"""simple docstring"""
from __future__ import annotations

from decimal import Decimal

from numpy import array







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  a__:     Any										=	Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(A__   ) == 2 and len(matrix[0]   ) == 2 and len(matrix[1]   ) == 2:
    # Calculate the determinant of the matrix
    a__:     List[Any]										=	float(
        d(matrix[0][0]   ) * d(matrix[1][1]   ) - d(matrix[1][0]   ) * d(matrix[0][1]   )   )
    if determinant == 0:
      raise ValueError('This matrix has no inverse.'   )

    # Creates a copy of the matrix with swapped positions of the elements
    a__:     List[Any]										=	[[0.0, 0.0], [0.0, 0.0]]
    a__							,    a__:     List[Any]										=	matrix[1][1], matrix[0][0]
    a__							,    a__:     Tuple										=	-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(A__   )   ) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(A__   ) == 3
      and len(matrix[0]   ) == 3
      and len(matrix[1]   ) == 3
      and len(matrix[2]   ) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    a__:     Union[str, Any]										=	float(
        (
            (d(matrix[0][0]   ) * d(matrix[1][1]   ) * d(matrix[2][2]   ))
            + (d(matrix[0][1]   ) * d(matrix[1][2]   ) * d(matrix[2][0]   ))
            + (d(matrix[0][2]   ) * d(matrix[1][0]   ) * d(matrix[2][1]   ))
        )
        - (
            (d(matrix[0][2]   ) * d(matrix[1][1]   ) * d(matrix[2][0]   ))
            + (d(matrix[0][1]   ) * d(matrix[1][0]   ) * d(matrix[2][2]   ))
            + (d(matrix[0][0]   ) * d(matrix[1][2]   ) * d(matrix[2][1]   ))
        )   )
    if determinant == 0:
      raise ValueError('This matrix has no inverse.'   )

    # Creating cofactor matrix
    a__:     int										=	[
        [d(0.0   ), d(0.0   ), d(0.0   )],
        [d(0.0   ), d(0.0   ), d(0.0   )],
        [d(0.0   ), d(0.0   ), d(0.0   )],
    ]
    a__:     List[Any]										=	(d(matrix[1][1]   ) * d(matrix[2][2]   )) - (
        d(matrix[1][2]   ) * d(matrix[2][1]   )
    )
    a__:     List[Any]										=	-(
        (d(matrix[1][0]   ) * d(matrix[2][2]   )) - (d(matrix[1][2]   ) * d(matrix[2][0]   ))
    )
    a__:     Optional[int]										=	(d(matrix[1][0]   ) * d(matrix[2][1]   )) - (
        d(matrix[1][1]   ) * d(matrix[2][0]   )
    )
    a__:     Any										=	-(
        (d(matrix[0][1]   ) * d(matrix[2][2]   )) - (d(matrix[0][2]   ) * d(matrix[2][1]   ))
    )
    a__:     Optional[Any]										=	(d(matrix[0][0]   ) * d(matrix[2][2]   )) - (
        d(matrix[0][2]   ) * d(matrix[2][0]   )
    )
    a__:     Union[str, Any]										=	-(
        (d(matrix[0][0]   ) * d(matrix[2][1]   )) - (d(matrix[0][1]   ) * d(matrix[2][0]   ))
    )
    a__:     str										=	(d(matrix[0][1]   ) * d(matrix[1][2]   )) - (
        d(matrix[0][2]   ) * d(matrix[1][1]   )
    )
    a__:     int										=	-(
        (d(matrix[0][0]   ) * d(matrix[1][2]   )) - (d(matrix[0][2]   ) * d(matrix[1][0]   ))
    )
    a__:     Any										=	(d(matrix[0][0]   ) * d(matrix[1][1]   )) - (
        d(matrix[0][1]   ) * d(matrix[1][0]   )
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    a__:     int										=	array(A__   )
    for i in range(3   ):
      for j in range(3   ):
        a__:     int										=	cofactor_matrix[j][i]

            # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    a__:     Optional[Any]										=	array(A__   )
    for i in range(3   ):
      for j in range(3   ):
        inverse_matrix[i][j] /= d(A__   )

            # Calculate the inverse of the matrix
    return [[float(d(A__   )   ) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError('Please provide a matrix of size 2x2 or 3x3.'   )





'''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     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 if isinstance(A__     ,				A__      ):
  __lowercase							  = np.full((len(A__      ), sequence_length, 2)     ,				A__      )
 else:
  __lowercase							  = np.full((len(A__      ), sequence_length)     ,				A__      )

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

 return out_tensor.tolist()


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = ord(A__      )
 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
 __lowercase							  = unicodedata.category(A__      )
 if cat.startswith('''P'''      ):
  return True
 return False






@dataclass
class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  PreTrainedTokenizerBase
  SCREAMING_SNAKE_CASE    :  Union[bool, str, PaddingStrategy]							=			True
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			None
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			None
  SCREAMING_SNAKE_CASE    :  int							=			-1_0_0
  SCREAMING_SNAKE_CASE    :  str							=			"pt"






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     List[str]			):
   import torch

   __lowercase							  = '''label''' if '''label''' in features[0].keys() else '''labels'''
   __lowercase							  = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   __lowercase							  = self.tokenizer.pad(
       lowercase__					,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

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

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

   return batch

