

"""simple docstring"""




from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
     import tensorflow as tf

     from ..tf_utils import stable_softmax


if is_torch_available():
     import torch


SCREAMING_SNAKE_CASE_     :			Optional[int]	  =							logging.get_logger(__name__)


@add_end_docstrings(
    __a				,       r'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '				,       )
class 		_A       (			__a					):





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					np.ndarray:
         if self.framework == "tf":
            lowerCamelCase__						=			tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()
         elif self.framework == "pt":
            lowerCamelCase__						=			torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,						as_tuple=UpperCAmelCase__		)
         else:
            raise ValueError("Unsupported framework"		)
         return masked_index





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					np.ndarray:
         lowerCamelCase__						=			self.get_masked_index(UpperCAmelCase__		)
         lowerCamelCase__						=			np.prod(masked_index.shape		)
         if numel < 1:
            raise PipelineException(
                "fill-mask"    ,						self.model.base_model_prefix    ,						f'No mask_token ({self.tokenizer.mask_token}) found on the input'    ,						)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         if isinstance(UpperCAmelCase__    ,						UpperCAmelCase__		):
            for model_input in model_inputs:
               self._ensure_exactly_one_mask_token(model_input["input_ids"][0]		)
         else:
            for input_ids in model_inputs["input_ids"]:
               self._ensure_exactly_one_mask_token(UpperCAmelCase__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__		)   ->					Dict[str, GenericTensor]:
         if return_tensors is None:
            lowerCamelCase__						=			self.framework
         lowerCamelCase__						=			self.tokenizer(UpperCAmelCase__    ,						return_tensors=UpperCAmelCase__		)
         self.ensure_exactly_one_mask_token(UpperCAmelCase__		)
         return model_inputs





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			self.model(**UpperCAmelCase__		)
         lowerCamelCase__						=			model_inputs["input_ids"]
         return model_outputs





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=5    ,						SCREAMING_SNAKE_CASE__=None		)   ->					List[str]:
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
            lowerCamelCase__						=			target_ids.shape[0]
         lowerCamelCase__						=			model_outputs["input_ids"][0]
         lowerCamelCase__						=			model_outputs["logits"]

         if self.framework == "tf":
            lowerCamelCase__						=			tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()[:, 0]

            lowerCamelCase__						=			outputs.numpy()

            lowerCamelCase__						=			outputs[0, masked_index, :]
            lowerCamelCase__						=			stable_softmax(UpperCAmelCase__    ,						axis=-1		)
            if target_ids is not None:
               lowerCamelCase__						=			tf.gather_nd(tf.squeeze(UpperCAmelCase__    ,						0		)    ,						target_ids.reshape(-1    ,						1		)		)
               lowerCamelCase__						=			tf.expand_dims(UpperCAmelCase__    ,						0		)

            lowerCamelCase__						=			tf.math.top_k(UpperCAmelCase__    ,						k=UpperCAmelCase__		)
            lowerCamelCase__					,       lowerCamelCase__						=			topk.values.numpy(), topk.indices.numpy()
         else:
            lowerCamelCase__						=			torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,						as_tuple=UpperCAmelCase__		).squeeze(-1		)
            # Fill mask pipeline supports only one ${mask_token} per sample

            lowerCamelCase__						=			outputs[0, masked_index, :]
            lowerCamelCase__						=			logits.softmax(dim=-1		)
            if target_ids is not None:
               lowerCamelCase__						=			probs[..., target_ids]

            lowerCamelCase__					,       lowerCamelCase__						=			probs.topk(UpperCAmelCase__		)

         lowerCamelCase__						=			[]
         lowerCamelCase__						=			values.shape[0] == 1
         for i, (_values, _predictions) in enumerate(zip(values.tolist()    ,						predictions.tolist()		)		):
            lowerCamelCase__						=			[]
            for v, p in zip(_values    ,						_predictions		):
               # Copy is important since we're going to modify this array in place
               lowerCamelCase__						=			input_ids.numpy().copy()
               if target_ids is not None:
                  lowerCamelCase__						=			target_ids[p].tolist()

