


'''simple docstring'''






from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload




class 	a       :
 def __init__(	self ,			__magic_name__ = None  )   ->							None:
      if components is None:
           _a      =							[]
      _a      =							list(__magic_name__  )
 def __len__(	self  )   ->							int:
      return len(self.__components  )
 def __str__(	self  )   ->							str:
      return "(" + ",".join(map(__magic_name__ ,			self.__components  )  ) + ")"
 def __add__(	self ,			__magic_name__  )   ->							Vector:
      _a      =							len(self  )
      if size == len(__magic_name__  ):
           _a      =							[self.__components[i] + other.component(__magic_name__  ) for i in range(__magic_name__  )]
           return Vector(__magic_name__  )
      else:
           raise Exception('must have the same size'  )
 def __sub__(	self ,			__magic_name__  )   ->							Vector:
      _a      =							len(self  )
      if size == len(__magic_name__  ):
           _a      =							[self.__components[i] - other.component(__magic_name__  ) for i in range(__magic_name__  )]
           return Vector(__magic_name__  )
      else:  # error case
           raise Exception('must have the same size'  )
 @overload
 def __mul__(	self ,			__magic_name__  )   ->							Vector:
      ...
 @overload
 def __mul__(	self ,			__magic_name__  )   ->							float:
      ...
 def __mul__(	self ,			__magic_name__  )   ->							float | Vector:
      if isinstance(__magic_name__ ,			(float, int)  ):
           _a      =							[c * other for c in self.__components]
           return Vector(__magic_name__  )
      elif isinstance(__magic_name__ ,			__magic_name__  ) and len(self  ) == len(__magic_name__  ):
           _a      =							len(self  )
           _a      =							[self.__components[i] * other.component(__magic_name__  ) for i in range(__magic_name__  )]
           return sum(__magic_name__  )
      else:  # error case
           raise Exception('invalid operand!'  )
 def 							__UpperCAmelCase						(	self  )   ->							Vector:
      return Vector(self.__components  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							float:
      if isinstance(__magic_name__ ,			__magic_name__  ) and -len(self.__components  ) <= i < len(self.__components  ):
           return self.__components[i]
      else:
           raise Exception('index out of range'  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							None:
      assert -len(self.__components  ) <= pos < len(self.__components  )
      _a      =							value
 def 							__UpperCAmelCase						(	self  )   ->							float:
      if len(self.__components  ) == 0:
           raise Exception('Vector is empty'  )
      _a      =							[c**2 for c in self.__components]
      return math.sqrt(sum(__magic_name__  )  )




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = False  )   ->							float:
      _a      =							self * other
      _a      =							self.euclidean_length() * other.euclidean_length()
      if deg:
           return math.degrees(math.acos(num / den  )  )
      else:
           return math.acos(num / den  )






def 		_A			(lowerCAmelCase__					:int						)				->					Vector:




     '''simple docstring'''



     assert isinstance(lowerCAmelCase__			,  lowerCAmelCase__						)
     return Vector([0] * dimension						)






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:int						)				->					Vector:




     '''simple docstring'''



     assert isinstance(lowerCAmelCase__			,  lowerCAmelCase__						) and (isinstance(lowerCAmelCase__			,  lowerCAmelCase__						))
     _a      =							[0] * dimension
     _a      =							1
     return Vector(lowerCAmelCase__						)






def 		_A			(lowerCAmelCase__					:float			,  lowerCAmelCase__					:Vector			,  lowerCAmelCase__					:Vector						)				->					Vector:




     '''simple docstring'''



     assert (
         isinstance(lowerCAmelCase__			,  lowerCAmelCase__						)
         and isinstance(lowerCAmelCase__			,  lowerCAmelCase__						)
         and (isinstance(lowerCAmelCase__			,  (int, float)						))
     )
     return x * scalar + y






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:int						)				->					Vector:




     '''simple docstring'''



     random.seed(lowerCAmelCase__						)
     _a      =							[random.randint(lowerCAmelCase__			,  lowerCAmelCase__						) for _ in range(lowerCAmelCase__						)]
     return Vector(lowerCAmelCase__						)




