





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


UpperCAmelCase	:	Optional[Any]		     =       logging.get_logger(__name__)







@add_end_docstrings(
    UpperCamelCase__						,				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 				__lowerCAmelCase       (    UpperCamelCase__):







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						np.ndarray:



        '''simple docstring'''




        if self.framework == "tf":
          a__	:					Optional[Any]										=tf.where(input_ids == self.tokenizer.mask_token_id							).numpy()
        elif self.framework == "pt":
          a__	:					Dict										=torch.nonzero(input_ids == self.tokenizer.mask_token_id		,      as_tuple=lowerCAmelCase__							)
        else:
          raise ValueError("Unsupported framework"							)
        return masked_index







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						np.ndarray:



        '''simple docstring'''




        a__	:					int										=self.get_masked_index(lowerCAmelCase__							)
        a__	:					Dict										=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		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							):
          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(lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=None		,      **lowerCAmelCase__							)   ->						Dict[str, GenericTensor]:



        '''simple docstring'''




        if return_tensors is None:
          a__	:					Optional[int]										=self.framework
        a__	:					Optional[Any]										=self.tokenizer(lowerCAmelCase__		,      return_tensors=lowerCAmelCase__							)
        self.ensure_exactly_one_mask_token(lowerCAmelCase__							)
        return model_inputs







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Tuple										=self.model(**lowerCAmelCase__							)
        a__	:					Optional[int]										=model_inputs["input_ids"]
        return model_outputs







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=5		,      lowerCAmelCase__=None							)   ->						Tuple:



        '''simple docstring'''




        if target_ids is not None and target_ids.shape[0] < top_k:
          a__	:					Optional[Any]										=target_ids.shape[0]
        a__	:					Tuple										=model_outputs["input_ids"][0]
        a__	:					Union[str, Any]										=model_outputs["logits"]

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

          a__	:					Any										=outputs.numpy()

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

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

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

          a__					,							a__	:					Any										=probs.topk(lowerCAmelCase__							)

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

            a__	:					Tuple										=p
            # Filter padding out:
            a__	:					Optional[Any]										=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
            a__	:					int										=self.tokenizer.decode(lowerCAmelCase__		,      skip_special_tokens=lowerCAmelCase__							)
            a__	:					Tuple										={"score": v, "token": p, "token_str": self.tokenizer.decode([p]							), "sequence": sequence}
            row.append(lowerCAmelCase__							)
          result.append(lowerCAmelCase__							)
        if single_mask:
          return result[0]
        return result







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=None							)   ->						List[str]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Tuple										={}

        if targets is not None:
          a__	:					Optional[int]										=self.get_target_ids(lowerCAmelCase__		,      lowerCAmelCase__							)
          a__	:					List[str]										=target_ids

        if top_k is not None:
          a__	:					int										=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		,      lowerCAmelCase__		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        a__	:					Any										=super().__call__(lowerCAmelCase__		,      **lowerCAmelCase__							)
        if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) and len(lowerCAmelCase__							) == 1:
          return outputs[0]
        return outputs










from __future__ import annotations

from collections.abc import Callable

UpperCAmelCase__   	=							list[list[float | int]]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Matrix:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(size + 1						)] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for row in range(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =matrix[row][col]

  _lowercase							      =vector[row][0]

 _lowercase							      =0
 _lowercase							      =0
 while row < size and col < size:
  # pivoting
  _lowercase							      =max((abs(augmented[rowa][col]						), rowa) for rowa in range(__snake_case     ,						__snake_case						)						)[
      1
  ]
  if augmented[pivot_row][col] == 0:
   col += 1
   continue
  else:
   _lowercase      ,    _lowercase							      =augmented[pivot_row], augmented[row]

  for rowa in range(row + 1     ,						__snake_case						):
   _lowercase							      =augmented[rowa][col] / augmented[row][col]
   _lowercase							      =0
   for cola in range(col + 1     ,						size + 1						):
    augmented[rowa][cola] -= augmented[row][cola] * ratio

  row += 1
  col += 1

 # back substitution
 for col in range(1     ,						__snake_case						):
  for row in range(__snake_case						):
   _lowercase							      =augmented[row][col] / augmented[col][col]
   for cola in range(__snake_case     ,						size + 1						):
    augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
 return [
     [round(augmented[row][size] / augmented[row][row]     ,						10						)] for row in range(__snake_case						)
 ]
def 				UpperCAmelCase_					(							__snake_case						)     ->			Callable[[int], int]:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(__snake_case						)] for _ in range(__snake_case						)]
 _lowercase							      =[[0] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for x_val, y_val in enumerate(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =(x_val + 1) ** (size - col - 1)
  _lowercase							      =y_val

 _lowercase							      =solve(__snake_case     ,						__snake_case						)

 def interpolated_func(__snake_case						) -> int:
  return sum(
      round(coeffs[x_val][0]						) * (var ** (size - x_val - 1))
      for x_val in range(__snake_case						)						)

 return interpolated_func
def 				UpperCAmelCase_					(							__snake_case						)     ->			int:







 """simple docstring"""


 return (
     1
     - variable
     + variable**2
     - variable**3
     + variable**4
     - variable**5
     + variable**6
     - variable**7
     + variable**8
     - variable**9
     + variable**10
 )
def 				UpperCAmelCase_					(							__snake_case = question_function     ,						__snake_case = 10						)     ->			int:







 """simple docstring"""


 _lowercase							      =[func(__snake_case						) for x_val in range(1     ,						order + 1						)]

 _lowercase							      =[
     interpolate(data_points[:max_coeff]						) for max_coeff in range(1     ,						order + 1						)
 ]

 _lowercase							      =0
 _lowercase							      =42
 _lowercase							      =42

 for poly in polynomials:
  _lowercase							      =1
  while func(__snake_case						) == poly(__snake_case						):
   x_val += 1

  ret += poly(__snake_case						)

 return ret


if __name__ == "__main__":
  print(f'''{solution() = }''')

