






'''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

UpperCamelCase							=logging.get_logger(__name__)




class     A    (      enum.Enum						):






 """simple docstring"""





 __a			:						Any      =						0
 __a			:						Optional[int]      =						1






@add_end_docstrings(SCREAMING_SNAKE_CASE__						)
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Any      =						'''generated'''
 def __init__(							self			,     *__lowerCAmelCase			,     **__lowerCAmelCase ):
       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   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   List[Any]					    =							{}
       if truncation is not None:
             UpperCamelCase_				:   Optional[Any]					    =							truncation

       UpperCamelCase_				:   Tuple					    =							generate_kwargs

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

       if clean_up_tokenization_spaces is not None:
             UpperCamelCase_				:   Any					    =							clean_up_tokenization_spaces

       if stop_sequence is not None:
             UpperCamelCase_				:   Union[str, Any]					    =							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.""" )
             UpperCamelCase_				:   Tuple					    =							stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       return True
 def   _UpperCAmelCase       (							self			,     *__lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   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""" )
             UpperCamelCase_				:   Any					    =							([prefix + arg for arg in args[0]],)
             UpperCamelCase_				:   str					    =							True

       elif isinstance(args[0]			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[str]					    =							(prefix + args[0],)
             UpperCamelCase_				:   Optional[Any]					    =							False
       else:
             raise ValueError(
                 F" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`" )
       UpperCamelCase_				:   Dict					    =							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 ):
       UpperCamelCase_				:   int					    =							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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							self._parse_and_tokenize(__lowerCAmelCase			,     truncation=__lowerCAmelCase			,     **__lowerCAmelCase )
       return inputs
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     **__lowerCAmelCase ):
       if self.framework == "pt":
             UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							model_inputs["""input_ids"""].shape
       elif self.framework == "tf":
             UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							tf.shape(model_inputs["""input_ids"""] ).numpy()

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





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=ReturnType.TEXT			,     __lowerCAmelCase=False ):
       UpperCamelCase_				:   Tuple					    =							[]
       for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                   UpperCamelCase_				:   Dict					    =							{F"{self.return_name}_token_ids": output_ids}
             elif return_type == ReturnType.TEXT:
                   UpperCamelCase_				:   Optional[int]					    =							{
                       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(SCREAMING_SNAKE_CASE__						)
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						List[str]      =						'''summary'''
 def __call__(							self			,     *__lowerCAmelCase			,     **__lowerCAmelCase ):
       return super().__call__(*__lowerCAmelCase			,     **__lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       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(SCREAMING_SNAKE_CASE__						)
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Dict      =						'''translation'''
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       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   _UpperCAmelCase       (							self			,     *__lowerCAmelCase			,     __lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None ):
       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   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     **__lowerCAmelCase ):
       UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							super()._sanitize_parameters(**__lowerCAmelCase )
       if src_lang is not None:
             UpperCamelCase_				:   Tuple					    =							src_lang
       if tgt_lang is not None:
             UpperCamelCase_				:   int					    =							tgt_lang
       if src_lang is None and tgt_lang is None:
             # Backward compatibility, direct arguments use is preferred.
             UpperCamelCase_				:   Dict					    =							kwargs.get("""task"""			,     self.task )
             UpperCamelCase_				:   Optional[int]					    =							task.split("""_""" )
             if task and len(__lowerCAmelCase ) == 4:
                   # translation, XX, to YY
                   UpperCamelCase_				:   List[str]					    =							items[1]
                   UpperCamelCase_				:   Optional[int]					    =							items[3]
       return preprocess_params, forward_params, postprocess_params





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














'''simple docstring'''

def 							snake_case					(     )       ->				int:





      """simple docstring"""

      return 1



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return 0 if x < 0 else two_pence(x - 2	) + one_pence()



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return 0 if x < 0 else five_pence(x - 5	) + two_pence(a_	)



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return 0 if x < 0 else ten_pence(x - 10	) + five_pence(a_	)



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return 0 if x < 0 else twenty_pence(x - 20	) + ten_pence(a_	)



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return 0 if x < 0 else fifty_pence(x - 50	) + twenty_pence(a_	)



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return 0 if x < 0 else one_pound(x - 100	) + fifty_pence(a_	)



def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      return 0 if x < 0 else two_pound(x - 200	) + one_pound(a_	)



def 							snake_case					(     a_							: int = 200	)       ->				int:





      """simple docstring"""

      return two_pound(a_	)


if __name__ == "__main__":
      print(solution(int(input().strip())))






