



"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		BertModel		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _UpperCAmelCase     :      List[str]														=       (
         ("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(SCREAMING_SNAKE_CASE__							):
          os.makedirs(SCREAMING_SNAKE_CASE__							)

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

     def to_tf_var_name(SCREAMING_SNAKE_CASE__      :		str							):
          for patt, repl in iter(SCREAMING_SNAKE_CASE__							):
               _UpperCAmelCase     :      int														=       name.replace(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          return f'bert/{name}'

     def create_tf_var(SCREAMING_SNAKE_CASE__      :		np.ndarray		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		tf.Session							):
          _UpperCAmelCase     :      Any														=       tf.dtypes.as_dtype(tensor.dtype							)
          _UpperCAmelCase     :      Optional[int]														=       tf.get_variable(dtype=SCREAMING_SNAKE_CASE__		,    shape=tensor.shape		,    name=SCREAMING_SNAKE_CASE__		,    initializer=tf.zeros_initializer()							)
          session.run(tf.variables_initializer([tf_var]							)							)
          session.run(SCREAMING_SNAKE_CASE__							)
          return tf_var

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

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]=None							)    ->  int:





     '''simple docstring'''

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

     _UpperCAmelCase     :      List[str]														=       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=SCREAMING_SNAKE_CASE__		,    ckpt_dir=args.tf_cache_dir		,    model_name=args.model_name							)


if __name__ == "__main__":
 main()





"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


if __name__ == "__main__":
 _lowerCAmelCase							:   Any				=				input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase							:   int				=				[int(item) for item in user_input.split(",")]
 print(patience_sort(unsorted))
