
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase=0							):
    # Format the message.
    if name is None:
        snake_case__  =			None
    else:
        snake_case__  =			"." * max(0      ,						spaces - 2							) + "# {:" + str(50 - spaces							) + "s}"
        snake_case__  =			fmt.format(__lowerCAmelCase							)

    # Print and recurse (if needed).
    if isinstance(__lowerCAmelCase      ,						__lowerCAmelCase							):
        if msg is not None:
            print(__lowerCAmelCase							)
        for k in val.keys():
            recursive_print(__lowerCAmelCase      ,						val[k]      ,						spaces + 2							)
    elif isinstance(__lowerCAmelCase      ,						torch.Tensor							):
        print(__lowerCAmelCase      ,						":"      ,						val.size()							)
    else:
        print(__lowerCAmelCase      ,						":"      ,						__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    snake_case__  =			param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    snake_case__  =			(num_heads, num_splits, hidden_size) + input_shape[1:]
    snake_case__  =			param.view(*__lowerCAmelCase							)
    snake_case__  =			param.transpose(0      ,						1							).contiguous()
    snake_case__  =			param.view(*__lowerCAmelCase							)
    return param





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			config.n_positions
    snake_case__  =			config.n_layer
    snake_case__  =			config.vocab_size
    snake_case__  =			config.n_head
    snake_case__  =			config.n_embd // config.n_head

    snake_case__  =			sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
    snake_case__  =			{
        "transformer.wte.weight": word_embeddings,
        "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
        "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
        "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
    }

    snake_case__  =			"model.language_model.encoder.layers."
    for i in range(__lowerCAmelCase							):
        snake_case__  =			torch.tril(torch.ones((n_positions, n_positions)      ,						dtype=torch.bool							)							)
        snake_case__  =			causal_mask.view(1      ,						1      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)
        snake_case__  =			causal_mask
        snake_case__  =			torch.tensor(-1E4      ,						dtype=torch.bfloataa							)

        snake_case__  =			sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
        snake_case__  =			sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

        snake_case__  =			sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
        snake_case__  =			fix_query_key_value_ordering(__lowerCAmelCase      ,						3      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)
        snake_case__  =			vala.transpose(0      ,						1							).contiguous()

        snake_case__  =			sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
        snake_case__  =			fix_query_key_value_ordering(__lowerCAmelCase      ,						3      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)
        snake_case__  =			vala

        snake_case__  =			sd_megatron[F"""{pf}{i}.self_attention.dense.weight"""].transpose(
            0      ,						1							)
        snake_case__  =			sd_megatron[F"""{pf}{i}.self_attention.dense.bias"""]
        snake_case__  =			sd_megatron[F"""{pf}{i}.post_attention_layernorm.weight"""]
        snake_case__  =			sd_megatron[F"""{pf}{i}.post_attention_layernorm.bias"""]
        snake_case__  =			sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0      ,						1							)
        snake_case__  =			sd_megatron[F"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
        snake_case__  =			sd_megatron[F"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
            0      ,						1							)
        snake_case__  =			sd_megatron[F"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

    # For LM head, transformers' wants the matrix to weight embeddings.
    snake_case__  =			word_embeddings

    return sd_hf





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			64_000
    snake_case__  =			config_megatron["encoder_seq_length"]
    snake_case__  =			config_megatron["hidden_size"]
    snake_case__  =			config_megatron["num_layers"]
    snake_case__  =			config_megatron["num_attention_heads"]
    snake_case__  =			config_megatron["ffn_hidden_size"]
    snake_case__  =			"gelu"
    snake_case__  =			0.1
    snake_case__  =			0.1
    snake_case__  =			0.1
    snake_case__  =			config_megatron["layernorm_epsilon"]  # 1e-5
    snake_case__  =			config_megatron["init_method_std"]  # 0.02
    snake_case__  =			config_megatron["apply_query_key_layer_scaling"]  # True
    snake_case__  =			True
    snake_case__  =			True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_096:
        snake_case__  =			1  # <|endoftext|>
        snake_case__  =			1  # <|endoftext|>
        snake_case__  =			0  # <unk>
    else:
        snake_case__  =			2  # <s>
        snake_case__  =			3  # <|endoftext|>
        snake_case__  =			0  # <pad>

    return config_hf





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    print(__lowerCAmelCase							)

    snake_case__  =			args.checkpoint_path
    snake_case__  =			args.save_path
    if isfile(__lowerCAmelCase							):
        raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}"""							)

    # Load the model.
    snake_case__  =			torch.load(__lowerCAmelCase      ,						map_location="cpu"							)

    # Load the config.
    snake_case__  =			checkpoint["hyper_parameters"]["cfg"]
    snake_case__  =			GPTaConfig()
    snake_case__  =			copy_config(config_hf=__lowerCAmelCase      ,						config_megatron=__lowerCAmelCase							)
    snake_case__  =			["GPT2LMHeadModel"]

    snake_case__  =			checkpoint["state_dict"]

