'''simple docstring'''

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__a: int          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						enum.Enum					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    0
       SCREAMING_SNAKE_CASE							=    1



@add_end_docstrings(a__					)
class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "generated"

       def __init__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  Any:
        super().__init__(*__lowerCAmelCase	,   **__lowerCAmelCase )

        self.check_model_type(
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
            if self.framework == '''tf'''
            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   **__lowerCAmelCase	,   )  ->  List[str]:
        lowercase__				:    Optional[int]  =					{}
        if truncation is not None:
         lowercase__				:    Optional[int]  =					truncation

        lowercase__				:    Dict  =					generate_kwargs

        lowercase__				:    Tuple  =					{}
        if return_tensors is not None and return_type is None:
         lowercase__				:    Optional[int]  =					ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
         lowercase__				:    Tuple  =					return_type

        if clean_up_tokenization_spaces is not None:
         lowercase__				:    str  =					clean_up_tokenization_spaces

        if stop_sequence is not None:
         lowercase__				:    str  =					self.tokenizer.encode(__lowerCAmelCase	,   add_special_tokens=__lowerCAmelCase )
         if len(__lowerCAmelCase ) > 1:
          warnings.warn(
              '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
              ''' the stop sequence will be used as the stop sequence string in the interim.''' )
         lowercase__				:    Optional[int]  =					stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        return True

       def _lowerCAmelCase(						self	,   *__lowerCAmelCase	,   __lowerCAmelCase )  ->  str:
        lowercase__				:    Dict  =					self.model.config.prefix if self.model.config.prefix is not None else ''''''
        if isinstance(args[0]	,   __lowerCAmelCase ):
         if self.tokenizer.pad_token_id is None:
          raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input''' )
         lowercase__				:    Optional[Any]  =					([prefix + arg for arg in args[0]],)
         lowercase__				:    Union[str, Any]  =					True

        elif isinstance(args[0]	,   __lowerCAmelCase ):
         lowercase__				:    Optional[int]  =					(prefix + args[0],)
         lowercase__				:    Any  =					False
        else:
         raise ValueError(
             F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`""" )
        lowercase__				:    str  =					self.tokenizer(*__lowerCAmelCase	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase	,   return_tensors=self.framework )
        # This is produced by tokenizers but is an invalid generate kwargs
        if "token_type_ids" in inputs:
         del inputs["token_type_ids"]
        return inputs

       def __call__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  List[str]:
        lowercase__				:    Union[str, Any]  =					super().__call__(*__lowerCAmelCase	,   **__lowerCAmelCase )
        if (
            isinstance(args[0]	,   __lowerCAmelCase )
            and all(isinstance(__lowerCAmelCase	,   __lowerCAmelCase ) for el in args[0] )
            and all(len(__lowerCAmelCase ) == 1 for res in result )
        ):
         return [res[0] for res in result]
        return result

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE	,   **__lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Dict  =					self._parse_and_tokenize(__lowerCAmelCase	,   truncation=__lowerCAmelCase	,   **__lowerCAmelCase )
        return inputs

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  Dict:
        if self.framework == "pt":
         lowercase__ ,							lowercase__				:    Union[str, Any]  =					model_inputs['''input_ids'''].shape
        elif self.framework == "tf":
         lowercase__ ,							lowercase__				:    str  =					tf.shape(model_inputs['''input_ids'''] ).numpy()

        lowercase__				:    List[str]  =					generate_kwargs.get('''min_length'''	,   self.model.config.min_length )
        lowercase__				:    Optional[Any]  =					generate_kwargs.get('''max_length'''	,   self.model.config.max_length )
        self.check_inputs(__lowerCAmelCase	,   generate_kwargs['''min_length''']	,   generate_kwargs['''max_length'''] )
        lowercase__				:    List[Any]  =					self.model.generate(**__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					output_ids.shape[0]
        if self.framework == "pt":
         lowercase__				:    int  =					output_ids.reshape(__lowerCAmelCase	,   out_b // in_b	,   *output_ids.shape[1:] )
        elif self.framework == "tf":
         lowercase__				:    List[str]  =					tf.reshape(__lowerCAmelCase	,   (in_b, out_b // in_b, *output_ids.shape[1:]) )
        return {"output_ids": output_ids}


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=ReturnType.TEXT	,   __lowerCAmelCase=False )  ->  Any:
        lowercase__				:    Optional[Any]  =					[]
        for output_ids in model_outputs["output_ids"][0]:
         if return_type == ReturnType.TENSORS:
          lowercase__				:    Tuple  =					{F"""{self.return_name}_token_ids""": output_ids}
         elif return_type == ReturnType.TEXT:
          lowercase__				:    Tuple  =					{
              F"""{self.return_name}_text""": self.tokenizer.decode(
                  __lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase	,   )
          }
         records.append(__lowerCAmelCase )
        return records



@add_end_docstrings(a__					)
class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "summary"

       def __call__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  str:
        return super().__call__(*__lowerCAmelCase	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  bool:
        if max_length < min_length:
         logger.warning(F"""Your min_length={min_length} must be inferior than your max_length={max_length}.""" )

        if input_length < max_length:
         logger.warning(
             F"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
             '''a summarization task, where outputs shorter than the input are typically wanted, you might '''
             F"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})""" )



@add_end_docstrings(a__					)
class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "translation"

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        if input_length > 0.9 * max_length:
         logger.warning(
             F"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
             '''increasing your max_length manually, e.g. translator(\'...\', max_length=400)''' )
        return True

       def _lowerCAmelCase(						self	,   *__lowerCAmelCase	,   __lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None )  ->  Dict:
        if getattr(self.tokenizer	,   '''_build_translation_inputs'''	,   __lowerCAmelCase ):
         return self.tokenizer._build_translation_inputs(
             *__lowerCAmelCase	,   return_tensors=self.framework	,   truncation=__lowerCAmelCase	,   src_lang=__lowerCAmelCase	,   tgt_lang=__lowerCAmelCase )
        else:
         return super()._parse_and_tokenize(*__lowerCAmelCase	,   truncation=__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   **__lowerCAmelCase )  ->  Union[str, Any]:
        lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[int]  =					super()._sanitize_parameters(**__lowerCAmelCase )
        if src_lang is not None:
         lowercase__				:    str  =					src_lang
        if tgt_lang is not None:
         lowercase__				:    Optional[Any]  =					tgt_lang
        if src_lang is None and tgt_lang is None:
         # Backward compatibility, direct arguments use is preferred.
         lowercase__				:    int  =					kwargs.get('''task'''	,   self.task )
         lowercase__				:    Optional[Any]  =					task.split('''_''' )
         if task and len(__lowerCAmelCase ) == 4:
          # translation, XX, to YY
          lowercase__				:    Optional[Any]  =					items[1]
          lowercase__				:    Dict  =					items[3]
        return preprocess_params, forward_params, postprocess_params


       def __call__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  List[str]:
        return super().__call__(*__lowerCAmelCase	,   **__lowerCAmelCase )


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__ ,							lowercase__				:    Optional[int]  =					[], []
 while len(UpperCAmelCase     ) > 1:
  lowercase__ ,							lowercase__				:    List[str]  =					min(UpperCAmelCase     ), max(UpperCAmelCase     )
  start.append(UpperCAmelCase     )
  end.append(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
  collection.remove(UpperCAmelCase     )
 end.reverse()
 return start + collection + end


if __name__ == "__main__":
  __a: Optional[int]          =   input("""Enter numbers separated by a comma:\n""").strip()
  __a: Tuple          =   [int(item) for item in user_input.split(""",""")]
  print(*merge_sort(unsorted), sep=""",""")

