


import math

import tensorflow as tf
from packaging import version

def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0     )    , x.dtype     )     ))

 return x * cdf

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   tf.cast(math.pi    , x.dtype     )
 UpperCamelCase__					:    List[Any]          =   tf.cast(0.04_4715    , x.dtype     )
 UpperCamelCase__					:    Any          =   0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi     ) * (x + coeff * tf.pow(SCREAMING_SNAKE_CASE    , 3     ))     ))

 return x * cdf

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE     )

 return x * tf.tanh(tf.math.softplus(SCREAMING_SNAKE_CASE     )     )

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    int          =   tf.cast(0.04_4715    , x.dtype     )
 UpperCamelCase__					:    Optional[Any]          =   tf.cast(0.79_7884_5608    , x.dtype     )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)     ))

def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Union[str, Any]          =   tf.cast(1.702    , x.dtype     )
 return x * tf.math.sigmoid(coeff * x     )

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







 """simple docstring"""



 return tf.clip_by_value(_gelu(SCREAMING_SNAKE_CASE     )    , -10    , 10     )

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]    , SCREAMING_SNAKE_CASE	:				List[Any]=-1     ):







 """simple docstring"""



 UpperCamelCase__       ,				UpperCamelCase__					:    List[str]          =   tf.split(SCREAMING_SNAKE_CASE    , 2    , axis=SCREAMING_SNAKE_CASE     )
 return a * tf.math.sigmoid(SCREAMING_SNAKE_CASE     )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):

       def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







        """simple docstring"""



        return tf.keras.activations.gelu(SCREAMING_SNAKE_CASE    , approximate=SCREAMING_SNAKE_CASE     )

       __UpperCamelCase       :       Optional[Any]						       =				tf.keras.activations.gelu






       __UpperCamelCase       :       List[str]						       =				approximate_gelu_wrap
else:
       __UpperCamelCase       :       Any						       =				_gelu
       __UpperCamelCase       :       List[str]						       =				_gelu_new


__UpperCamelCase       :       Dict						       =				{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def 			_a  (				SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()     )}"     )






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


