'''simple docstring'''
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[str] ,   __UpperCAmelCase  :					Tuple ,   __UpperCAmelCase  :					List[Any]=0			)							->	Tuple:



  if name is None:
    lowercase				:							Optional[Any]									=   None
  else:
    lowercase				:							Any									=   '.' * max(0 ,   spaces - 2			) + '# {:' + str(50 - spaces			) + 's}'
    lowercase				:							Optional[int]									=   fmt.format(_a			)

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







def 				__UpperCamelCase (					__UpperCAmelCase  :					Tuple ,   __UpperCAmelCase  :					Tuple ,   __UpperCAmelCase  :					Any ,   __UpperCAmelCase  :					int			)							->	Any:
  lowercase				:							Optional[int]									=   param.size()
  # other versions store [num_heads * num_splits * hidden_size, :]
  lowercase				:							Tuple									=   (num_heads, num_splits, hidden_size) + input_shape[1:]
  lowercase				:							int									=   param.view(*_a			)
  lowercase				:							Optional[Any]									=   param.transpose(0 ,   1			).contiguous()
  lowercase				:							Union[str, Any]									=   param.view(*_a			)
  return param







def 				__UpperCamelCase (					__UpperCAmelCase  :					str ,   __UpperCAmelCase  :					Dict			)							->	Tuple:
  lowercase				:							List[Any]									=   config.n_positions
  lowercase				:							List[Any]									=   config.n_layer
  lowercase				:							List[Any]									=   config.vocab_size
  lowercase				:							str									=   config.n_head
  lowercase				:							List[Any]									=   config.n_embd // config.n_head

  lowercase				:							Union[str, Any]									=   sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
  lowercase				:							List[Any]									=   {
      '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'],
  }

  lowercase				:							str									=   'model.language_model.encoder.layers.'
  for i in range(_a			):
    lowercase				:							str									=   torch.tril(torch.ones((n_positions, n_positions) ,   dtype=torch.bool			)			)
    lowercase				:							List[Any]									=   causal_mask.view(1 ,   1 ,   _a ,   _a			)
    lowercase				:							Optional[Any]									=   causal_mask
    lowercase				:							Union[str, Any]									=   torch.tensor(-1e4 ,   dtype=torch.bfloataa			)

    lowercase				:							Union[str, Any]									=   sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
    lowercase				:							List[Any]									=   sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

    lowercase				:							Any									=   sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
    lowercase				:							Optional[Any]									=   fix_query_key_value_ordering(_a ,   3 ,   _a ,   _a			)
    lowercase				:							List[Any]									=   vala.transpose(0 ,   1			).contiguous()

    lowercase				:							str									=   sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
    lowercase				:							Optional[int]									=   fix_query_key_value_ordering(_a ,   3 ,   _a ,   _a			)
    lowercase				:							Union[str, Any]									=   vala

    lowercase				:							str									=   sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
        0 ,   1			)
    lowercase				:							Optional[Any]									=   sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
    lowercase				:							Optional[int]									=   sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
    lowercase				:							str									=   sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
    lowercase				:							int									=   sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0 ,   1			)
    lowercase				:							Optional[int]									=   sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
    lowercase				:							Any									=   sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
        0 ,   1			)
    lowercase				:							Optional[int]									=   sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

  # For LM head, transformers' wants the matrix to weight embeddings.
  lowercase				:							List[str]									=   word_embeddings

  return sd_hf







def 				__UpperCamelCase (					__UpperCAmelCase  :					Optional[Any] ,   __UpperCAmelCase  :					str			)							->	int:
  lowercase				:							Optional[Any]									=   64_000
  lowercase				:							List[str]									=   config_megatron['encoder_seq_length']
  lowercase				:							Any									=   config_megatron['hidden_size']
  lowercase				:							Optional[int]									=   config_megatron['num_layers']
  lowercase				:							str									=   config_megatron['num_attention_heads']
  lowercase				:							Optional[Any]									=   config_megatron['ffn_hidden_size']
  lowercase				:							List[str]									=   'gelu'
  lowercase				:							List[str]									=   0.1
  lowercase				:							str									=   0.1
  lowercase				:							Optional[Any]									=   0.1
  lowercase				:							Any									=   config_megatron['layernorm_epsilon']  # 1e-5
  lowercase				:							Any									=   config_megatron['init_method_std']  # 0.02
  lowercase				:							Tuple									=   config_megatron['apply_query_key_layer_scaling']  # True
  lowercase				:							str									=   True
  lowercase				:							Optional[Any]									=   True

