
import math
from datetime import datetime, timedelta

def 		UpperCamelCase	(      lowercase_   )		->	datetime:


  '''simple docstring'''






  lowercase__  :   Dict    		=      year % 19
  lowercase__  :   List[str]    		=      year % 4
  lowercase__  :   Any    		=      year % 7
  lowercase__  :   List[Any]    		=      math.floor(year / 1_00   )
  lowercase__  :   int    		=      math.floor((13 + 8 * leap_day_inhibits) / 25   )
  lowercase__  :   str    		=      leap_day_inhibits / 4
  lowercase__  :   Any    		=      (
      15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
  ) % 30
  lowercase__  :   Any    		=      (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

  # days to be added to March 21
  lowercase__  :   Union[str, Any]    		=      (19 * metonic_cycle + secular_moon_shift) % 30

  # PHM -> Paschal Full Moon
  lowercase__  :   List[Any]    		=      (
      2 * julian_leap_year
      + 4 * non_leap_year
      + 6 * days_to_add
      + century_starting_point
  ) % 7

  if days_to_add == 29 and days_from_phm_to_sunday == 6:
    return datetime(__SCREAMING_SNAKE_CASE , 4 , 19   )
  elif days_to_add == 28 and days_from_phm_to_sunday == 6:
    return datetime(__SCREAMING_SNAKE_CASE , 4 , 18   )
  else:
    return datetime(__SCREAMING_SNAKE_CASE , 3 , 22   ) + timedelta(
        days=int(days_to_add + days_from_phm_to_sunday   )   )


if __name__ == "__main__":
     for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
          lowerCamelCase__    :		int          =   """will be""" if year > datetime.now().year else """was"""
          print(f'''Easter in {year} {tense} {gauss_easter(year)}''')








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()






