'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any





class 				lowercase    :
   lowerCamelCase_            =('_front', '_back', '_len')





   @dataclass
   class 				lowercase    :
      lowerCamelCase_            =None
      lowerCamelCase_            =None
      lowerCamelCase_            =None





   class 				lowercase    :
      lowerCamelCase_            =('_cur',)


      def __init__( self					:						Tuple ,		__lowerCAmelCase					:						Optional[Any])   ->       Tuple:
            lowercase_     			=   cur


      def __iter__( self					:						Optional[int])   ->       Union[str, Any]:
            return self


      def __UpperCAmelCase							( self					:						List[Any])   ->       Tuple:
            if self._cur is None:
                  # finished iterating
                  raise StopIteration
            lowercase_     			=   self._cur.val
            lowercase_     			=   self._cur.next_node

            return val




   def __init__( self					:						Tuple ,		__lowerCAmelCase					:						int = None)   ->       Dict:
         lowercase_     			=   None
         lowercase_     			=   None
         lowercase_     			=   0

         if iterable is not None:
               # append every value to the deque
               for val in iterable:
                     self.append(lowercase_)


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						List[Any])   ->       Tuple:
         lowercase_     			=   self._Node(lowercase_ ,		lowercase_ ,		lowercase_)
         if self.is_empty():
               # front = back
               lowercase_     			=   node
               lowercase_     			=   1
         else:
               # connect nodes
               lowercase_     			=   node
               lowercase_     			=   self._back
               lowercase_     			=   node  # assign new back to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."


   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						Tuple)   ->       Tuple:
         lowercase_     			=   self._Node(lowercase_ ,		lowercase_ ,		lowercase_)
         if self.is_empty():
               # front = back
               lowercase_     			=   node
               lowercase_     			=   1
         else:
               # connect nodes
               lowercase_     			=   self._front
               lowercase_     			=   node
               lowercase_     			=   node  # assign new front to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."


   def __UpperCAmelCase							( self					:						str ,		__lowerCAmelCase					:						Dict)   ->       List[str]:
         for val in iterable:
               self.append(lowercase_)


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						Dict)   ->       Optional[int]:
         for val in iterable:
               self.appendleft(lowercase_)


   def __UpperCAmelCase							( self					:						int)   ->       Optional[int]:
         assert not self.is_empty(), "Deque is empty."

         lowercase_     			=   self._back
         lowercase_     			=   self._back.prev_node  # set new back
         # drop the last node - python will deallocate memory automatically
         lowercase_     			=   None

         self._len -= 1

         return topop.val


   def __UpperCAmelCase							( self					:						Tuple)   ->       Any:
         assert not self.is_empty(), "Deque is empty."

         lowercase_     			=   self._front
         lowercase_     			=   self._front.next_node  # set new front and drop the first node
         lowercase_     			=   None

         self._len -= 1

         return topop.val


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       int:
         return self._front is None


   def __len__( self					:						str)   ->       List[str]:
         return self._len


   def __eq__( self					:						Optional[int] ,		__lowerCAmelCase					:						Tuple)   ->       Any:

         if not isinstance(lowercase_ ,		lowercase_):
               return NotImplemented

         lowercase_     			=   self._front
         lowercase_     			=   other._front

         # if the length of the dequeues are not the same, they are not equal
         if len(self) != len(lowercase_):
               return False

         while me is not None and oth is not None:
               # compare every value
               if me.val != oth.val:
                     return False
               lowercase_     			=   me.next_node
               lowercase_     			=   oth.next_node

         return True


   def __iter__( self					:						Union[str, Any])   ->       Optional[int]:
         return Deque._Iterator(self._front)


   def __repr__( self					:						int)   ->       List[Any]:
         lowercase_     			=   []
         lowercase_     			=   self._front
         while aux is not None:
               # append the values in a list to display
               values_list.append(aux.val)
               lowercase_     			=   aux.next_node

         return F'[{", ".join(repr(lowercase_) for val in values_list)}]'


if __name__ == "__main__":
       import doctest

       doctest.testmod()








'''simple docstring'''
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,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    json.load(open(__SCREAMING_SNAKE_CASE,		"r"							)							)
    snake_case_			:     Optional[int]        =    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}'							)
    snake_case_			:     Optional[Any]        =    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)




