelCase    :					Optional[int]							=   right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array


def 		_UpperCAmelCase			(					_lowerCamelCase  :    list						)      ->  list:
  if len(_lowerCamelCase						) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  _lowerCAmelCase    :					Union[str, Any]							=   0 + (len(_lowerCamelCase						) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  _lowerCAmelCase    :					Dict							=   array[:middle]
  _lowerCAmelCase    :					Optional[int]							=   array[middle:]

  return merge(merge_sort(_lowerCamelCase						)					, merge_sort(_lowerCamelCase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

     UpperCamelCase_            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


