





"""simple docstring"""


from __future__ import annotations




def     lowercase__			(    lowercase_						,lowercase_	)    ->						Any:




      """simple docstring"""

      _UpperCamelCase					:		Dict  =				[None] * (len(A_	) + len(A_	))

      _UpperCamelCase					:		Union[str, Any]  =				0  # pointer to current index for left Half
      _UpperCamelCase					:		Union[str, Any]  =				0  # pointer to current index for the right Half
      _UpperCamelCase					:		Union[str, Any]  =				0  # pointer to current index for the sorted array Half

      while pointera < len(A_	) and pointera < len(A_	):
            if left_half[pointera] < right_half[pointera]:
                  _UpperCamelCase					:		str  =				left_half[pointera]
                  pointera += 1
                  index += 1
            else:
                  _UpperCamelCase					:		Tuple  =				right_half[pointera]
                  pointera += 1
                  index += 1
      while pointera < len(A_	):
            _UpperCamelCase					:		Any  =				left_half[pointera]
            pointera += 1
            index += 1

      while pointera < len(A_	):
            _UpperCamelCase					:		Any  =				right_half[pointera]
            pointera += 1
            index += 1

      return sorted_array




def     lowercase__			(    lowercase_	)    ->						Optional[Any]:




      """simple docstring"""

      if len(A_	) <= 1:
            return array
      # the actual formula to calculate the middle element = left + (right - left) // 2
      # this avoids integer overflow in case of large N
      _UpperCamelCase					:		Tuple  =				0 + (len(A_	) - 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
      _UpperCamelCase					:		Dict  =				array[:middle]
      _UpperCamelCase					:		str  =				array[middle:]

      return merge(merge_sort(A_	)						,merge_sort(A_	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()








"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase				:    List[Any]     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Any     =						['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    List[str]     =						[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase				:    Union[str, Any]     =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




