



"""simple docstring"""


import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
				from comet_ml import OfflineExperiment

if is_tensorboard_available():
				import struct

				import tensorboard.compat.proto.event_pba as event_pba

snake_case_								=			logging.getLogger(__name__)







@require_tensorboard
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     int:
												UpperCAmelCase			 =					'test_project_with_config'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																		accelerator.init_trackers(lowercase_  ,    lowercase_   )
																		accelerator.end_training()
																		for child in Path(f"""{dirpath}/{project_name}"""   ).glob('*/**'   ):
																								UpperCAmelCase			 =					list(filter(lambda lowercase_   : x.is_file()  ,    child.iterdir()   )   )[0]
																		self.assertNotEqual(str(lowercase_   )  ,    ''   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Tuple:
												UpperCAmelCase			 =					'test_project_with_log'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		accelerator.init_trackers(lowercase_   )
																		UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																		# Names are randomly generated each time
																		UpperCAmelCase			 =					list(filter(lambda lowercase_   : x.is_file()  ,    Path(f"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
																		self.assertNotEqual(str(lowercase_   )  ,    ''   )






						def       UpperCAmelCase__	(   self					:Any   )							->     List[Any]:
												UpperCAmelCase			 =					'test_project_with_log'
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )
																		accelerator.init_trackers(lowercase_   )
																		UpperCAmelCase			 =					{'tensor': torch.tensor(1   )}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
																		# Names are randomly generated each time
																		UpperCAmelCase			 =					list(filter(lambda lowercase_   : x.is_file()  ,    Path(f"""{dirpath}/{project_name}"""   ).iterdir()   )   )[0]
																		# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
																		with open(lowercase_  ,    'rb'   ) as f:
																								UpperCAmelCase			 =					f.read()
																		UpperCAmelCase			 =					False
																		while data:
																								UpperCAmelCase			 =					struct.unpack('Q'  ,    data[:8]   )

																								UpperCAmelCase			 =					data[12 : 12 + int(header[0]   )]  # 8+4
																								UpperCAmelCase			 =					data[12 + int(header[0]   ) + 4 :]
																								UpperCAmelCase			 =					event_pba.Event()

																								event.ParseFromString(lowercase_   )
																								if event.HasField('summary'   ):
																														for value in event.summary.value:
																																				if value.simple_value == 1.0 and value.tag == "tensor":
																																										UpperCAmelCase			 =					True
																		self.assertTrue(lowercase_  ,    'Converted tensor was not found in the log file!'   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     int:
												with self.assertRaisesRegex(lowercase_  ,    'Logging with `tensorboard` requires a `logging_dir`'   ):
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'   )
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												UpperCAmelCase			 =					ProjectConfiguration(total_limit=30   )
												with tempfile.TemporaryDirectory() as dirpath:
																		UpperCAmelCase			 =					Accelerator(log_with='tensorboard'  ,    project_dir=lowercase_  ,    project_config=lowercase_   )







@require_wandb
@mock.patch.dict(os.environ      ,  {"""WANDB_MODE""": """offline"""}							)
class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Dict   )							->     Optional[int]:
												super().setUp()
												# wandb let's us override where logs are stored to via the WANDB_DIR env var
												self.add_mocks(mock.patch.dict(os.environ  ,    {'WANDB_DIR': self.tmpdir}   )   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str  ,    lowercase_					:str  ,    lowercase_					:bool = True   )							->     Dict:
												UpperCAmelCase			 =					Rf"""{section} ([\S\s]*?)\n\n"""
												if record:
																		UpperCAmelCase			 =					Rf"""Record: {pattern}"""
												UpperCAmelCase			 =					re.findall(lowercase_  ,    lowercase_   )[0]
												# A config
												if section == "config" or section == "history":
																		UpperCAmelCase			 =					re.findall(R'"([a-zA-Z0-9_.,]+)'  ,    lowercase_   )
																		return {key: val for key, val in zip(cleaned_record[0::2]  ,    cleaned_record[1::2]   )}
												# Everything else
												else:
																		return dict(re.findall(R'(\w+): "([^\s]+)"'  ,    lowercase_   )   )






						@skip
						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Any:
												UpperCAmelCase			 =					'test_project_with_config'
												UpperCAmelCase			 =					Accelerator(log_with='wandb'   )
												UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
												UpperCAmelCase			 =					{'wandb': {'tags': ['my_tag']}}
												accelerator.init_trackers(lowercase_  ,    lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
												accelerator.log(lowercase_  ,    step=0   )
												accelerator.end_training()
												# The latest offline log is stored at wandb/latest-run/*.wandb
												for child in Path(f"""{self.tmpdir}/wandb/latest-run"""   ).glob('*'   ):
																		if child.is_file() and child.suffix == ".wandb":
																								UpperCAmelCase			 =					subprocess.check_output(
																								    ['wandb', 'sync', '--view', '--verbose', str(lowercase_   )]  ,    env=os.environ.copy()   ).decode('utf8'  ,    'ignore'   )
																								break

