

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any]       ,     SCREAMING_SNAKE_CASE__     :      List[str]=0 )				-> Optional[int]:





    '''simple docstring'''






    if name is None:
        A__        =  None
    else:
        A__        =  '''.''' * max(0       ,     spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
        A__        =  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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      List[str] )				-> Dict:





    '''simple docstring'''






    A__        =  param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A__        =  (num_heads, num_splits, hidden_size) + input_shape[1:]
    A__        =  param.view(*UpperCamelCase__ )
    A__        =  param.transpose(0       ,     1 ).contiguous()
    A__        =  param.view(*UpperCamelCase__ )
    return param
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Any       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Optional[int]:





    '''simple docstring'''






    A__        =  config.n_positions
    A__        =  config.n_layer
    A__        =  config.vocab_size
    A__        =  config.n_head
    A__        =  config.n_embd // config.n_head

    A__        =  sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
    A__        =  {
        '''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'''],
    }

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

        A__        =  sd_megatron[f'{pf}{i}.input_layernorm.weight']
        A__        =  sd_megatron[f'{pf}{i}.input_layernorm.bias']

        A__        =  sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
        A__        =  fix_query_key_value_ordering(UpperCamelCase__       ,     3       ,     UpperCamelCase__       ,     UpperCamelCase__ )
        A__        =  vala.transpose(0       ,     1 ).contiguous()

        A__        =  sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
        A__        =  fix_query_key_value_ordering(UpperCamelCase__       ,     3       ,     UpperCamelCase__       ,     UpperCamelCase__ )
        A__        =  vala

        A__        =  sd_megatron[f'{pf}{i}.self_attention.dense.weight'].transpose(
            0       ,     1 )
        A__        =  sd_megatron[f'{pf}{i}.self_attention.dense.bias']
        A__        =  sd_megatron[f'{pf}{i}.post_attention_layernorm.weight']
        A__        =  sd_megatron[f'{pf}{i}.post_attention_layernorm.bias']
        A__        =  sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0       ,     1 )
        A__        =  sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.bias']
        A__        =  sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0       ,     1 )
        A__        =  sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.bias']

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

    return sd_hf
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Union[str, Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  64000
    A__        =  config_megatron['''encoder_seq_length''']
    A__        =  config_megatron['''hidden_size''']
    A__        =  config_megatron['''num_layers''']
    A__        =  config_megatron['''num_attention_heads''']
    A__        =  config_megatron['''ffn_hidden_size''']
    A__        =  '''gelu'''
    A__        =  0.1
    A__        =  0.1
    A__        =  0.1
    A__        =  config_megatron['''layernorm_epsilon''']  # 1e-5
    A__        =  config_megatron['''init_method_std''']  # 0.02
    A__        =  config_megatron['''apply_query_key_layer_scaling''']  # True
    A__        =  True
    A__        =  True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4096:
        A__        =  1  # <|endoftext|>
        A__        =  1  # <|endoftext|>
        A__        =  0  # <unk>
    else:
        A__        =  2  # <s>
        A__        =  3  # <|endoftext|>
        A__        =  0  # <pad>

    return config_hf
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any] )				-> Tuple:





    '''simple docstring'''






    print(UpperCamelCase__ )

    A__        =  args.checkpoint_path
    A__        =  args.save_path
    if isfile(UpperCamelCase__ ):
        raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}' )

    # Load the model.
    A__        =  torch.load(UpperCamelCase__       ,     map_location='cpu' )

    # Load the config.
    A__        =  checkpoint['''hyper_parameters''']['''cfg''']
    A__        =  GPTaConfig()
    A__        =  copy_config(config_hf=UpperCamelCase__       ,     config_megatron=UpperCamelCase__ )
    A__        =  ['''GPT2LMHeadModel''']

    A__        =  checkpoint['''state_dict''']

    # Convert.
    print('Converting' )
    A__        =  convert_megatron_checkpoint(UpperCamelCase__       ,     UpperCamelCase__ )

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

    A__        =  '''GPTSw3Tokenizer'''

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

    # Store the state_dict to file.
    A__        =  os.path.join(UpperCamelCase__       ,     'pytorch_model.bin' )
    print(f'Saving checkpoint to "{output_checkpoint_file}"' )
    torch.save(UpperCamelCase__       ,     UpperCamelCase__ )


if __name__ == "__main__":
     lowercase_									=    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")
     lowercase_									=    parser.parse_args()
     main(_args)








