





"""simple docstring"""






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

import sentencepiece as spm

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


A			      =	logging.get_logger(__name__)

A			      =	'''▁'''

A			      =	{'''vocab_file''': '''sentencepiece.bpe.model'''}

A			      =	{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'''
        ),
    }
}

A			      =	{
    '''facebook/nllb-200-distilled-600M''': 1_024,
}

# fmt: off
A			      =	['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	VOCAB_FILES_NAMES
					__lowerCAmelCase	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__lowerCAmelCase	=	PRETRAINED_VOCAB_FILES_MAP
					__lowerCAmelCase	=	['''input_ids''', '''attention_mask''']

					__lowerCAmelCase	=	[]
					__lowerCAmelCase	=	[]







					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase="<s>"						,      _UpperCAmelCase="</s>"						,      _UpperCAmelCase="</s>"						,      _UpperCAmelCase="<s>"						,      _UpperCAmelCase="<unk>"						,      _UpperCAmelCase="<pad>"						,      _UpperCAmelCase="<mask>"						,      _UpperCAmelCase=None						,      _UpperCAmelCase=None						,      _UpperCAmelCase=None						,      _UpperCAmelCase = None						,      _UpperCAmelCase=None						,      _UpperCAmelCase=False						,      **_UpperCAmelCase						,      ):
									# Mask token behave like a normal word, i.e. include the space before it
									__a			:    List[Any]      							=	AddedToken(_UpperCAmelCase						,      lstrip=_UpperCAmelCase						,      rstrip=_UpperCAmelCase  ) if isinstance(_UpperCAmelCase						,      _UpperCAmelCase  ) else mask_token

									__a			:    Optional[Any]      							=	{} if sp_model_kwargs is None else sp_model_kwargs
									__a			:    Any      							=	legacy_behaviour

									super().__init__(
									    bos_token=_UpperCAmelCase						,      eos_token=_UpperCAmelCase						,      unk_token=_UpperCAmelCase						,      sep_token=_UpperCAmelCase						,      cls_token=_UpperCAmelCase						,      pad_token=_UpperCAmelCase						,      mask_token=_UpperCAmelCase						,      tokenizer_file=_UpperCAmelCase						,      src_lang=_UpperCAmelCase						,      tgt_lang=_UpperCAmelCase						,      additional_special_tokens=_UpperCAmelCase						,      sp_model_kwargs=self.sp_model_kwargs						,      legacy_behaviour=_UpperCAmelCase						,      **_UpperCAmelCase						,      )

									__a			:    Any      							=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.Load(str(_UpperCAmelCase  )  )
									__a			:    Tuple      							=	vocab_file
									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
									# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
									# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

									# Mimic fairseq token-to-id alignment for the first 4 token
									__a			:    int      							=	{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									__a			:    Dict      							=	1

									__a			:    Optional[Any]      							=	len(self.sp_model  )
									__a			:    Dict      							=	{
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCAmelCase  )
									}
									__a			:    Dict      							=	{v: k for k, v in self.lang_code_to_id.items()}
									__a			:    str      							=	len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
									__a			:    List[Any]      							=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
									__a			:    Union[str, Any]      							=	list(self.lang_code_to_id.keys()  )

									if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													self._additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in self._additional_special_tokens]  )

									__a			:    List[str]      							=	src_lang if src_lang is not None else '''eng_Latn'''
									__a			:    List[Any]      							=	self.lang_code_to_id[self._src_lang]
									__a			:    List[str]      							=	tgt_lang
									self.set_src_lang_special_tokens(self._src_lang  )







					def __getstate__(    self  ):
									__a			:    Dict      							=	self.__dict__.copy()
									__a			:    Optional[Any]      							=	None
									__a			:    int      							=	self.sp_model.serialized_model_proto()
									return state







					def __setstate__(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	d

									# for backward compatibility
									if not hasattr(self						,      '''sp_model_kwargs'''  ):
													__a			:    Dict      							=	{}

									__a			:    List[str]      							=	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )







					@property
					def 			_lowerCamelCase		(    self  ):
									return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token







					@property
					def 			_lowerCamelCase		(    self  ):
									return self._src_lang







					@src_lang.setter
					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    Optional[int]      							=	new_src_lang
									self.set_src_lang_special_tokens(self._src_lang  )







					def 			_lowerCamelCase		(    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  )

