
import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

snake_case__  							=							logging.getLogger()
snake_case__  							=							torch.cuda.is_available()
snake_case__  							=							{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-0_8,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}

def        lowerCamelCase_      (			UpperCAmelCase_							:				Path							,			UpperCAmelCase_							:				list       ):
						lowercase       :    Tuple											=  '''\n'''.join(UpperCAmelCase_       )
						Path(UpperCAmelCase_       ).open('''w'''       ).writelines(UpperCAmelCase_       )


snake_case__  							=							[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case__  							=							["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case__  							=							"""patrickvonplaten/t5-tiny-random"""
snake_case__  							=							"""sshleifer/t5-tinier-random"""
snake_case__  							=							"""sshleifer/bart-tiny-random"""
snake_case__  							=							"""sshleifer/tiny-mbart"""
snake_case__  							=							"""sshleifer/tiny-marian-en-de"""
snake_case__  							=							"""stas/tiny-wmt19-en-de"""


snake_case__  							=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple       ):
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(UpperCAmelCase_							,			f'''{split}.source'''       )							,			UpperCAmelCase_       )
												_dump_articles(os.path.join(UpperCAmelCase_							,			f'''{split}.target'''       )							,			UpperCAmelCase_       )
						return tmp_dir







class 							UpperCAmelCase	(   __lowerCamelCase					):

					@classmethod
					def        _lowerCAmelCase	(			cls      :					Tuple					):
											logging.disable(logging.CRITICAL					)  # remove noisy download output from tracebacks
											return cls

					@slow
					@require_torch_gpu
					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    List[Any]											=  list_models()
											lowercase       :    Dict											=  '''sshleifer'''
											lowercase       :    str											=  [x.modelId for x in model_list if x.modelId.startswith(lowerCAmelCase					)]
											lowercase       :    Any											=  ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
											lowercase       :    int											=  []
											for m in model_ids:
																	if m in allowed_to_be_broken:
																							continue
																	try:
																							AutoConfig.from_pretrained(lowerCAmelCase					)
																	except Exception:
																							failures.append(lowerCAmelCase					)
											assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''

					def        _lowerCAmelCase	(			self      :					Tuple					):
											lowercase       :    Optional[int]											=  {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
											self._test_distiller_cli(lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											lowercase       :    str											=  {
											    '''student_encoder_layers''': 2,
											    '''student_decoder_layers''': 1,
											    '''max_epochs''': 4,
											    '''val_check_interval''': 0.25,
											    '''alpha_hid''': 2.0,
											    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
											}
											lowercase       :    List[str]											=  self._test_distiller_cli(lowerCAmelCase ,      check_contents=lowerCAmelCase					)

											lowercase       :    Tuple											=  list(Path(model.output_dir					).glob('''*.ckpt'''					)					)
											self.assertEqual(1 ,      len(lowerCAmelCase					)					)
											lowercase       :    Optional[Any]											=  list(Path(model.output_dir					).glob('''**/*.bin'''					)					)
											self.assertEqual(len(lowerCAmelCase					) ,      2					)
											lowercase       :    Optional[int]											=  lmap(str.strip ,      Path(model.hparams.data_dir					).joinpath('''test.source'''					).open().readlines()					)
											lowercase       :    Dict											=  tempfile.mktemp()  # XXX: not being cleaned up
											generate_summaries_or_translations(lowerCAmelCase ,      lowerCAmelCase ,      str(model.output_dir / '''best_tfmr'''					)					)
											self.assertTrue(Path(lowerCAmelCase					).exists()					)

											lowercase       :    Optional[int]											=  self.get_auto_remove_tmp_dir()
											convert_pl_to_hf(ckpts[0] ,      transformer_ckpts[0].parent ,      lowerCAmelCase					)
											assert os.path.exists(os.path.join(lowerCAmelCase ,      '''pytorch_model.bin'''					)					)

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											lowercase       :    List[str]											=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase					)
											lowercase				,				lowercase       :    int											=  model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
											lowercase       :    int											=  torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]] ,      dtype=torch.long ,      device=model.device					)
											lowercase       :    Union[str, Any]											=  target_ids[:, :-1].contiguous()  # Why this line?
											lowercase       :    Dict											=  target_ids[:, 1:].clone()  # why clone?
											lowercase       :    Optional[int]											=  model(
											    lowerCAmelCase ,      attention_mask=lowerCAmelCase ,      decoder_input_ids=lowerCAmelCase ,      labels=lowerCAmelCase ,      use_cache=lowerCAmelCase					).loss

											lowercase       :    Optional[int]											=  model(lowerCAmelCase ,      attention_mask=lowerCAmelCase ,      decoder_input_ids=lowerCAmelCase ,      use_cache=lowerCAmelCase					).logits

