"""simple docstring"""


from __future__ import annotations


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       Union[str, Any]       ):
   _lowercase							     =       0
   _lowercase							     =       len(_snake_case       ) - 1
   while left <= right:
      _lowercase							     =       (left + right) // 2
      if left == right:
         if sorted_list[middle] < item:
            _lowercase							     =       middle + 1
         break
      elif sorted_list[middle] < item:
         _lowercase							     =       middle + 1
      else:
         _lowercase							     =       middle - 1
   sorted_list.insert(_snake_case,				_snake_case       )
   return sorted_list


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       List[Any]       ):
   _lowercase							     =       []
   while left and right:
      if left[0][0] < right[0][0]:
         result.append(left.pop(0       )       )
      else:
         result.append(right.pop(0       )       )
   return result + left + right


def   __UpperCAmelCase     (	_snake_case		:       Tuple       ):
   _lowercase							     =       len(_snake_case       )
   if length <= 1:
      return list_ad
   _lowercase							     =       length // 2
   return merge(sortlist_ad(list_ad[:middle]       ),				sortlist_ad(list_ad[middle:]       )       )


def   __UpperCAmelCase     (	_snake_case		:       list[int]       ):

   if len(_snake_case       ) <= 1:
      return collection
   _lowercase							     =       []
   _lowercase							     =       False
   for i in range(0,				len(_snake_case       ),				2       ):
      if i == len(_snake_case       ) - 1:
         _lowercase							     =       True
      else:
         if collection[i] < collection[i + 1]:
            two_paired_list.append([collection[i], collection[i + 1]]       )
         else:
            two_paired_list.append([collection[i + 1], collection[i]]       )
   _lowercase							     =       sortlist_ad(_snake_case       )
   _lowercase							     =       [i[0] for i in sorted_list_ad]
   result.append(sorted_list_ad[-1][1]       )
   if has_last_odd_item:
      _lowercase							     =       collection[-1]
      _lowercase							     =       binary_search_insertion(_snake_case,				_snake_case       )
   _lowercase							     =       False
   for i in range(len(_snake_case       ) - 1       ):
      if result[i] == collection[-1] and has_last_odd_item:
         _lowercase							     =       True
      _lowercase							     =       sorted_list_ad[i][1]
      # If last_odd_item is inserted before the item's index,
      # you should forward index one more.
      if is_last_odd_item_inserted_before_this_index:
         _lowercase							     =       result[: i + 2] + binary_search_insertion(result[i + 2 :],				_snake_case       )
      else:
         _lowercase							     =       result[: i + 1] + binary_search_insertion(result[i + 1 :],				_snake_case       )

   return result


if __name__ == "__main__":
  __UpperCamelCase       :    List[Any]         =		input("Enter numbers separated by a comma:\n").strip()
  __UpperCamelCase       :    Dict         =		[int(item) for item in user_input.split(",")]
  print(merge_insertion_sort(unsorted))
"""simple docstring"""


from typing import TYPE_CHECKING

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


__UpperCamelCase       :    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       :    Optional[int]         =		["OpenAIGPTTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    List[Any]         =		[
      "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       :    Tuple         =		[
      "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       :    str         =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)