
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class       _snake_case   (	pl.LightningModule							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__()
   lowercase__  :   str    		=      model
   lowercase__  :   Dict    		=      2
   lowercase__  :   List[Any]    		=      nn.Linear(self.model.config.hidden_size					,  self.num_labels)



 def      lowercase__     (					self):


   '''simple docstring'''




   pass

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Tuple:


  '''simple docstring'''






  lowercase__  :   Union[str, Any]    		=      LongformerModel.from_pretrained(lowercase_   )
  lowercase__  :   str    		=      LightningModel(lowercase_   )

  lowercase__  :   Tuple    		=      torch.load(lowercase_ , map_location=torch.device("""cpu"""   )   )
  lightning_model.load_state_dict(ckpt["""state_dict"""]   )

  # init longformer question answering model
  lowercase__  :   Optional[int]    		=      LongformerForQuestionAnswering.from_pretrained(lowercase_   )

  # 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(lowercase_   )

  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__    :		Any          =   parser.parse_args()
     convert_longformer_qa_checkpoint_to_pytorch(
         args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
     )








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Dict    		=      ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

  lowercase__  :   Union[str, Any]    		=      (
      ("""layer.""", """layer_"""),
      ("""word_embeddings.weight""", """word_embeddings"""),
      ("""position_embeddings.weight""", """position_embeddings"""),
      ("""token_type_embeddings.weight""", """token_type_embeddings"""),
      (""".""", """/"""),
      ("""LayerNorm/weight""", """LayerNorm/gamma"""),
      ("""LayerNorm/bias""", """LayerNorm/beta"""),
      ("""weight""", """kernel"""),
  )

  if not os.path.isdir(lowercase_   ):
    os.makedirs(lowercase_   )

  lowercase__  :   Optional[Any]    		=      model.state_dict()

  def to_tf_var_name(lowercase_   ):
    for patt, repl in iter(lowercase_   ):
      lowercase__  :   str    		=      name.replace(lowercase_ , lowercase_   )
    return F'bert/{name}'

  def create_tf_var(lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   List[str]    		=      tf.dtypes.as_dtype(tensor.dtype   )
    lowercase__  :   Tuple    		=      tf.get_variable(dtype=lowercase_ , shape=tensor.shape , name=lowercase_ , initializer=tf.zeros_initializer()   )
    session.run(tf.variables_initializer([tf_var]   )   )
    session.run(lowercase_   )
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowercase__  :   Any    		=      to_tf_var_name(lowercase_   )
      lowercase__  :   Dict    		=      state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose   ):
        lowercase__  :   Optional[int]    		=      torch_tensor.T
      lowercase__  :   Any    		=      create_tf_var(tensor=lowercase_ , name=lowercase_ , session=lowercase_   )
      tf.keras.backend.set_value(lowercase_ , lowercase_   )
      lowercase__  :   Optional[int]    		=      session.run(lowercase_   )
      print(F'Successfully created {tf_name}: {np.allclose(lowercase_ , lowercase_   )}'   )

    lowercase__  :   List[Any]    		=      tf.train.Saver(tf.trainable_variables()   )
    saver.save(lowercase_ , os.path.join(lowercase_ , model_name.replace("""-""" , """_"""   ) + """.ckpt"""   )   )

def 		UpperCamelCase	(      lowercase_=None   )		->	Union[str, Any]:


  '''simple docstring'''






  lowercase__  :   List[str]    		=      argparse.ArgumentParser()
  parser.add_argument("""--model_name""" , type=lowercase_ , required=lowercase_ , help="""model name e.g. bert-base-uncased"""   )
  parser.add_argument(
      """--cache_dir""" , type=lowercase_ , default=lowercase_ , required=lowercase_ , help="""Directory containing pytorch model"""   )
  parser.add_argument("""--pytorch_model_path""" , type=lowercase_ , required=lowercase_ , help="""/path/to/<pytorch-model-name>.bin"""   )
  parser.add_argument("""--tf_cache_dir""" , type=lowercase_ , required=lowercase_ , help="""Directory in which to save tensorflow model"""   )
  lowercase__  :   List[str]    		=      parser.parse_args(lowercase_   )

  lowercase__  :   Any    		=      BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path   ) , cache_dir=args.cache_dir , )

  convert_pytorch_checkpoint_to_tf(model=lowercase_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name   )


if __name__ == "__main__":
     main()






