

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase=0				)					-> Dict:



    '''simple docstring'''






    if name is None:
        lowerCAmelCase				:			Tuple    			=      None
    else:
        lowerCAmelCase				:			Any    			=      '.' * max(0,      spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
        lowerCAmelCase				:			Union[str, Any]    			=      fmt.format(_UpperCAmelCase				)

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



    '''simple docstring'''






    lowerCAmelCase				:			List[str]    			=      param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    lowerCAmelCase				:			Tuple    			=      (num_heads, num_splits, hidden_size) + input_shape[1:]
    lowerCAmelCase				:			List[str]    			=      param.view(*_UpperCAmelCase				)
    lowerCAmelCase				:			Union[str, Any]    			=      param.transpose(0,      1				).contiguous()
    lowerCAmelCase				:			List[str]    			=      param.view(*_UpperCAmelCase				)
    return param
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> str:



    '''simple docstring'''






    lowerCAmelCase				:			int    			=      config.n_positions
    lowerCAmelCase				:			str    			=      config.n_layer
    lowerCAmelCase				:			Union[str, Any]    			=      config.vocab_size
    lowerCAmelCase				:			List[Any]    			=      config.n_head
    lowerCAmelCase				:			int    			=      config.n_embd // config.n_head

    lowerCAmelCase				:			str    			=      sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    lowerCAmelCase				:			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'],
    }

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

        lowerCAmelCase				:			List[str]    			=      sd_megatron[f"{pf}{i}.input_layernorm.weight"]
        lowerCAmelCase				:			Optional[Any]    			=      sd_megatron[f"{pf}{i}.input_layernorm.bias"]

        lowerCAmelCase				:			Any    			=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.weight"]
        lowerCAmelCase				:			Dict    			=      fix_query_key_value_ordering(_UpperCAmelCase,      3,      _UpperCAmelCase,      _UpperCAmelCase				)
        lowerCAmelCase				:			List[str]    			=      vala.transpose(0,      1				).contiguous()

        lowerCAmelCase				:			Union[str, Any]    			=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.bias"]
        lowerCAmelCase				:			Tuple    			=      fix_query_key_value_ordering(_UpperCAmelCase,      3,      _UpperCAmelCase,      _UpperCAmelCase				)
        lowerCAmelCase				:			Union[str, Any]    			=      vala

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

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

    return sd_hf
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> Optional[int]:



    '''simple docstring'''






    lowerCAmelCase				:			List[Any]    			=      64_000
    lowerCAmelCase				:			List[str]    			=      config_megatron['encoder_seq_length']
    lowerCAmelCase				:			Any    			=      config_megatron['hidden_size']
    lowerCAmelCase				:			Dict    			=      config_megatron['num_layers']
    lowerCAmelCase				:			Union[str, Any]    			=      config_megatron['num_attention_heads']
    lowerCAmelCase				:			Union[str, Any]    			=      config_megatron['ffn_hidden_size']
    lowerCAmelCase				:			Optional[int]    			=      'gelu'
    lowerCAmelCase				:			str    			=      0.1
    lowerCAmelCase				:			Optional[Any]    			=      0.1
    lowerCAmelCase				:			Union[str, Any]    			=      0.1
    lowerCAmelCase				:			Optional[int]    			=      config_megatron['layernorm_epsilon']  # 1e-5
    lowerCAmelCase				:			List[Any]    			=      config_megatron['init_method_std']  # 0.02
    lowerCAmelCase				:			Optional[Any]    			=      config_megatron['apply_query_key_layer_scaling']  # True
    lowerCAmelCase				:			Dict    			=      True
    lowerCAmelCase				:			Union[str, Any]    			=      True

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

    return config_hf
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Optional[int]:



    '''simple docstring'''






    print(_UpperCAmelCase				)

    lowerCAmelCase				:			Tuple    			=      args.checkpoint_path
    lowerCAmelCase				:			Any    			=      args.save_path
    if isfile(_UpperCAmelCase				):
        raise FileNotFoundError(f"ERROR! could not find file {checkpoint_path}"				)

    # Load the model.
    lowerCAmelCase				:			List[Any]    			=      torch.load(_UpperCAmelCase,      map_location='cpu'				)

    # Load the config.
    lowerCAmelCase				:			List[Any]    			=      checkpoint['hyper_parameters']['cfg']
    lowerCAmelCase				:			Optional[int]    			=      GPTaConfig()
    lowerCAmelCase				:			Optional[int]    			=      copy_config(config_hf=_UpperCAmelCase,      config_megatron=_UpperCAmelCase				)
    lowerCAmelCase				:			List[str]    			=      ['GPT2LMHeadModel']

    lowerCAmelCase				:			Tuple    			=      checkpoint['state_dict']

    # Convert.
    print('Converting'				)
    lowerCAmelCase				:			str    			=      convert_megatron_checkpoint(_UpperCAmelCase,      _UpperCAmelCase				)

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

    lowerCAmelCase				:			str    			=      'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
 __A   :     int      =			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   :     Union[str, Any]      =			parser.parse_args()
 main(_args)








from __future__ import annotations
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:



    '''simple docstring'''






    lowerCAmelCase				:			List[str]    			=      {1}
    lowerCAmelCase				:			int    			=      [1]
    for _ in range(2,      depth + 1				):
        lowerCAmelCase				:			str    			=      [*previous_coefficients, 0]
        lowerCAmelCase				:			Optional[int]    			=      [0, *previous_coefficients]
        lowerCAmelCase				:			Tuple    			=      []
        for x, y in zip(_UpperCAmelCase,      _UpperCAmelCase				):
            coefficients.add(x + y				)
            previous_coefficients.append(x + y				)
    return coefficients
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:



    '''simple docstring'''






    lowerCAmelCase				:			str    			=      set()
    for number in unique_coefficients:
        lowerCAmelCase				:			Dict    			=      2
        lowerCAmelCase				:			Tuple    			=      number
        while divisor**2 <= copy_number:
            lowerCAmelCase				:			List[str]    			=      0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(_UpperCAmelCase				)
                break
            divisor += 1

    return unique_coefficients.difference(_UpperCAmelCase				)
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase = 51				)					-> int:



    '''simple docstring'''






    lowerCAmelCase				:			int    			=      get_pascal_triangle_unique_coefficients(_UpperCAmelCase				)
    lowerCAmelCase				:			Optional[int]    			=      get_squarefrees(_UpperCAmelCase				)
    return sum(_UpperCAmelCase				)


if __name__ == "__main__":
 print(F'{solution() = }')