        # Check HPS through careful parsing and cleaning
												UpperCAmelCase			 =					self.parse_log(lowercase_  ,    'config'   )
												self.assertEqual(logged_items['num_iterations']  ,    '12'   )
												self.assertEqual(logged_items['learning_rate']  ,    '0.01'   )
												self.assertEqual(logged_items['some_boolean']  ,    'false'   )
												self.assertEqual(logged_items['some_string']  ,    'some_value'   )
												self.assertEqual(logged_items['some_string']  ,    'some_value'   )

												# Run tags
												UpperCAmelCase			 =					self.parse_log(lowercase_  ,    'run'  ,    lowercase_   )
												self.assertEqual(logged_items['tags']  ,    'my_tag'   )

												# Actual logging
												UpperCAmelCase			 =					self.parse_log(lowercase_  ,    'history'   )
												self.assertEqual(logged_items['total_loss']  ,    '0.1'   )
												self.assertEqual(logged_items['iteration']  ,    '1'   )
												self.assertEqual(logged_items['my_text']  ,    'some_value'   )
												self.assertEqual(logged_items['_step']  ,    '0'   )


def 					_lowerCAmelCase (  self						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					run_name
						UpperCAmelCase			 =					OfflineExperiment(project_name=lowercase_						,      offline_directory=lowercase_		)
						logger.info(F"""Initialized offline CometML project {self.run_name}"""		)
						logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'		)







@require_comet_ml
@mock.patch.object(SCREAMING_SNAKE_CASE_      ,  """__init__"""      ,  SCREAMING_SNAKE_CASE_							)
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:bool = False   )							->     int:
												for log in log_list:
																		UpperCAmelCase			 =					json.loads(lowercase_   )['payload']
																		if is_param and "param" in j.keys():
																								if j["param"]["paramName"] == key:
																														return j["param"]["paramValue"]
																		if "log_other" in j.keys():
																								if j["log_other"]["key"] == key:
																														return j["log_other"]["val"]
																		if "metric" in j.keys():
																								if j["metric"]["metricName"] == key:
																														return j["metric"]["metricValue"]






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Tuple:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					CometMLTracker('test_project_with_config'  ,    lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																		accelerator.init_trackers(lowercase_  ,    lowercase_   )
																		accelerator.end_training()
																		UpperCAmelCase			 =					os.listdir(lowercase_   )[0]  # Comet is nice, it's just a zip file here
																		# We parse the raw logs
																		UpperCAmelCase			 =					os.path.join(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					zipfile.ZipFile(lowercase_  ,    'r'   )
																		UpperCAmelCase			 =					archive.open('messages.json'   ).read().decode('utf-8'   )
												UpperCAmelCase			 =					log.split('\n'   )[:-1]
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'num_iterations'  ,    lowercase_   )  ,    12   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'learning_rate'  ,    lowercase_   )  ,    0.01   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'some_boolean'  ,    lowercase_   )  ,    lowercase_   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'some_string'  ,    lowercase_   )  ,    'some_value'   )






						def       UpperCAmelCase__	(   self					:str   )							->     Optional[int]:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					CometMLTracker('test_project_with_config'  ,    lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		accelerator.init_trackers(lowercase_   )
																		UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		UpperCAmelCase			 =					os.listdir(lowercase_   )[0]  # Comet is nice, it's just a zip file here
																		# We parse the raw logs
																		UpperCAmelCase			 =					os.path.join(lowercase_  ,    lowercase_   )
																		UpperCAmelCase			 =					zipfile.ZipFile(lowercase_  ,    'r'   )
																		UpperCAmelCase			 =					archive.open('messages.json'   ).read().decode('utf-8'   )
												UpperCAmelCase			 =					log.split('\n'   )[:-1]
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'curr_step'  ,    lowercase_   )  ,    0   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'total_loss'   )  ,    0.1   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'iteration'   )  ,    1   )
												self.assertEqual(self.get_value_from_key(lowercase_  ,    'my_text'   )  ,    'some_value'   )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				[
						    """total_loss""",
						    """iteration""",
						    """my_text""",
						    """learning_rate""",
						    """num_iterations""",
						    """some_boolean""",
						    """some_string""",
						]

