import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase=0						)       ->       Tuple:

						'''simple docstring'''



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

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

						'''simple docstring'''



						_snake_case						:     Tuple				=    param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						_snake_case						:     List[str]				=    (num_heads, num_splits, hidden_size) + input_shape[1:]
						_snake_case						:     Optional[Any]				=    param.view(*__lowercase						)
						_snake_case						:     Tuple				=    param.transpose(0					,			1						).contiguous()
						_snake_case						:     Dict				=    param.view(*__lowercase						)
						return param
def 					snake_case						(__lowercase					,			__lowercase						)       ->       Dict:

						'''simple docstring'''



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

						_snake_case						:     int				=    sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
						_snake_case						:     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"],
						}

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

												_snake_case						:     str				=    sd_megatron[F"""{pf}{i}.input_layernorm.weight"""]
												_snake_case						:     Optional[int]				=    sd_megatron[F"""{pf}{i}.input_layernorm.bias"""]

												_snake_case						:     Union[str, Any]				=    sd_megatron[F"""{pf}{i}.self_attention.query_key_value.weight"""]
												_snake_case						:     int				=    fix_query_key_value_ordering(__lowercase					,			3					,			__lowercase					,			__lowercase						)
												_snake_case						:     Union[str, Any]				=    vala.transpose(0					,			1						).contiguous()

												_snake_case						:     str				=    sd_megatron[F"""{pf}{i}.self_attention.query_key_value.bias"""]
												_snake_case						:     int				=    fix_query_key_value_ordering(__lowercase					,			3					,			__lowercase					,			__lowercase						)
												_snake_case						:     List[Any]				=    vala

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

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

						return sd_hf
def 					snake_case						(__lowercase					,			__lowercase						)       ->       str:

						'''simple docstring'''



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

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

						return config_hf
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						print(__lowercase						)

						_snake_case						:     Any				=    args.checkpoint_path
						_snake_case						:     List[Any]				=    args.save_path
						if isfile(__lowercase						):
												raise FileNotFoundError(F"""ERROR! could not find file {checkpoint_path}"""						)

						# Load the model.
						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)

						# Load the config.
						_snake_case						:     Optional[Any]				=    checkpoint["hyper_parameters"]["cfg"]
						_snake_case						:     List[str]				=    GPTaConfig()
						_snake_case						:     Dict				=    copy_config(config_hf=__lowercase					,			config_megatron=__lowercase						)
						_snake_case						:     Any				=    ["GPT2LMHeadModel"]

						_snake_case						:     int				=    checkpoint["state_dict"]

						# Convert.
						print("Converting"						)
						_snake_case						:     int				=    convert_megatron_checkpoint(__lowercase					,			__lowercase						)

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

						_snake_case						:     Optional[int]				=    "GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[Any]     = 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')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				main(_args)

"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case    =		logging.get_logger(__name__)

snake_case    =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__=None		,	lowercase__=None		,	lowercase__=False		,	lowercase__=True		,	lowercase__=False		,	lowercase__="<s>"		,	lowercase__="</s>"		,	lowercase__="<unk>"		,	lowercase__="<sep>"		,	lowercase__="<pad>"		,	lowercase__="<cls>"		,	lowercase__="<mask>"		,	lowercase__=["<eop>", "<eod>"]		,	**lowercase__		,	)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__			) if isinstance(lowercase__		,	lowercase__			) else mask_token

									super().__init__(
									    vocab_file=lowercase__		,	tokenizer_file=lowercase__		,	do_lower_case=lowercase__		,	remove_space=lowercase__		,	keep_accents=lowercase__		,	bos_token=lowercase__		,	eos_token=lowercase__		,	unk_token=lowercase__		,	sep_token=lowercase__		,	pad_token=lowercase__		,	cls_token=lowercase__		,	mask_token=lowercase__		,	additional_special_tokens=lowercase__		,	**lowercase__		,	)

									SCREAMING_SNAKE_CASE				=	3
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	remove_space
									SCREAMING_SNAKE_CASE				=	keep_accents
									SCREAMING_SNAKE_CASE				=	vocab_file
									SCREAMING_SNAKE_CASE				=	False if not self.vocab_file else True

									try:
														import jieba
									except ModuleNotFoundError as error:
														raise error.__class__(
														    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
														    'See https://pypi.org/project/jieba/ for installation.'			)
									SCREAMING_SNAKE_CASE				=	jieba
									SCREAMING_SNAKE_CASE				=	str.maketrans(' \n'		,	'\u2582\u2583'			)


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[self.cls_token_id]
									if token_ids_a is None:
														return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[2]

									if token_ids_a is None:
														return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple[str]:
									"""simple docstring"""







									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
														    'tokenizer.'			)

									if not os.path.isdir(lowercase__			):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
									SCREAMING_SNAKE_CASE				=	os.path.join(
									    lowercase__		,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(lowercase__			):
														copyfile(self.vocab_file		,	lowercase__			)

									return (out_vocab_file,)


				def 							A					(				self		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[
									    ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowercase__		,	cut_all=lowercase__			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(lowercase__		,	*lowercase__		,	**lowercase__			)




				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	super()._decode(*lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	text.replace(' '		,	''			).replace('\u2582'		,	' '			).replace('\u2583'		,	'\n'			)
									return text
