





from .number_theory.prime_numbers import next_prime

class 			__A:

     def __init__(   self,							A,							A = None,							A = None,							):





            """simple docstring"""

            _UpperCamelCase														=    size_table
            _UpperCamelCase														=    [None] * self.size_table
            _UpperCamelCase														=    0.75 if lim_charge is None else lim_charge
            _UpperCamelCase														=    1 if charge_factor is None else charge_factor
            _UpperCamelCase														=    []
            _UpperCamelCase														=    {}

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return self._keys

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return sum(1 for slot in self.values if slot is not None   ) / (
                self.size_table * self.charge_factor
            )

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            return key % self.size_table

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            print(F'''step {step_ord}'''   )
            print(list(range(len(self.values   )   )   )   )
            print(self.values   )

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    1
            _UpperCamelCase														=    values
            for value in values:
                   self.insert_data(A_   )
                   self._step_by_step(A_   )
                   i += 1

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    data
            _UpperCamelCase														=    data

     def 						_UpperCamelCase  (   self,							A,							A=None   ):





            """simple docstring"""

            _UpperCamelCase														=    self.hash_function(key + 1   )

            while self.values[new_key] is not None and self.values[new_key] != key:
                   if self.values.count(A_   ) > 0:
                          _UpperCamelCase														=    self.hash_function(new_key + 1   )
                   else:
                          _UpperCamelCase														=    None
                          break

            return new_key

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    [value for value in self.values if value is not None]
            _UpperCamelCase														=    next_prime(self.size_table,							factor=2   )
            self._keys.clear()
            _UpperCamelCase														=    [None] * self.size_table  # hell's pointers D: don't DRY ;/
            for value in survivor_values:
                   self.insert_data(A_   )



     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.hash_function(A_   )

            if self.values[key] is None:
                   self._set_value(A_,							A_   )

            elif self.values[key] == data:
                   pass

            else:
                   _UpperCamelCase														=    self._collision_resolution(A_,							A_   )
                   if collision_resolution is not None:
                          self._set_value(A_,							A_   )
                   else:
                          self.rehashing()
                          self.insert_data(A_   )










import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