									__a			:    List[Any]      							=	[1] * len(self.prefix_tokens  )
									__a			:    List[str]      							=	[1] * len(self.suffix_tokens  )
									if token_ids_a is None:
													return prefix_ones + ([0] * len(_UpperCAmelCase  )) + suffix_ones
									return prefix_ones + ([0] * len(_UpperCAmelCase  )) + ([0] * len(_UpperCAmelCase  )) + suffix_ones







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = None  ):
									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = None  ):
									__a			:    Dict      							=	[self.sep_token_id]
									__a			:    str      							=	[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 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      **_UpperCAmelCase  ):
									if src_lang is None or tgt_lang is None:
													raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
									__a			:    List[str]      							=	src_lang
									__a			:    Dict      							=	self(_UpperCAmelCase						,      add_special_tokens=_UpperCAmelCase						,      return_tensors=_UpperCAmelCase						,      **_UpperCAmelCase  )
									__a			:    Any      							=	self.convert_tokens_to_ids(_UpperCAmelCase  )
									__a			:    int      							=	tgt_lang_id
									return inputs







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	{self.convert_ids_to_tokens(_UpperCAmelCase  ): i for i in range(self.vocab_size  )}
									vocab.update(self.added_tokens_encoder  )
									return vocab







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									return self.sp_model.encode(_UpperCAmelCase						,      out_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									__a			:    Tuple      							=	self.sp_model.PieceToId(_UpperCAmelCase  )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    int      							=	''''''.join(_UpperCAmelCase  ).replace(_UpperCAmelCase						,      ''' '''  ).strip()
									return out_string







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

									if os.path.abspath(self.vocab_file  ) != os.path.abspath(_UpperCAmelCase  ) and os.path.isfile(self.vocab_file  ):
													copyfile(self.vocab_file						,      _UpperCAmelCase  )
									elif not os.path.isfile(self.vocab_file  ):
													with open(_UpperCAmelCase						,      '''wb'''  ) as fi:
																	__a			:    int      							=	self.sp_model.serialized_model_proto()
																	fi.write(_UpperCAmelCase  )

									return (out_vocab_file,)







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase = "eng_Latn"						,      _UpperCAmelCase = None						,      _UpperCAmelCase = "fra_Latn"						,      **_UpperCAmelCase						,      ):
									__a			:    Optional[int]      							=	src_lang
									__a			:    Tuple      							=	tgt_lang
									return super().prepare_seqaseq_batch(_UpperCAmelCase						,      _UpperCAmelCase						,      **_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									return self.set_src_lang_special_tokens(self.src_lang  )







					def 			_lowerCamelCase		(    self  ):
									return self.set_tgt_lang_special_tokens(self.tgt_lang  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	self.lang_code_to_id[src_lang]
									if self.legacy_behaviour:
													__a			:    Any      							=	[]
													__a			:    Dict      							=	[self.eos_token_id, self.cur_lang_code]
									else:
													__a			:    Dict      							=	[self.cur_lang_code]
													__a			:    Optional[Any]      							=	[self.eos_token_id]







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    Dict      							=	self.lang_code_to_id[lang]
									if self.legacy_behaviour:
													__a			:    List[Any]      							=	[]
													__a			:    str      							=	[self.eos_token_id, self.cur_lang_code]
									else:
													__a			:    List[str]      							=	[self.cur_lang_code]
													__a			:    Union[str, Any]      							=	[self.eos_token_id]




'''simple docstring'''

# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=0					)				->					Dict:





			"""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(__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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Any      							=param.size()
			if checkpoint_version == 1.0:
						# version 1.0 stores [num_heads * hidden_size * num_splits, :]
						snake_case:    Tuple      							=(num_heads, hidden_size, num_splits) + input_shape[1:]
						snake_case:    Tuple      							=param.view(*__UpperCAmelCase					)
						snake_case:    List[Any]      							=param.transpose(0				,							2					)
						snake_case:    Union[str, Any]      							=param.transpose(1				,							2					).contiguous()
			elif checkpoint_version >= 2.0:
						# other versions store [num_heads * num_splits * hidden_size, :]
						snake_case:    Any      							=(num_heads, num_splits, hidden_size) + input_shape[1:]
						snake_case:    str      							=param.view(*__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=param.transpose(0				,							1					).contiguous()
			snake_case:    Any      							=param.view(*__UpperCAmelCase					)
			return param







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Optional[Any]      							={}

