




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      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	int:
  if isinstance(UpperCamelCase	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[Any]		       =     np.full((len(UpperCamelCase      ), sequence_length, 2)	,      UpperCamelCase      )
  else:
    lowerCAmelCase__				:  Optional[Any]		       =     np.full((len(UpperCamelCase      ), sequence_length)	,      UpperCamelCase      )

  for i, tensor in enumerate(UpperCamelCase      ):
    if padding_side == "right":
      if isinstance(UpperCamelCase	,      UpperCamelCase      ):
        lowerCAmelCase__				:  List[str]		       =     tensor[:sequence_length]
      else:
        lowerCAmelCase__				:  Tuple		       =     tensor[:sequence_length]
    else:
      if isinstance(UpperCamelCase	,      UpperCamelCase      ):
        lowerCAmelCase__				:  Optional[Any]		       =     tensor[:sequence_length]
      else:
        lowerCAmelCase__				:  Any		       =     tensor[:sequence_length]

  return out_tensor.tolist()



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  Dict		       =     ord(UpperCamelCase      )
  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
  lowerCAmelCase__				:  Union[str, Any]		       =     unicodedata.category(UpperCamelCase      )
  if cat.startswith('''P'''      ):
    return True
  return False






@dataclass
class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		42
      A__		     =		True
      A__		     =		None
      A__		     =		None
      A__		     =		-100
      A__		     =		"pt"

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        import torch

        lowerCAmelCase__				:  str		       =     '''label''' if '''label''' in features[0].keys() else '''labels'''
        lowerCAmelCase__				:  int		       =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
        lowerCAmelCase__				:  Any		       =     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

        lowerCAmelCase__				:  Union[str, Any]		       =     torch.tensor(batch['''entity_ids''']		).shape[1]
        lowerCAmelCase__				:  int		       =     self.tokenizer.padding_side
        if padding_side == "right":
          lowerCAmelCase__				:  Optional[int]		       =     [
              list(__UpperCAmelCase		) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase		)) for label in labels
          ]
        else:
          lowerCAmelCase__				:  int		       =     [
              [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase		)) + list(__UpperCAmelCase		) for label in labels
          ]

        lowerCAmelCase__				:  Tuple		       =     [feature['''ner_tags'''] for feature in features]
        lowerCAmelCase__				:  Any		       =     padding_tensor(__UpperCAmelCase      ,				-1      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     [feature['''original_entity_spans'''] for feature in features]
        lowerCAmelCase__				:  Union[str, Any]		       =     padding_tensor(__UpperCAmelCase      ,				(-1, -1)      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     {k: torch.tensor(__UpperCAmelCase      ,				dtype=torch.intaa		) for k, v in batch.items()}

        return batch







import math



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * power_factor



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * math.sqrt(1 - power_factor**2      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