'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_     			=						{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
}

snake_case_     			=						{
    """roberta-base""": 5_12,
    """roberta-large""": 5_12,
    """roberta-large-mnli""": 5_12,
    """distilroberta-base""": 5_12,
    """roberta-base-openai-detector""": 5_12,
    """roberta-large-openai-detector""": 5_12,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							Tuple				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							List[Any]				=    bs[:]
    A							:							Tuple				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Optional[int]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			int		)		->	str:
    A							:							int				=    set()
    A							:							Any				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							Optional[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							Any				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							List[Any]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       A							:							Optional[int]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else mask_token

       super().__init__(
           errors=_UpperCAmelCase     , bos_token=_UpperCAmelCase     , eos_token=_UpperCAmelCase     , unk_token=_UpperCAmelCase     , sep_token=_UpperCAmelCase     , cls_token=_UpperCAmelCase     , pad_token=_UpperCAmelCase     , mask_token=_UpperCAmelCase     , add_prefix_space=_UpperCAmelCase     , **_UpperCAmelCase     , )

       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as vocab_handle:
           A							:							int				=    json.load(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    {v: k for k, v in self.encoder.items()}
       A							:							Tuple				=    errors  # how to handle errors in decoding
       A							:							List[Any]				=    bytes_to_unicode()
       A							:							List[Any]				=    {v: k for k, v in self.byte_encoder.items()}
       with open(_UpperCAmelCase     , encoding='''utf-8'''       ) as merges_handle:
           A							:							Optional[int]				=    merges_handle.read().split('''\n'''       )[1:-1]
       A							:							int				=    [tuple(merge.split()       ) for merge in bpe_merges]
       A							:							Union[str, Any]				=    dict(zip(_UpperCAmelCase     , range(len(_UpperCAmelCase       )       )       )       )
       A							:							List[str]				=    {}
       A							:							List[Any]				=    add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       A							:							Any				=    re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )


   @property
   def        snake_case					(			self       ):
       return len(self.encoder       )


   def        snake_case					(			self       ):
       return dict(self.encoder     , **self.added_tokens_encoder       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Union[str, Any]				=    tuple(_UpperCAmelCase       )
       A							:							Tuple				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

       while True:
           A							:							Any				=    min(_UpperCAmelCase     , key=lambda _UpperCAmelCase       : self.bpe_ranks.get(_UpperCAmelCase     , float('''inf'''       )       )       )
           if bigram not in self.bpe_ranks:
               break
           A,						A							:							Optional[int]				=    bigram
           A							:							str				=    []
           A							:							List[Any]				=    0
           while i < len(_UpperCAmelCase       ):
               try:
                   A							:							Any				=    word.index(_UpperCAmelCase     , _UpperCAmelCase       )
               except ValueError:
                   new_word.extend(word[i:]       )
                   break
               else:
                   new_word.extend(word[i:j]       )
                   A							:							Tuple				=    j

               if word[i] == first and i < len(_UpperCAmelCase       ) - 1 and word[i + 1] == second:
                   new_word.append(first + second       )
                   i += 2
               else:
                   new_word.append(word[i]       )
                   i += 1
           A							:							Optional[Any]				=    tuple(_UpperCAmelCase       )
           A							:							Dict				=    new_word
           if len(_UpperCAmelCase       ) == 1:
               break
           else:
               A							:							int				=    get_pairs(_UpperCAmelCase       )
       A							:							Union[str, Any]				=    ''' '''.join(_UpperCAmelCase       )
       A							:							Tuple				=    word
       return word


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Tuple				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Tuple				=    ''''''.join(
               self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase       ).split(''' '''       )       )
       return bpe_tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Any				=    ''''''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if not os.path.isdir(_UpperCAmelCase       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
       A							:							Any				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as f:
           f.write(json.dumps(self.encoder     , indent=2     , sort_keys=_UpperCAmelCase     , ensure_ascii=_UpperCAmelCase       ) + '''\n'''       )

       A							:							Tuple				=    0
       with open(_UpperCAmelCase     , '''w'''     , encoding='''utf-8'''       ) as writer:
           writer.write('''#version: 0.2\n'''       )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     , key=lambda _UpperCAmelCase       : kv[1]       ):
               if index != token_index:
                   logger.warning(
                       f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                       ''' Please check that the tokenizer is not corrupted!'''       )
                   A							:							List[Any]				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Dict				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase     , token_ids_a=_UpperCAmelCase     , already_has_special_tokens=_UpperCAmelCase       )

       if token_ids_a is None:
           return [1] + ([0] * len(_UpperCAmelCase       )) + [1]
       return [1] + ([0] * len(_UpperCAmelCase       )) + [1, 1] + ([0] * len(_UpperCAmelCase       )) + [1]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Dict				=    [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]

       if token_ids_a is None:
           return len(cls + token_ids_a + sep       ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							str				=    kwargs.pop('''add_prefix_space'''     , self.add_prefix_space       )
       if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase       ) > 0 and not text[0].isspace()):
           A							:							List[Any]				=    ''' ''' + text
       return (text, kwargs)
