


'''simple docstring'''





import socket

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    List[str]          =   socket.socket(socket.AF_INET    , socket.SOCK_STREAM     )
 UpperCamelCase__					:    List[str]          =   socket.gethostname()
 UpperCamelCase__					:    Dict          =   12312

 sock.connect((host, port)     )
 sock.send(B'''Hello server!'''     )

 with open('''Received_file'''    , '''wb'''     ) as out_file:
  print('''File opened'''     )
  print('''Receiving data...'''     )
  while True:
   UpperCamelCase__					:    int          =   sock.recv(1024     )
   if not data:
    break
   out_file.write(SCREAMING_SNAKE_CASE     )

 print('''Successfully received the file'''     )
 sock.close()
 print('''Connection closed'''     )


if __name__ == "__main__":
       main()






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  (				SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 if isinstance(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Dict          =   np.full((len(SCREAMING_SNAKE_CASE     ), sequence_length, 2)    , SCREAMING_SNAKE_CASE     )
 else:
  UpperCamelCase__					:    Dict          =   np.full((len(SCREAMING_SNAKE_CASE     ), sequence_length)    , SCREAMING_SNAKE_CASE     )

 for i, tensor in enumerate(SCREAMING_SNAKE_CASE     ):
  if padding_side == "right":
   if isinstance(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ):
    UpperCamelCase__					:    str          =   tensor[:sequence_length]
   else:
    UpperCamelCase__					:    Union[str, Any]          =   tensor[:sequence_length]
  else:
   if isinstance(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ):
    UpperCamelCase__					:    str          =   tensor[:sequence_length]
   else:
    UpperCamelCase__					:    str          =   tensor[:sequence_length]

 return out_tensor.tolist()

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    Union[str, Any]          =   ord(SCREAMING_SNAKE_CASE     )
 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__					:    Any          =   unicodedata.category(SCREAMING_SNAKE_CASE     )
 if cat.startswith('''P'''     ):
  return True
 return False






@dataclass
class       __magic_name__	(     __lowerCAmelCase):
     A:		PreTrainedTokenizerBase
     A:		Union[bool, str, PaddingStrategy]			=      True
     A:		Optional[int]			=      None
     A:		Optional[int]			=      None
     A:		int			=      -1_0_0
     A:		str			=      "pt"


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     List[Any]			)  ->      Any:





      '''simple docstring'''






      import torch

      UpperCamelCase__					:    Tuple          =   '''label''' if '''label''' in features[0].keys() else '''labels'''
      UpperCamelCase__					:    Optional[Any]          =   [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      UpperCamelCase__					:    Optional[int]          =   self.tokenizer.pad(
          lowerCamelCase__							,	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__					:    Dict          =   torch.tensor(batch['''entity_ids''']			).shape[1]
      UpperCamelCase__					:    Union[str, Any]          =   self.tokenizer.padding_side
      if padding_side == "right":
       UpperCamelCase__					:    Optional[int]          =   [
           list(lowerCamelCase__			) + [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase__			)) for label in labels
       ]
      else:
       UpperCamelCase__					:    int          =   [
           [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase__			)) + list(lowerCamelCase__			) for label in labels
       ]

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

      return batch