											lowercase       :    Union[str, Any]											=  nn.functional.log_softmax(lowerCAmelCase ,      dim=-1					)
											lowercase				,				lowercase       :    Any											=  label_smoothed_nll_loss(
											    lowerCAmelCase ,      lowerCAmelCase ,      0.1 ,      ignore_index=model.config.pad_token_id					)
											with self.assertRaises(lowerCAmelCase					):
																	# TODO: understand why this breaks
																	self.assertEqual(lowerCAmelCase ,      lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    Any											=  {
											    '''student_encoder_layers''': 2,
											    '''student_decoder_layers''': 1,
											    '''num_train_epochs''': 4,
											    '''val_check_interval''': 0.25,
											    '''alpha_hid''': 2.0,
											    '''task''': '''translation''',
											    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
											    '''tokenizer_name''': MBART_TINY,
											    '''teacher''': MBART_TINY,
											    '''src_lang''': '''en_XX''',
											    '''tgt_lang''': '''ro_RO''',
											}
											lowercase       :    List[str]											=  self._test_distiller_cli(lowerCAmelCase ,      check_contents=lowerCAmelCase					)
											assert model.model.config.model_type == "mbart"

											lowercase       :    str											=  list(Path(model.output_dir					).glob('''*.ckpt'''					)					)
											self.assertEqual(1 ,      len(lowerCAmelCase					)					)
											lowercase       :    Optional[int]											=  list(Path(model.output_dir					).glob('''**/*.bin'''					)					)
											lowercase       :    str											=  list(Path(model.output_dir					).glob('''best_tfmr/*'''					)					)
											assert len(lowerCAmelCase					) > 2
											self.assertEqual(len(lowerCAmelCase					) ,      2					)

					def        _lowerCAmelCase	(			self      :					Tuple					):
											lowercase       :    Any											=  {
											    '''student_encoder_layers''': 1,
											    '''student_decoder_layers''': 1,
											    '''alpha_hid''': 2.0,
											    '''teacher''': T5_TINY,
											    '''model_name_or_path''': T5_TINY,
											    '''tokenizer_name''': T5_TINY,
											}
											self._test_distiller_cli(lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					int					):
											lowercase       :    Any											=  {
											    '''teacher''': T5_TINY,
											    '''student''': T5_TINIER,
											    '''model_name_or_path''': T5_TINIER,
											    '''tokenizer_name''': T5_TINIER,
											}
											self._test_distiller_cli(lowerCAmelCase					)




					def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Any=True					):
											lowercase       :    Dict											=  {
											    '''label_smoothing''': 0.0,
											    '''early_stopping_patience''': -1,
											    '''train_batch_size''': 1,
											    '''eval_batch_size''': 2,
											    '''max_epochs''': 2,
											    '''alpha_mlm''': 0.2,
											    '''alpha_ce''': 0.8,
											    '''do_predict''': True,
											    '''model_name_or_path''': '''sshleifer/tinier_bart''',
											    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
											    '''val_check_interval''': 0.5,
											}
											default_updates.update(lowerCAmelCase					)
											lowercase       :    dict											=  CHEAP_ARGS.copy()
											lowercase       :    List[Any]											=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
											lowercase       :    List[Any]											=  self.get_auto_remove_tmp_dir()

											args_d.update(data_dir=lowerCAmelCase ,      output_dir=lowerCAmelCase ,      **lowerCAmelCase					)
											lowercase       :    int											=  distill_main(argparse.Namespace(**lowerCAmelCase					)					)
											if not check_contents:
																	return model
											lowercase       :    str											=  os.listdir(lowerCAmelCase					)
											lowercase       :    List[str]											=  {os.path.basename(lowerCAmelCase					) for p in contents}
											lowercase       :    int											=  [p for p in contents if p.endswith('''ckpt'''					)]
											assert len(lowerCAmelCase					) > 0

											self.assertIn('''test_generations.txt''' ,      lowerCAmelCase					)
											self.assertIn('''test_results.txt''' ,      lowerCAmelCase					)

											lowercase       :    int											=  load_json(model.metrics_save_path					)
											lowercase       :    Dict											=  metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time'''] ,      0.01					)
											self.assertGreaterEqual(1.0 ,      last_step_stats['''val_avg_gen_time''']					)
											self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}'''] ,      lowerCAmelCase					)
											lowercase       :    Union[str, Any]											=  int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1					)
											self.assertEqual(len(metrics['''val''']					) ,      lowerCAmelCase					)
											self.assertEqual(len(metrics['''test''']					) ,      1					)
											return model







class 							UpperCAmelCase	(   __lowerCamelCase					):

