

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 	__snake_case		(      __UpperCamelCase     :      Dict							,__UpperCamelCase     :      int							,__UpperCamelCase     :      str							,__UpperCamelCase     :      int  ):



      """simple docstring"""

      if isinstance(_A							,_A  ):
            A_		   =							np.full((len(_A  ), sequence_length, 2)							,_A  )
      else:
            A_		   =							np.full((len(_A  ), sequence_length)							,_A  )

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

      return out_tensor.tolist()

def 	__snake_case		(      __UpperCamelCase     :      Any  ):



      """simple docstring"""

      A_		   =							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
      A_		   =							unicodedata.category(_A  )
      if cat.startswith("P"  ):
            return True
      return False



@dataclass
class    _a (       UpperCAmelCase__							):




      """simple docstring"""

      _lowerCamelCase    :       Union[str, Any]							=    4_2
      _lowerCamelCase    :       List[str]							=    True
      _lowerCamelCase    :       List[str]							=    None
      _lowerCamelCase    :       Union[str, Any]							=    None
      _lowerCamelCase    :       Dict							=    -1_0_0
      _lowerCamelCase    :       Tuple							=    'pt'





      def 						__A     (					self				:							Dict		,       UpperCAmelCase				:							Optional[int]     ):
            import torch

            A_		   =							"label" if "label" in features[0].keys() else "labels"
            A_		   =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
            A_		   =							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

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

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

            return batch





from torch import nn


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(f"Unsupported activation function: {act_fn}"							)
