
"""simple docstring"""




from __future__ import annotations

from typing import Any







class   _lowercase						:


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 0  )->					None:
          UpperCAmelCase__							: Dict 							=						row, column
          UpperCAmelCase__							: Union[str, Any] 							=						[[default_value for c in range(__UpperCamelCase  )] for r in range(__UpperCamelCase  )]



     def __str__(      self  )->					str:
          UpperCAmelCase__							: List[Any] 							=						F"Matrix consist of {self.row} rows and {self.column} columns\n"

          # Make string identifier
          UpperCAmelCase__							: Union[str, Any] 							=						0
          for row_vector in self.array:
               for obj in row_vector:
                    UpperCAmelCase__							: int 							=						max(__UpperCamelCase ,			len(str(__UpperCamelCase  )  )  )
          UpperCAmelCase__							: Optional[Any] 							=						F"%{max_element_length}s"

          # Make string and return
          def single_line(__UpperCamelCase  ) -> str:
               nonlocal string_format_identifier
               UpperCAmelCase__							: Union[str, Any] 							=						"["
               line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
               line += "]"
               return line

          s += "\n".join(single_line(__UpperCamelCase  ) for row_vector in self.array  )
          return s



     def __repr__(      self  )->					str:
          return str(self  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					bool:
          if not (isinstance(__UpperCamelCase ,			(list, tuple)  ) and len(__UpperCamelCase  ) == 2):
               return False
          elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
               return False
          else:
               return True



     def __getitem__(      self ,			__UpperCamelCase  )->					Any:
          assert self.validate_indicies(__UpperCamelCase  )
          return self.array[loc[0]][loc[1]]



     def __setitem__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					None:
          assert self.validate_indicies(__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						value



     def __add__(      self ,			__UpperCamelCase  )->					Matrix:
          assert isinstance(__UpperCamelCase ,			__UpperCamelCase  )
          assert self.row == another.row and self.column == another.column

          # Add
          UpperCAmelCase__							: Union[str, Any] 							=						Matrix(self.row ,			self.column  )
          for r in range(self.row  ):
               for c in range(self.column  ):
                    UpperCAmelCase__							: Union[str, Any] 							=						self[r, c] + another[r, c]
          return result



     def __neg__(      self  )->					Matrix:
          UpperCAmelCase__							: Union[str, Any] 							=						Matrix(self.row ,			self.column  )
          for r in range(self.row  ):
               for c in range(self.column  ):
                    UpperCAmelCase__							: List[Any] 							=						-self[r, c]
          return result



     def __sub__(      self ,			__UpperCamelCase  )->					Matrix:
          return self + (-another)



     def __mul__(      self ,			__UpperCamelCase  )->					Matrix:

          if isinstance(__UpperCamelCase ,			(int, float)  ):  # Scalar multiplication
               UpperCAmelCase__							: Any 							=						Matrix(self.row ,			self.column  )
               for r in range(self.row  ):
                    for c in range(self.column  ):
                         UpperCAmelCase__							: Optional[int] 							=						self[r, c] * another
               return result
          elif isinstance(__UpperCamelCase ,			__UpperCamelCase  ):  # Matrix multiplication
               assert self.column == another.row
               UpperCAmelCase__							: int 							=						Matrix(self.row ,			another.column  )
               for r in range(self.row  ):
                    for c in range(another.column  ):
                         for i in range(self.column  ):
                              result[r, c] += self[r, i] * another[i, c]
               return result
          else:
               UpperCAmelCase__							: List[str] 							=						F"Unsupported type given for another ({type(__UpperCamelCase  )})"
               raise TypeError(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Matrix:
          UpperCAmelCase__							: Dict 							=						Matrix(self.column ,			self.row  )
          for r in range(self.row  ):
               for c in range(self.column  ):
                    UpperCAmelCase__							: List[str] 							=						self[r, c]
          return result



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Any:
          assert isinstance(__UpperCamelCase ,			__UpperCamelCase  ) and isinstance(__UpperCamelCase ,			__UpperCamelCase  )
          assert self.row == self.column == u.row == v.row  # u, v should be column vector
          assert u.column == v.column == 1  # u, v should be column vector

          # Calculate
          UpperCAmelCase__							: List[str] 							=						v.transpose()
          UpperCAmelCase__							: Dict 							=						(v_t * self * u)[0, 0] + 1
          if numerator_factor == 0:
               return None  # It's not invertable
          return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



 def 							a__ (				):






      '''simple docstring'''
      UpperCAmelCase__							: Optional[int] 							=						Matrix(3       ,    3       ,    0		)
      for i in range(3		):
           UpperCAmelCase__							: List[str] 							=						1
      print(F"a^(-1) is {ainv}"		)
      # u, v
      UpperCAmelCase__							: str 							=						Matrix(3       ,    1       ,    0		)
      UpperCAmelCase__							: Union[str, Any] 							=						1, 2, -3
      UpperCAmelCase__							: int 							=						Matrix(3       ,    1       ,    0		)
      UpperCAmelCase__							: List[Any] 							=						4, -2, 5
      print(F"u is {u}"		)
      print(F"v is {v}"		)
      print(F"uv^T is {u * v.transpose()}"		)
      # Sherman Morrison
      print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCAmelCase       ,    lowerCAmelCase		)}"		)



 def 							a__ (				):






      '''simple docstring'''
      import doctest

      doctest.testmod()

 testa()





"""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	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          UpperCAmelCase__							: Optional[int] 							=						np.full((len(lowerCAmelCase		), sequence_length, 2)       ,    lowerCAmelCase		)
     else:
          UpperCAmelCase__							: Optional[Any] 							=						np.full((len(lowerCAmelCase		), sequence_length)       ,    lowerCAmelCase		)

     for i, tensor in enumerate(lowerCAmelCase		):
          if padding_side == "right":
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Dict 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: Tuple 							=						tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: Optional[Any] 							=						tensor[:sequence_length]
               else:
                    UpperCAmelCase__							: int 							=						tensor[:sequence_length]

     return out_tensor.tolist()



def 							a__ (				lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						ord(lowerCAmelCase		)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     UpperCAmelCase__							: Optional[Any] 							=						unicodedata.category(lowerCAmelCase		)
     if cat.startswith("P"		):
          return True
     return False







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							True
     _A								=							None
     _A								=							None
     _A								=							-100
     _A								=							"pt"



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          import torch

          UpperCAmelCase__							: Optional[Any] 							=						"label" if "label" in features[0].keys() else "labels"
          UpperCAmelCase__							: Dict 							=						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
          UpperCAmelCase__							: str 							=						self.tokenizer.pad(
              __UpperCamelCase ,			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

          UpperCAmelCase__							: Optional[Any] 							=						torch.tensor(batch["entity_ids"]  ).shape[1]
          UpperCAmelCase__							: int 							=						self.tokenizer.padding_side
          if padding_side == "right":
               UpperCAmelCase__							: int 							=						[
                   list(__UpperCamelCase  ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) for label in labels
               ]
          else:
               UpperCAmelCase__							: List[Any] 							=						[
                   [self.label_pad_token_id] * (sequence_length - len(__UpperCamelCase  )) + list(__UpperCamelCase  ) for label in labels
               ]

          UpperCAmelCase__							: Optional[Any] 							=						[feature["ner_tags"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			-1 ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						[feature["original_entity_spans"] for feature in features]
          UpperCAmelCase__							: int 							=						padding_tensor(__UpperCamelCase ,			(-1, -1) ,			__UpperCamelCase ,			__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						{k: torch.tensor(__UpperCamelCase ,			dtype=torch.intaa  ) for k, v in batch.items()}

          return batch