						__UpperCamelCase													=				"""my_custom_tracker"""
						__UpperCamelCase													=				False






						def __init__(   self					:Optional[int]  ,    lowercase_					:str   )							->     Optional[int]:
												UpperCAmelCase			 =					open(f"""{dir}/log.csv"""  ,    'w+'   )
												UpperCAmelCase			 =					csv.DictWriter(self.f  ,    fieldnames=self._col_names   )
												self.writer.writeheader()






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[Any]:
												return self.writer






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:dict   )							->     Optional[int]:
												logger.info('Call init'   )
												self.writer.writerow(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:dict  ,    lowercase_					:Optional[int]   )							->     Optional[int]:
												logger.info('Call log'   )
												self.writer.writerow(lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[int]:
												self.f.close()







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					MyCustomTracker(lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		UpperCAmelCase			 =					{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
																		accelerator.init_trackers('Some name'  ,    lowercase_   )
																		accelerator.end_training()
																		with open(f"""{d}/log.csv"""  ,    'r'   ) as f:
																								UpperCAmelCase			 =					csv.DictReader(lowercase_   )
																								UpperCAmelCase			 =					next(lowercase_   )
																								UpperCAmelCase			 =					{
																								    'total_loss': '',
																								    'iteration': '',
																								    'my_text': '',
																								    'learning_rate': '0.01',
																								    'num_iterations': '12',
																								    'some_boolean': 'False',
																								    'some_string': 'some_value',
																								}
																								self.assertDictEqual(lowercase_  ,    lowercase_   )






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Union[str, Any]:
												with tempfile.TemporaryDirectory() as d:
																		UpperCAmelCase			 =					MyCustomTracker(lowercase_   )
																		UpperCAmelCase			 =					Accelerator(log_with=lowercase_   )
																		accelerator.init_trackers('Some name'   )
																		UpperCAmelCase			 =					{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
																		accelerator.log(lowercase_  ,    step=0   )
																		accelerator.end_training()
																		with open(f"""{d}/log.csv"""  ,    'r'   ) as f:
																								UpperCAmelCase			 =					csv.DictReader(lowercase_   )
																								UpperCAmelCase			 =					next(lowercase_   )
																								UpperCAmelCase			 =					{
																								    'total_loss': '0.1',
																								    'iteration': '1',
																								    'my_text': 'some_value',
																								    'learning_rate': '',
																								    'num_iterations': '',
																								    'some_boolean': '',
																								    'some_string': '',
																								}
																								self.assertDictEqual(lowercase_  ,    lowercase_   )












'''simple docstring'''






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 				_lowerCamelCase(	UpperCamelCase__     :			List[str]     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			List[str]=0		)		->	Optional[int]:
    # Format the message.
    if name is None:
        A							:							Dict				=    None
    else:
        A							:							Optional[Any]				=    '''.''' * max(0     ,							spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
        A							:							Tuple				=    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 				_lowerCamelCase(	UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[Any]     ,							UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[str]		)		->	Dict:
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    A							:							int				=    param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A							:							List[str]				=    (num_heads, num_splits, hidden_size) + input_shape[1:]
    A							:							Optional[int]				=    param.view(*UpperCamelCase__		)
    A							:							Optional[Any]				=    param.transpose(0     ,							1		).contiguous()
    A							:							Dict				=    param.view(*UpperCamelCase__		)
    return param
def 				_lowerCamelCase(	UpperCamelCase__     :			Any     ,							UpperCamelCase__     :			Tuple		)		->	Optional[int]:
    A							:							int				=    config.n_positions
    A							:							Dict				=    config.n_layer
    A							:							List[str]				=    config.vocab_size
    A							:							Optional[Any]				=    config.n_head
    A							:							Union[str, Any]				=    config.n_embd // config.n_head

    A							:							Optional[int]				=    sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
    A							:							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'''],
    }

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

        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

        A							:							Any				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
        A							:							List[Any]				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala.transpose(0     ,							1		).contiguous()

        A							:							List[Any]				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
        A							:							Any				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala

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

    # For LM head, transformers' wants the matrix to weight embeddings.
    A							:							Optional[int]				=    word_embeddings

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

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

    return config_hf
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Tuple:
    print(UpperCamelCase__		)

    A							:							Tuple				=    args.checkpoint_path
    A							:							Dict				=    args.save_path
    if isfile(UpperCamelCase__		):
        raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''		)

    # Load the model.
    A							:							str				=    torch.load(UpperCamelCase__     ,							map_location='''cpu'''		)

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

    A							:							Optional[int]				=    checkpoint['''state_dict''']

    # Convert.
    print('''Converting'''		)
    A							:							Any				=    convert_megatron_checkpoint(UpperCamelCase__     ,							UpperCamelCase__		)

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

    A							:							Union[str, Any]				=    '''GPTSw3Tokenizer'''

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

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


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