			# old versions did not store training args
			snake_case:    Dict      							=input_state_dict.get('args'				,							__UpperCAmelCase					)
			if ds_args is not None:
						# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
						# from pprint import pprint
						# pprint(vars(ds_args))

						snake_case:    List[Any]      							=ds_args.padded_vocab_size
						snake_case:    List[Any]      							=ds_args.max_position_embeddings
						snake_case:    str      							=ds_args.hidden_size
						snake_case:    Any      							=ds_args.num_layers
						snake_case:    Dict      							=ds_args.num_attention_heads
						snake_case:    Dict      							=ds_args.ffn_hidden_size
						# pprint(config)

			# The number of heads.
			snake_case:    Any      							=config.n_head
			# The hidden_size per head.
			snake_case:    Union[str, Any]      							=config.n_embd // config.n_head
			# Megatron-LM checkpoint version
			if "checkpoint_version" in input_state_dict.keys():
						snake_case:    Any      							=input_state_dict['checkpoint_version']
			else:
						snake_case:    Optional[int]      							=0.0

			# The model.
			snake_case:    List[str]      							=input_state_dict['model']
			# The language model.
			snake_case:    List[Any]      							=model['language_model']
			# The embeddings.
			snake_case:    Union[str, Any]      							=lm['embedding']

			# The word embeddings.
			snake_case:    List[Any]      							=embeddings['word_embeddings']['weight']
			# Truncate the embedding table to vocab_size rows.
			snake_case:    Dict      							=word_embeddings[: config.vocab_size, :]
			snake_case:    List[str]      							=word_embeddings

			# The position embeddings.
			snake_case:    str      							=embeddings['position_embeddings']['weight']
			# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
			snake_case:    Dict      							=pos_embeddings.size(0					)
			if n_positions != config.n_positions:
						raise ValueError(
						    f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''					)
			# Store the position embeddings.
			snake_case:    Any      							=pos_embeddings

			# The transformer.
			snake_case:    Union[str, Any]      							=lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

			# The regex to extract layer names.
			snake_case:    Union[str, Any]      							=re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'					)

			# The simple map of names for "automated" rules.
			snake_case:    List[str]      							={
			    'attention.dense': '.attn.c_proj.',
			    'self_attention.dense': '.attn.c_proj.',
			    'mlp.dense_h_to_4h': '.mlp.c_fc.',
			    'mlp.dense_4h_to_h': '.mlp.c_proj.',
			}

			# Extract the layers.
			for key, val in transformer.items():
						# Match the name.
						snake_case:    Union[str, Any]      							=layer_re.match(__UpperCAmelCase					)

						# Stop if that's not a layer
						if m is None:
									break

						# The index of the layer.
						snake_case:    str      							=int(m.group(1					)					)
						# The name of the operation.
						snake_case:    Optional[Any]      							=m.group(2					)
						# Is it a weight or a bias?
						snake_case:    Any      							=m.group(3					)

						# The name of the layer.
						snake_case:    Tuple      							=f'''transformer.h.{layer_idx}'''

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith('layernorm'					):
									snake_case:    Union[str, Any]      							='ln_1' if op_name.startswith('input'					) else 'ln_2'
									snake_case:    List[str]      							=val

						# Transpose the QKV matrix.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "weight":
									# Insert a tensor of 1x1xDxD bias.
									snake_case:    Optional[Any]      							=torch.tril(torch.ones((n_positions, n_positions)				,							dtype=torch.floataa					)					).view(
									    1				,							1				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									snake_case:    int      							=causal_mask

									# Insert a "dummy" tensor for masked_bias.
									snake_case:    Dict      							=torch.tensor(-1e4				,							dtype=torch.floataa					)
									snake_case:    Optional[Any]      							=masked_bias

									snake_case:    Dict      							=fix_query_key_value_ordering(__UpperCAmelCase				,							__UpperCAmelCase				,							3				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
									snake_case:    Dict      							=out_val.transpose(0				,							1					).contiguous()
									# Store.
									snake_case:    Optional[Any]      							=out_val

						# Transpose the bias.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "bias":
									snake_case:    Dict      							=fix_query_key_value_ordering(__UpperCAmelCase				,							__UpperCAmelCase				,							3				,							__UpperCAmelCase				,							__UpperCAmelCase					)
									# Store. No change of shape.
									snake_case:    str      							=out_val

						# Transpose the weights.
						elif weight_or_bias == "weight":
									snake_case:    Optional[int]      							=megatron_to_transformers[op_name]
									snake_case:    str      							=val.transpose(0				,							1					)