    # Convert.
    print("Converting"							)
    snake_case__  =			convert_megatron_checkpoint(__lowerCAmelCase      ,						__lowerCAmelCase							)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(__lowerCAmelCase      ,						__lowerCAmelCase							)

    snake_case__  =			"GPTSw3Tokenizer"

    # Store the config to file.
    print("Saving config"							)
    config_hf.save_pretrained(__lowerCAmelCase							)

    # Store the state_dict to file.
    snake_case__  =			os.path.join(__lowerCAmelCase      ,						"pytorch_model.bin"							)
    print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""							)
    torch.save(__lowerCAmelCase      ,						__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__           =					argparse.ArgumentParser()
     parser.add_argument(
         '''--checkpoint_path''',
         type=str,
         required=True,
         help='''e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000''',
     )
     parser.add_argument('''--save_path''', type=str, required=True, help='''e.g. /home/user/gpt-sw3/hf''')
     parser.add_argument('''--print-checkpoint-structure''', action='''store_true''')
     __magic_name__           =					parser.parse_args()
     main(_args)





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      @abstractmethod
      def __lt__(     self						,		lowerCamelCase	):
          pass
      @abstractmethod
      def __gt__(     self						,		lowerCamelCase	):
          pass



      @abstractmethod
      def __eq__(     self						,		lowerCamelCase	):
          pass





__magic_name__           =					TypeVar('''T''', bound=Comparable)




class    _SCREAMING_SNAKE_CASE					( Generic[T]	):
      def __init__(     self	):
          snake_case__  =			[]
          snake_case__  =			0
      def __repr__(     self	):
          return str(self.h	)
      def A_     (     self						,		lowerCamelCase	):
          if child_idx > 0:
              return (child_idx - 1) // 2
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 1
          if left_child_index < self.heap_size:
              return left_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 2
          if right_child_index < self.heap_size:
              return right_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          if index < self.heap_size:
              snake_case__  =			index
              snake_case__  =			self.left_child_idx(lowerCamelCase	)
              snake_case__  =			self.right_child_idx(lowerCamelCase	)
              # check which child is larger than its parent
              if left_child is not None and self.h[left_child] > self.h[violation]:
                  snake_case__  =			left_child
              if right_child is not None and self.h[right_child] > self.h[violation]:
                  snake_case__  =			right_child
              # if violation indeed exists
              if violation != index:
                  # swap to fix the violation
                  snake_case__  ,   snake_case__  =			self.h[index], self.h[violation]
                  # fix the subsequent violation recursively if any
                  self.max_heapify(lowerCamelCase	)
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			list(lowerCamelCase	)
          snake_case__  =			len(self.h	)
          if self.heap_size > 1:
              # max_heapify from right to left but exclude leaves (last level)
              for i in range(self.heap_size // 2 - 1						,		-1						,		-1	):
                  self.max_heapify(lowerCamelCase	)
      def A_     (     self	):
          if self.heap_size >= 2:
              snake_case__  =			self.h[0]
              snake_case__  =			self.h.pop(-1	)
              self.heap_size -= 1
              self.max_heapify(0	)
              return me
          elif self.heap_size == 1:
              self.heap_size -= 1
              return self.h.pop(-1	)
          else:
              raise Exception("Empty heap"	)
      def A_     (     self						,		lowerCamelCase	):
          self.h.append(lowerCamelCase	)
          snake_case__  =			(self.heap_size - 1) // 2
          self.heap_size += 1
          while idx >= 0:
              self.max_heapify(lowerCamelCase	)
              snake_case__  =			(idx - 1) // 2



      def A_     (     self	):
          snake_case__  =			self.heap_size
          for j in range(size - 1						,		0						,		-1	):
              snake_case__  ,   snake_case__  =			self.h[j], self.h[0]
              self.heap_size -= 1
              self.max_heapify(0	)
          snake_case__  =			size


if __name__ == "__main__":
     import doctest

     # run doc test
     doctest.testmod()

     # demo
     for unsorted in [
         [0],
         [2],
         [3, 5],
         [5, 3],
         [5, 5],
         [0, 0, 0, 0],
         [1, 1, 1, 1],
         [2, 2, 3, 5],
         [0, 2, 2, 3, 5],
         [2, 5, 3, 0, 2, 3, 0, 3],
         [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
         [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
         [-45, -2, -5],
     ]:
          print(F'''unsorted array: {unsorted}''')

          __magic_name__           =					Heap()
          heap.build_max_heap(unsorted)
          print(F'''after build heap: {heap}''')

          print(F'''max value: {heap.extract_max()}''')
          print(F'''after max value removed: {heap}''')

          heap.insert(100)
          print(F'''after new value 100 inserted: {heap}''')

          heap.heap_sort()
          print(F'''heap-sorted array: {heap}\n''')



