
"""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		(       snake_case   :List[str]       ,      snake_case   :List[Any]       ,      snake_case   :List[str]       ,      snake_case   :Union[str, Any]	)   ->			str:
 if isinstance(snake_case       ,      snake_case	):
  __UpperCamelCase       						= np.full((len(snake_case	), sequence_length, 2)       ,      snake_case	)
 else:
  __UpperCamelCase       						= np.full((len(snake_case	), sequence_length)       ,      snake_case	)

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

 return out_tensor.tolist()






def 							A		(       snake_case   :int	)   ->			Any:
 __UpperCamelCase       						= ord(snake_case	)
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __UpperCamelCase       						= unicodedata.category(snake_case	)
 if cat.startswith('P'	):
  return True
 return False





@dataclass
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		42
     lowercase		     =		True
     lowercase		     =		None
     lowercase		     =		None
     lowercase		     =		-100
     lowercase		     =		"pt"





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      import torch

      __UpperCamelCase       						= 'label' if 'label' in features[0].keys() else 'labels'
      __UpperCamelCase       						= [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      __UpperCamelCase       						= 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       						= torch.tensor(batch['entity_ids']   ).shape[1]
      __UpperCamelCase       						= self.tokenizer.padding_side
      if padding_side == "right":
       __UpperCamelCase       						= [
           list(__UpperCAmelCase   ) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase   )) for label in labels
       ]
      else:
       __UpperCamelCase       						= [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase   )) + list(__UpperCAmelCase   ) for label in labels
       ]

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

      return batch





"""simple docstring"""

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							A		(       )   ->			List[Any]:
 __UpperCamelCase       						= ArgumentParser(
     description=(
         'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
     )	)

 # Optional arguments for the launch helper
 parser.add_argument('--num_cores'       ,      type=snake_case       ,      default=1       ,      help='Number of TPU cores to use (1 or 8).'	)

 # positional
 parser.add_argument(
     'training_script'       ,      type=snake_case       ,      help=(
         'The full path to the single TPU training '
         'program/script to be launched in parallel, '
         'followed by all the arguments for the '
         'training script'
     )       ,      )

 # rest from the training program
 parser.add_argument('training_script_args'       ,      nargs=snake_case	)

 return parser.parse_args()






def 							A		(       )   ->			Optional[int]:
 __UpperCamelCase       						= parse_args()

 # Import training_script as a module.
 __UpperCamelCase       						= Path(args.training_script	)
 sys.path.append(str(script_fpath.parent.resolve()	)	)
 __UpperCamelCase       						= script_fpath.stem
 __UpperCamelCase       						= importlib.import_module(snake_case	)

 # Patch sys.argv
 __UpperCamelCase       						= [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores	)]

 xmp.spawn(mod._mp_fn       ,      args=()       ,      nprocs=args.num_cores	)


if __name__ == "__main__":
      main()



