


import math

import tensorflow as tf
from packaging import version

def 	lowerCAmelCase__			(      a__:		Union[str, Any]	)  ->		int:


							'''simple docstring'''





							_UpperCAmelCase					=      tf.convert_to_tensor(a__	)
							_UpperCAmelCase					=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0	)   ,  x.dtype	)	))

							return x * cdf

def 	lowerCAmelCase__			(      a__:		Optional[Any]	)  ->		List[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tf.convert_to_tensor(a__	)
							_UpperCAmelCase					=      tf.cast(math.pi   ,  x.dtype	)
							_UpperCAmelCase					=      tf.cast(0.044_715   ,  x.dtype	)
							_UpperCAmelCase					=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi	) * (x + coeff * tf.pow(a__   ,  3	))	))

							return x * cdf

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      tf.convert_to_tensor(a__	)

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

def 	lowerCAmelCase__			(      a__:		str	)  ->		str:


							'''simple docstring'''





							_UpperCAmelCase					=      tf.convert_to_tensor(a__	)
							_UpperCAmelCase					=      tf.cast(0.044_715   ,  x.dtype	)
							_UpperCAmelCase					=      tf.cast(0.7_978_845_608   ,  x.dtype	)

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

def 	lowerCAmelCase__			(      a__:		List[str]	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      tf.convert_to_tensor(a__	)
							_UpperCAmelCase					=      tf.cast(1.702   ,  x.dtype	)
							return x * tf.math.sigmoid(coeff * x	)

def 	lowerCAmelCase__			(      a__:		Union[str, Any]	)  ->		int:


							'''simple docstring'''





							return tf.clip_by_value(_gelu(a__	)   ,  -1_0   ,  1_0	)

def 	lowerCAmelCase__			(      a__:		Tuple   ,  a__:		Tuple=-1	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      tf.split(a__   ,  2   ,  axis=a__	)
							return a * tf.math.sigmoid(a__	)


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

		def 	lowerCAmelCase__			(      a__:		Optional[Any]	)  ->		Dict:


									'''simple docstring'''





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

		lowerCAmelCase__  :int             =							tf.keras.activations.gelu







		lowerCAmelCase__  :Optional[int]             =							approximate_gelu_wrap
else:
		lowerCAmelCase__  :Any             =							_gelu
		lowerCAmelCase__  :List[str]             =							_gelu_new


lowerCAmelCase__  :Tuple             =							{
    '''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 	lowerCAmelCase__			(      a__:		Optional[Any]	)  ->		Any:


							'''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 argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

							_UpperCAmelCase					=      torch.load(a__      ,  map_location=torch.device('cpu'	)	)
							lightning_model.load_state_dict(ckpt['state_dict']	)

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()	)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()	)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(a__	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--longformer_model''',
		    default=None,
		    type=str,
		    required=True,
		    help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
		)
		parser.add_argument(
		    '''--longformer_question_answering_ckpt_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch Lightning Checkpoint.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
