


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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if isinstance(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):
    lowerCamelCase :					Optional[int]    =				np.full((len(SCREAMING_SNAKE_CASE_	), sequence_length, 2)      ,						SCREAMING_SNAKE_CASE_	)
  else:
    lowerCamelCase :					str    =				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_	):
        lowerCamelCase :					Union[str, Any]    =				tensor[:sequence_length]
      else:
        lowerCamelCase :					Any    =				tensor[:sequence_length]
    else:
      if isinstance(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):
        lowerCamelCase :					Union[str, Any]    =				tensor[:sequence_length]
      else:
        lowerCamelCase :					Optional[Any]    =				tensor[:sequence_length]

  return out_tensor.tolist()






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				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
  lowerCamelCase :					str    =				unicodedata.category(SCREAMING_SNAKE_CASE_	)
  if cat.startswith("P"	):
    return True
  return False







@dataclass
class 	UpperCAmelCase_					(					_UpperCAmelCase    ):

   '''simple docstring'''




   __A :      PreTrainedTokenizerBase
   __A :      Union[bool, str, PaddingStrategy]     		=		True
   __A :      Optional[int]     		=		None
   __A :      Optional[int]     		=		None
   __A :      int     		=		-100
   __A :      str     		=		"pt"






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     import torch

     lowerCamelCase :					Tuple    =				"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 :					List[str]    =				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

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

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

     return batch




import requests





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''Content-Type''': '''application/json'''}
				__lowercase 			=							requests.post(lowercase							,			json={'''text''': message_body}							,			headers=lowercase							)
				if response.status_code != 200:
								__lowercase 			=							(
								    '''Request to slack returned an error '''
								    F"{response.status_code}, the response is:\n{response.text}"
								)
								raise ValueError(lowercase							)


if __name__ == "__main__":
		# Set the slack url to the one provided by Slack when you create the webhook at
		# https://my.slack.com/services/new/incoming-webhook/
		send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")