




"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

      UpperCAmelCase_    				=							(
          ("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_    				=							model.state_dict()

      def to_tf_var_name(lowerCAmelCase__   ):
            for patt, repl in iter(SCREAMING_SNAKE_CASE_   ):
                  UpperCAmelCase_    				=							name.replace(SCREAMING_SNAKE_CASE_			,    SCREAMING_SNAKE_CASE_   )
            return f"""bert/{name}"""

      def create_tf_var(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
            UpperCAmelCase_    				=							tf.dtypes.as_dtype(tensor.dtype   )
            UpperCAmelCase_    				=							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_    				=							to_tf_var_name(SCREAMING_SNAKE_CASE_   )
                  UpperCAmelCase_    				=							state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose   ):
                        UpperCAmelCase_    				=							torch_tensor.T
                  UpperCAmelCase_    				=							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_    				=							session.run(SCREAMING_SNAKE_CASE_   )
                  print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_			,    SCREAMING_SNAKE_CASE_   )}"""   )

            UpperCAmelCase_    				=							tf.train.Saver(tf.trainable_variables()   )
            saver.save(SCREAMING_SNAKE_CASE_			,    os.path.join(SCREAMING_SNAKE_CASE_			,    model_name.replace("-"			,    "_"   ) + ".ckpt"   )   )
def      a__			(   lowerCAmelCase__=None   ):
      UpperCAmelCase_    				=							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_    				=							parser.parse_args(SCREAMING_SNAKE_CASE_   )

      UpperCAmelCase_    				=							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
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_				,					UpperCAmelCase_    				=							input_list[low:mid], input_list[mid : high + 1]
      while left and right:
            result.append((left if left[0] <= right[0] else right).pop(0   )   )
      UpperCAmelCase_    				=							result + left + right
      return input_list
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) <= 1:
            return input_list
      UpperCAmelCase_    				=							list(lowerCAmelCase__   )

      # iteration for two-way merging
      UpperCAmelCase_    				=							2
      while p <= len(lowerCAmelCase__   ):
            # getting low, high and middle value for merge-sort of single list
            for i in range(0			,    len(lowerCAmelCase__   )			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							i + p - 1
                  UpperCAmelCase_    				=							(low + high + 1) // 2
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
            # final merge of last two parts
            if p * 2 >= len(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    0			,    lowerCAmelCase__			,    len(lowerCAmelCase__   ) - 1   )
                  break
            p *= 2

      return input_list


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