class 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							None:
      _a      =							matrix
      _a      =							w
      _a      =							h
 def __str__(	self  )   ->							str:
      _a      =							''
      for i in range(self.__height  ):
           ans += "|"
           for j in range(self.__width  ):
                if j < self.__width - 1:
                     ans += str(self.__matrix[i][j]  ) + ","
                else:
                     ans += str(self.__matrix[i][j]  ) + "|\n"
      return ans
 def __add__(	self ,			__magic_name__  )   ->							Matrix:
      if self.__width == other.width() and self.__height == other.height():
           _a      =							[]
           for i in range(self.__height  ):
                _a      =							[
                    self.__matrix[i][j] + other.component(__magic_name__ ,			__magic_name__  )
                    for j in range(self.__width  )
                ]
                matrix.append(__magic_name__  )
           return Matrix(__magic_name__ ,			self.__width ,			self.__height  )
      else:
           raise Exception('matrix must have the same dimension!'  )
 def __sub__(	self ,			__magic_name__  )   ->							Matrix:
      if self.__width == other.width() and self.__height == other.height():
           _a      =							[]
           for i in range(self.__height  ):
                _a      =							[
                    self.__matrix[i][j] - other.component(__magic_name__ ,			__magic_name__  )
                    for j in range(self.__width  )
                ]
                matrix.append(__magic_name__  )
           return Matrix(__magic_name__ ,			self.__width ,			self.__height  )
      else:
           raise Exception('matrices must have the same dimension!'  )
 @overload
 def __mul__(	self ,			__magic_name__  )   ->							Matrix:
      ...
 @overload
 def __mul__(	self ,			__magic_name__  )   ->							Vector:
      ...
 def __mul__(	self ,			__magic_name__  )   ->							Vector | Matrix:
      if isinstance(__magic_name__ ,			__magic_name__  ):  # matrix-vector
           if len(__magic_name__  ) == self.__width:
                _a      =							zero_vector(self.__height  )
                for i in range(self.__height  ):
                     _a      =							[
                         self.__matrix[i][j] * other.component(__magic_name__  )
                         for j in range(self.__width  )
                     ]
                     ans.change_component(__magic_name__ ,			sum(__magic_name__  )  )
                return ans
           else:
                raise Exception(
                    'vector must have the same size as the '
                    'number of columns of the matrix!'  )
      elif isinstance(__magic_name__ ,			(int, float)  ):  # matrix-scalar
           _a      =							[
               [self.__matrix[i][j] * other for j in range(self.__width  )]
               for i in range(self.__height  )
           ]
           return Matrix(__magic_name__ ,			self.__width ,			self.__height  )
      return None
 def 							__UpperCAmelCase						(	self  )   ->							int:
      return self.__height
 def 							__UpperCAmelCase						(	self  )   ->							int:
      return self.__width
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							float:
      if 0 <= x < self.__height and 0 <= y < self.__width:
           return self.__matrix[x][y]
      else:
           raise Exception('change_component: indices out of bounds'  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							None:
      if 0 <= x < self.__height and 0 <= y < self.__width:
           _a      =							value
      else:
           raise Exception('change_component: indices out of bounds'  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							float:
      if self.__height != self.__width:
           raise Exception('Matrix is not square'  )
      _a      =							self.__matrix[:x] + self.__matrix[x + 1 :]
      for i in range(len(__magic_name__  )  ):
           _a      =							minor[i][:y] + minor[i][y + 1 :]
      return Matrix(__magic_name__ ,			self.__width - 1 ,			self.__height - 1  ).determinant()
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							float:
      if self.__height != self.__width:
           raise Exception('Matrix is not square'  )
      if 0 <= x < self.__height and 0 <= y < self.__width:
           return (-1) ** (x + y) * self.minor(__magic_name__ ,			__magic_name__  )
      else:
           raise Exception('Indices out of bounds'  )




 def 							__UpperCAmelCase						(	self  )   ->							float:
      if self.__height != self.__width:
           raise Exception('Matrix is not square'  )
      if self.__height < 1:
           raise Exception('Matrix has no element'  )
      elif self.__height == 1:
           return self.__matrix[0][0]
      elif self.__height == 2:
           return (
               self.__matrix[0][0] * self.__matrix[1][1]
               - self.__matrix[0][1] * self.__matrix[1][0]
           )
      else:
           _a      =							[
               self.__matrix[0][y] * self.cofactor(0 ,			__magic_name__  ) for y in range(self.__width  )
           ]
           return sum(__magic_name__  )






def 		_A			(lowerCAmelCase__					:int						)				->					Matrix:




     '''simple docstring'''



     _a      =							[[0] * n for _ in range(lowerCAmelCase__						)]
     return Matrix(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:int						)				->					Matrix:




     '''simple docstring'''



     random.seed(lowerCAmelCase__						)
     _a      =							[
         [random.randint(lowerCAmelCase__			,  lowerCAmelCase__						) for _ in range(lowerCAmelCase__						)] for _ in range(lowerCAmelCase__						)
     ]
     return Matrix(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)





'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

     for i, tensor in enumerate(lowerCAmelCase__						):
          if padding_side == "right":
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]

     return out_tensor.tolist()






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




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"pt"
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      import torch

      _a      =							'label' if 'label' in features[0].keys() else 'labels'
      _a      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =							self.tokenizer.pad(
          __magic_name__ ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors='pt' if labels is None else None ,			)

      if labels is None:
           return batch

      _a      =							torch.tensor(batch['entity_ids']  ).shape[1]
      _a      =							self.tokenizer.padding_side
      if padding_side == "right":
           _a      =							[
               list(__magic_name__  ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) for label in labels
           ]
      else:
           _a      =							[
               [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) + list(__magic_name__  ) for label in labels
           ]

      _a      =							[feature['ner_tags'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			-1 ,			__magic_name__ ,			__magic_name__  )
      _a      =							[feature['original_entity_spans'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			(-1, -1) ,			__magic_name__ ,			__magic_name__  )
      _a      =							{k: torch.tensor(__magic_name__ ,			dtype=torch.intaa  ) for k, v in batch.items()}

      return batch