						# Copy the bias.
						elif weight_or_bias == "bias":
									snake_case:    int      							=megatron_to_transformers[op_name]
									snake_case:    Dict      							=val

    # DEBUG.
			assert config.n_layer == layer_idx + 1

			# The final layernorm.
			snake_case:    Optional[int]      							=transformer['final_layernorm.weight']
			snake_case:    Optional[Any]      							=transformer['final_layernorm.bias']

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

			# It should be done!
			return output_state_dict







def a_     (    )				->					Tuple:





			"""simple docstring"""







			snake_case:    List[str]      							=argparse.ArgumentParser()
			parser.add_argument('--print-checkpoint-structure'				,							action='store_true'					)
			parser.add_argument(
			    'path_to_checkpoint'				,							type=__UpperCAmelCase				,							help='Path to the checkpoint file (.zip archive or direct .pt file)'				,							)
			parser.add_argument(
			    '--config_file'				,							default=''				,							type=__UpperCAmelCase				,							help='An optional config json file describing the pre-trained model.'				,							)
			snake_case:    List[Any]      							=parser.parse_args()

			# Extract the basename.
			snake_case:    Any      							=os.path.dirname(args.path_to_checkpoint					)

			# Load the model.
			# the .zip is very optional, let's keep it for backward compatibility
			print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''					)
			if args.path_to_checkpoint.endswith('.zip'					):
						with zipfile.ZipFile(args.path_to_checkpoint				,							'r'					) as checkpoint:
									with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'					) as pytorch_dict:
												snake_case:    List[Any]      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)
			else:
						snake_case:    Dict      							=torch.load(args.path_to_checkpoint				,							map_location='cpu'					)

			snake_case:    Optional[Any]      							=input_state_dict.get('args'				,							__UpperCAmelCase					)

			# Read the config, or default to the model released by NVIDIA.
			if args.config_file == "":
						if ds_args is not None:
									if ds_args.bias_gelu_fusion:
												snake_case:    List[Any]      							='gelu_fast'
									elif ds_args.openai_gelu:
												snake_case:    Optional[int]      							='gelu_new'
									else:
												snake_case:    Any      							='gelu'
						else:
									# in the very early days this used to be "gelu_new"
									snake_case:    Dict      							='gelu_new'

						# Spell out all parameters in case the defaults change.
						snake_case:    Union[str, Any]      							=GPTaConfig(
						    vocab_size=5_02_57				,							n_positions=10_24				,							n_embd=10_24				,							n_layer=24				,							n_head=16				,							n_inner=40_96				,							activation_function=__UpperCAmelCase				,							resid_pdrop=0.1				,							embd_pdrop=0.1				,							attn_pdrop=0.1				,							layer_norm_epsilon=1e-5				,							initializer_range=0.02				,							summary_type='cls_index'				,							summary_use_proj=__UpperCAmelCase				,							summary_activation=__UpperCAmelCase				,							summary_proj_to_labels=__UpperCAmelCase				,							summary_first_dropout=0.1				,							scale_attn_weights=__UpperCAmelCase				,							use_cache=__UpperCAmelCase				,							bos_token_id=5_02_56				,							eos_token_id=5_02_56				,							)
			else:
						snake_case:    Optional[Any]      							=GPTaConfig.from_json_file(args.config_file					)

			snake_case:    int      							=['GPT2LMHeadModel']

			# Convert.
			print('Converting'					)
			snake_case:    str      							=convert_megatron_checkpoint(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)

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

			# Add tokenizer class info to config
			# see https://github.com/huggingface/transformers/issues/13906)
			if ds_args is not None:
						snake_case:    Dict      							=ds_args.tokenizer_type
						if tokenizer_type == "GPT2BPETokenizer":
									snake_case:    Tuple      							='gpt2'
						elif tokenizer_type == "PretrainedFromHF":
									snake_case:    int      							=ds_args.tokenizer_name_or_path
						else:
									raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''					)
			else:
						snake_case:    Optional[Any]      							='gpt2'

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(__UpperCAmelCase					)
			snake_case:    Any      							=type(__UpperCAmelCase					).__name__
			snake_case:    Optional[Any]      							=tokenizer_class

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

			# Save tokenizer based on args
			print(f'''Adding {tokenizer_class} tokenizer files'''					)
			tokenizer.save_pretrained(__UpperCAmelCase					)

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


####################################################################################################

if __name__ == "__main__":
	main()

####################################################################################################




