





'''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

_SCREAMING_SNAKE_CASE		       =							logging.get_logger(__name__)

class       lowerCAmelCase_						(						enum.Enum						):
 __lowerCamelCase  :							List[str]				     =    0
 __lowerCamelCase  :							int				     =    1







@add_end_docstrings(snake_case__						)
class       lowerCAmelCase_						(						snake_case__						):
 __lowerCamelCase  :							str				     =    "generated"




 def __init__(		self	,   *_lowerCAmelCase	,   **_lowerCAmelCase						)			->							int:
     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 							_snake_case      (		self	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   **_lowerCAmelCase	,   )			->							Union[str, Any]:
     _lowerCAmelCase			=					{}
     if truncation is not None:
         _lowerCAmelCase			=					truncation

     _lowerCAmelCase			=					generate_kwargs

     _lowerCAmelCase			=					{}
     if return_tensors is not None and return_type is None:
         _lowerCAmelCase			=					ReturnType.TENSORS if return_tensors else ReturnType.TEXT
     if return_type is not None:
         _lowerCAmelCase			=					return_type

     if clean_up_tokenization_spaces is not None:
         _lowerCAmelCase			=					clean_up_tokenization_spaces

     if stop_sequence is not None:
         _lowerCAmelCase			=					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."						)
         _lowerCAmelCase			=					stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Union[str, Any]:
     return True




 def 							_snake_case      (		self	,   *_lowerCAmelCase	,   _lowerCAmelCase						)			->							Union[str, Any]:
     _lowerCAmelCase			=					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"						)
         _lowerCAmelCase			=					([prefix + arg for arg in args[0]],)
         _lowerCAmelCase			=					True

     elif isinstance(args[0]	,   _lowerCAmelCase						):
         _lowerCAmelCase			=					(prefix + args[0],)
         _lowerCAmelCase			=					False
     else:
         raise ValueError(
             f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''						)
     _lowerCAmelCase			=					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						)			->							Optional[Any]:
     _lowerCAmelCase			=					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 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE	,   **_lowerCAmelCase						)			->							str:
     _lowerCAmelCase			=					self._parse_and_tokenize(_lowerCAmelCase	,   truncation=_lowerCAmelCase	,   **_lowerCAmelCase						)
     return inputs




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   **_lowerCAmelCase						)			->							List[str]:







     if self.framework == "pt":
         _lowerCAmelCase	,	_lowerCAmelCase			=					model_inputs["input_ids"].shape
     elif self.framework == "tf":
         _lowerCAmelCase	,	_lowerCAmelCase			=					tf.shape(model_inputs["input_ids"]						).numpy()

     _lowerCAmelCase			=					generate_kwargs.get("min_length"	,   self.model.config.min_length						)
     _lowerCAmelCase			=					generate_kwargs.get("max_length"	,   self.model.config.max_length						)
     self.check_inputs(_lowerCAmelCase	,   generate_kwargs["min_length"]	,   generate_kwargs["max_length"]						)
     _lowerCAmelCase			=					self.model.generate(**_lowerCAmelCase	,   **_lowerCAmelCase						)
     _lowerCAmelCase			=					output_ids.shape[0]
     if self.framework == "pt":
         _lowerCAmelCase			=					output_ids.reshape(_lowerCAmelCase	,   out_b // in_b	,   *output_ids.shape[1:]						)
     elif self.framework == "tf":
         _lowerCAmelCase			=					tf.reshape(_lowerCAmelCase	,   (in_b, out_b // in_b, *output_ids.shape[1:])						)
     return {"output_ids": output_ids}





 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase=ReturnType.TEXT	,   _lowerCAmelCase=False						)			->							int:
     _lowerCAmelCase			=					[]
     for output_ids in model_outputs["output_ids"][0]:
         if return_type == ReturnType.TENSORS:
             _lowerCAmelCase			=					{f'''{self.return_name}_token_ids''': output_ids}
         elif return_type == ReturnType.TEXT:
             _lowerCAmelCase			=					{
                 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(snake_case__						)
class       lowerCAmelCase_						(						snake_case__						):
 __lowerCamelCase  :							int				     =    "summary"




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





 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							List[str]:







     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(snake_case__						)
class       lowerCAmelCase_						(						snake_case__						):
 __lowerCamelCase  :							Union[str, Any]				     =    "translation"




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							str:







     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 							_snake_case      (		self	,   *_lowerCAmelCase	,   _lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None						)			->							str:







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





 def __call__(		self	,   *_lowerCAmelCase	,   **_lowerCAmelCase						)			->							Optional[Any]:
     return super().__call__(*_lowerCAmelCase	,   **_lowerCAmelCase						)










def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


if __name__ == "__main__":
		_a      	=  input('''Enter numbers separated by a comma:\n''').strip()
		_a      	=  [int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