               lowerCamelCase__						=			p
               # Filter padding out:
               lowerCamelCase__						=			tokens[np.where(tokens != self.tokenizer.pad_token_id		)]
               # Originally we skip special tokens to give readable output.
               # For multi masks though, the other [MASK] would be removed otherwise
               # making the output look odd, so we add them back
               lowerCamelCase__						=			self.tokenizer.decode(UpperCAmelCase__    ,						skip_special_tokens=UpperCAmelCase__		)
               lowerCamelCase__						=			{"score": v, "token": p, "token_str": self.tokenizer.decode([p]		), "sequence": sequence}
               row.append(UpperCAmelCase__		)
            result.append(UpperCAmelCase__		)
         if single_mask:
            return result[0]
         return result





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					Union[str, Any]:
         if isinstance(UpperCAmelCase__    ,						UpperCAmelCase__		):
            lowerCamelCase__						=			[targets]
         try:
            lowerCamelCase__						=			self.tokenizer.get_vocab()
         except Exception:
            lowerCamelCase__						=			{}
         lowerCamelCase__						=			[]
         for target in targets:
            lowerCamelCase__						=			vocab.get(UpperCAmelCase__    ,						UpperCAmelCase__		)
            if id_ is None:
               lowerCamelCase__						=			self.tokenizer(
                   UpperCAmelCase__    ,						add_special_tokens=UpperCAmelCase__    ,						return_attention_mask=UpperCAmelCase__    ,						return_token_type_ids=UpperCAmelCase__    ,						max_length=1    ,						truncation=UpperCAmelCase__    ,						)["input_ids"]
               if len(UpperCAmelCase__		) == 0:
                  logger.warning(
                      f'The specified target token `{target}` does not exist in the model vocabulary. '
                      "We cannot replace it with anything meaningful, ignoring it"		)
                  continue
               lowerCamelCase__						=			input_ids[0]
               # XXX: If users encounter this pass
               # it becomes pretty slow, so let's make sure
               # The warning enables them to fix the input to
               # get faster performance.
               logger.warning(
                   f'The specified target token `{target}` does not exist in the model vocabulary. '
                   f'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_		)}`.'		)
            target_ids.append(id_		)
         lowerCamelCase__						=			list(set(UpperCAmelCase__		)		)
         if len(UpperCAmelCase__		) == 0:
            raise ValueError("At least one target must be provided when passed."		)
         lowerCamelCase__						=			np.array(UpperCAmelCase__		)
         return target_ids





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None		)   ->					Dict:
         lowerCamelCase__						=			{}

         if targets is not None:
            lowerCamelCase__						=			self.get_target_ids(UpperCAmelCase__    ,						UpperCAmelCase__		)
            lowerCamelCase__						=			target_ids

         if top_k is not None:
            lowerCamelCase__						=			top_k

         if self.tokenizer.mask_token_id is None:
            raise PipelineException(
                "fill-mask"    ,						self.model.base_model_prefix    ,						"The tokenizer does not define a `mask_token`."		)
         return {}, {}, postprocess_params





      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					str:
         lowerCamelCase__						=			super().__call__(UpperCAmelCase__    ,						**UpperCAmelCase__		)
         if isinstance(UpperCAmelCase__    ,						UpperCAmelCase__		) and len(UpperCAmelCase__		) == 1:
            return outputs[0]
         return outputs







"""simple docstring"""




def 	UpperCAmelCase__   (				)      ->      int:




   """simple docstring"""
   return 1



def 	UpperCAmelCase__   (				A__   )      ->      int:




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



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else five_pence(x - 5   ) + two_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else ten_pence(x - 10   ) + five_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else twenty_pence(x - 20   ) + ten_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else fifty_pence(x - 50   ) + twenty_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else one_pound(x - 100   ) + fifty_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else two_pound(x - 200   ) + one_pound(A__   )



def 	UpperCAmelCase__   (				A__ = 200   )      ->      int:




   """simple docstring"""
   return two_pound(A__   )


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




