
"""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


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


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

    ''' ,       )
class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def 				lowercase_				(  self			,					A_							)  ->						np.ndarray:





         """simple docstring"""




         if self.framework == "tf":
           _lowercase:      Tuple   			=  tf.where(input_ids == self.tokenizer.mask_token_id							).numpy()
         elif self.framework == "pt":
           _lowercase:      str   			=  torch.nonzero(input_ids == self.tokenizer.mask_token_id			,					as_tuple=A_							)
         else:
           raise ValueError('''Unsupported framework'''							)
         return masked_index
       def 				lowercase_				(  self			,					A_							)  ->						np.ndarray:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.get_masked_index(A_							)
         _lowercase:      str   			=  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 				lowercase_				(  self			,					A_							)  ->						Any:





         """simple docstring"""




         if isinstance(A_			,					A_							):
           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(A_							)
       def 				lowercase_				(  self			,					A_			,					A_=None			,					**A_							)  ->						Dict[str, GenericTensor]:





         """simple docstring"""




         if return_tensors is None:
           _lowercase:      Tuple   			=  self.framework
         _lowercase:      int   			=  self.tokenizer(A_			,					return_tensors=A_							)
         self.ensure_exactly_one_mask_token(A_							)
         return model_inputs
       def 				lowercase_				(  self			,					A_							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Any   			=  self.model(**A_							)
         _lowercase:      Optional[int]   			=  model_inputs['''input_ids''']
         return model_outputs
       def 				lowercase_				(  self			,					A_			,					A_=5			,					A_=None							)  ->						Union[str, Any]:





         """simple docstring"""




         if target_ids is not None and target_ids.shape[0] < top_k:
           _lowercase:      Tuple   			=  target_ids.shape[0]
         _lowercase:      Dict   			=  model_outputs['''input_ids'''][0]
         _lowercase:      Optional[Any]   			=  model_outputs['''logits''']

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

           _lowercase:      List[Any]   			=  outputs.numpy()

           _lowercase:      int   			=  outputs[0, masked_index, :]
           _lowercase:      Tuple   			=  stable_softmax(A_			,					axis=-1							)
           if target_ids is not None:
             _lowercase:      Optional[int]   			=  tf.gather_nd(tf.squeeze(A_			,					0							)			,					target_ids.reshape(-1			,					1							)							)
             _lowercase:      Any   			=  tf.expand_dims(A_			,					0							)

           _lowercase:      Union[str, Any]   			=  tf.math.top_k(A_			,					k=A_							)
           _lowercase			,					_lowercase:      Dict   			=  topk.values.numpy(), topk.indices.numpy()
         else:
           _lowercase:      str   			=  torch.nonzero(input_ids == self.tokenizer.mask_token_id			,					as_tuple=A_							).squeeze(-1							)
           # Fill mask pipeline supports only one ${mask_token} per sample

           _lowercase:      Optional[int]   			=  outputs[0, masked_index, :]
           _lowercase:      Tuple   			=  logits.softmax(dim=-1							)
           if target_ids is not None:
             _lowercase:      Dict   			=  probs[..., target_ids]

           _lowercase			,					_lowercase:      Dict   			=  probs.topk(A_							)

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

             _lowercase:      List[str]   			=  p
             # Filter padding out:
             _lowercase:      Optional[int]   			=  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
             _lowercase:      List[str]   			=  self.tokenizer.decode(A_			,					skip_special_tokens=A_							)
             _lowercase:      int   			=  {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]							), '''sequence''': sequence}
             row.append(A_							)
           result.append(A_							)
         if single_mask:
           return result[0]
         return result
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Any:





         """simple docstring"""




         if isinstance(A_			,					A_							):
           _lowercase:      str   			=  [targets]
         try:
           _lowercase:      Dict   			=  self.tokenizer.get_vocab()
         except Exception:
           _lowercase:      Dict   			=  {}
         _lowercase:      int   			=  []
         for target in targets:
           _lowercase:      Optional[Any]   			=  vocab.get(A_			,					A_							)
           if id_ is None:
             _lowercase:      List[Any]   			=  self.tokenizer(
                 A_			,					add_special_tokens=A_			,					return_attention_mask=A_			,					return_token_type_ids=A_			,					max_length=1			,					truncation=A_			,					)['''input_ids''']
             if len(A_							) == 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
             _lowercase:      Union[str, Any]   			=  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_							)
         _lowercase:      Tuple   			=  list(set(A_							)							)
         if len(A_							) == 0:
           raise ValueError('''At least one target must be provided when passed.'''							)
         _lowercase:      Optional[int]   			=  np.array(A_							)
         return target_ids
       def 				lowercase_				(  self			,					A_=None			,					A_=None							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Any   			=  {}

         if targets is not None:
           _lowercase:      int   			=  self.get_target_ids(A_			,					A_							)
           _lowercase:      Optional[Any]   			=  target_ids

         if top_k is not None:
           _lowercase:      Union[str, Any]   			=  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			,					A_			,					*A_			,					**A_							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  super().__call__(A_			,					**A_							)
         if isinstance(A_			,					A_							) and len(A_							) == 1:
           return outputs[0]
         return outputs


"""simple docstring"""




from __future__ import annotations

import typing
from collections import Counter




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      typing.Counter[int]   			=  Counter()
  for base in range(1			,					max_perimeter + 1    ):
    for perpendicular in range(_UpperCamelCase			,					max_perimeter + 1    ):
      _lowercase:      str   			=  (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(_UpperCamelCase    ):
        _lowercase:      str   			=  int(base + perpendicular + hypotenuse    )
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets




def 			_lowerCAmelCase		(		_UpperCamelCase = 1_000    ):






  """simple docstring"""



  _lowercase:      int   			=  pythagorean_triple(_UpperCamelCase    )
  return triplets.most_common(1    )[0][0]


if __name__ == "__main__":
  print(f"""Perimeter {solution()} has maximum solutions""")