  # This identifies the 6.7B (7B) model which uses a different tokenizer
  if config_megatron["hidden_size"] == 4_096:
    lowercase				:							Optional[int]									=   1  # <|endoftext|>
    lowercase				:							Any									=   1  # <|endoftext|>
    lowercase				:							str									=   0  # <unk>
  else:
    lowercase				:							Union[str, Any]									=   2  # <s>
    lowercase				:							Tuple									=   3  # <|endoftext|>
    lowercase				:							Dict									=   0  # <pad>

  return config_hf







def 				__UpperCamelCase (					__UpperCAmelCase  :					Tuple			)							->	Any:
  print(_a			)

  lowercase				:							str									=   args.checkpoint_path
  lowercase				:							str									=   args.save_path
  if isfile(_a			):
    raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""			)

  # Load the model.
  lowercase				:							Any									=   torch.load(_a ,   map_location='''cpu'''			)

  # Load the config.
  lowercase				:							Optional[Any]									=   checkpoint['hyper_parameters']['cfg']
  lowercase				:							List[Any]									=   GPTaConfig()
  lowercase				:							Union[str, Any]									=   copy_config(config_hf=_a ,   config_megatron=_a			)
  lowercase				:							Tuple									=   ['GPT2LMHeadModel']

  lowercase				:							Tuple									=   checkpoint['state_dict']

  # Convert.
  print('''Converting'''			)
  lowercase				:							Tuple									=   convert_megatron_checkpoint(_a ,   _a			)

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

  lowercase				:							Dict									=   'GPTSw3Tokenizer'

  # Store the config to file.
  print('''Saving config'''			)
  config_hf.save_pretrained(_a			)

  # Store the state_dict to file.
  lowercase				:							int									=   os.path.join(_a ,   '''pytorch_model.bin'''			)
  print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""			)
  torch.save(_a ,   _a			)


if __name__ == "__main__":
 _A		:      List[str]						   =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''')
 _A		:      List[Any]						   =parser.parse_args()
 main(_args)
'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						bool = False      ):




    """simple docstring"""



    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(_a      )
    np.random.seed(_a      )
    torch.manual_seed(_a      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(_a      )
    elif is_npu_available():
        torch.npu.manual_seed_all(_a      )
    else:
        torch.cuda.manual_seed_all(_a      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(_a      )







def 			_UpperCamelCase			(  _a						:						Optional[RNGType] = None					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    if rng_type == RNGType.TORCH:
        __UpperCamelCase						:						Union[str, Any]		     =	torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __UpperCamelCase						:						int		     =	torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __UpperCamelCase						:						Dict		     =	torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __UpperCamelCase						:						Optional[Any]		     =	torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __UpperCamelCase						:						str		     =	torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __UpperCamelCase						:						List[Any]		     =	generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __UpperCamelCase						:						List[Any]		     =	AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __UpperCamelCase						:						Union[str, Any]		     =	rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __UpperCamelCase						:						str		     =	rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __UpperCamelCase						:						List[Any]		     =	rng_state.to(state.device      )
        torch.distributed.broadcast(_a					,  0      )
        __UpperCamelCase						:						Optional[Any]		     =	rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(_a					,  0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(_a      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(_a      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(_a      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(_a      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(_a      )







def 			_UpperCamelCase			(  _a						:						List[Union[str, RNGType]]					,  _a						:						Optional[torch.Generator] = None      ):




    """simple docstring"""



    for rng_type in rng_types:
        synchronize_rng_state(RNGType(_a      )					,  generator=_a      )