					@parameterized.expand(
					    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY] ,      )
					def        _lowerCAmelCase	(			self      :					Optional[Any] ,      lowerCAmelCase      :					Union[str, Any]					):
											lowercase       :    dict											=  CHEAP_ARGS.copy()
											lowercase       :    Optional[int]											=  '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
											lowercase       :    Optional[Any]											=  0.1 if task == '''translation''' else 0

											lowercase       :    Optional[int]											=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
											lowercase       :    str											=  self.get_auto_remove_tmp_dir()
											args_d.update(
											    data_dir=lowerCAmelCase ,      model_name_or_path=lowerCAmelCase ,      tokenizer_name=lowerCAmelCase ,      train_batch_size=2 ,      eval_batch_size=2 ,      output_dir=lowerCAmelCase ,      do_predict=lowerCAmelCase ,      task=lowerCAmelCase ,      src_lang='''en_XX''' ,      tgt_lang='''ro_RO''' ,      freeze_encoder=lowerCAmelCase ,      freeze_embeds=lowerCAmelCase ,      )
											assert "n_train" in args_d
											lowercase       :    Tuple											=  argparse.Namespace(**lowerCAmelCase					)
											lowercase       :    str											=  main(lowerCAmelCase					)

											lowercase       :    Any											=  module.model.get_input_embeddings()
											assert not input_embeds.weight.requires_grad
											if model == T5_TINY:
																	lowercase       :    List[Any]											=  module.model.lm_head
																	assert not lm_head.weight.requires_grad
																	assert (lm_head.weight == input_embeds.weight).all().item()
											elif model == FSMT_TINY:
																	lowercase       :    Union[str, Any]											=  module.model.model
																	lowercase       :    int											=  fsmt.decoder.embed_positions
																	assert not embed_pos.weight.requires_grad
																	assert not fsmt.decoder.embed_tokens.weight.requires_grad
																	# check that embeds are not the same
																	assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
											else:
																	lowercase       :    Optional[Any]											=  module.model.model
																	lowercase       :    Any											=  bart.decoder.embed_positions
																	assert not embed_pos.weight.requires_grad
																	assert not bart.shared.weight.requires_grad
																	# check that embeds are the same
																	assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
																	assert bart.decoder.embed_tokens == bart.shared

											lowercase       :    Optional[Any]											=  load_json(module.output_dir / '''text_batch.json'''					)
											assert isinstance(lowerCAmelCase ,      lowerCAmelCase					)
											assert len(lowerCAmelCase					) >= 4

					def        _lowerCAmelCase	(			self      :					int					):
											lowercase       :    dict											=  CHEAP_ARGS.copy()

											lowercase       :    Union[str, Any]											=  '''summarization'''
											lowercase       :    Optional[int]											=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

											args_d.update(
											    data_dir=lowerCAmelCase ,      tokenizer_name=lowerCAmelCase ,      train_batch_size=2 ,      eval_batch_size=2 ,      do_predict=lowerCAmelCase ,      task=lowerCAmelCase ,      src_lang='''en_XX''' ,      tgt_lang='''ro_RO''' ,      freeze_encoder=lowerCAmelCase ,      freeze_embeds=lowerCAmelCase ,      )

											# test models whose config includes the extra_model_args
											lowercase       :    str											=  BART_TINY
											lowercase       :    List[Any]											=  self.get_auto_remove_tmp_dir()
											lowercase       :    Optional[Any]											=  args_d.copy()
											args_da.update(
											    model_name_or_path=lowerCAmelCase ,      output_dir=lowerCAmelCase ,      )
											lowercase       :    List[Any]											=  ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
											for p in extra_model_params:
																	lowercase       :    Tuple											=  0.5
											lowercase       :    Dict											=  argparse.Namespace(**lowerCAmelCase					)
											lowercase       :    int											=  main(lowerCAmelCase					)
											for p in extra_model_params:
																	assert getattr(model.config ,      lowerCAmelCase					) == 0.5, f'''failed to override the model config for param {p}'''

											# test models whose config doesn't include the extra_model_args
											lowercase       :    str											=  T5_TINY
											lowercase       :    int											=  self.get_auto_remove_tmp_dir()
											lowercase       :    Any											=  args_d.copy()
											args_da.update(
											    model_name_or_path=lowerCAmelCase ,      output_dir=lowerCAmelCase ,      )
											lowercase       :    Union[str, Any]											=  '''encoder_layerdrop'''
											lowercase       :    Union[str, Any]											=  0.5
											lowercase       :    Union[str, Any]											=  argparse.Namespace(**lowerCAmelCase					)
											with pytest.raises(lowerCAmelCase					) as excinfo:
																	lowercase       :    str											=  main(lowerCAmelCase					)
											assert str(excinfo.value					) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''




					def        _lowerCAmelCase	(			self      :					List[str]					):
											lowercase       :    dict											=  CHEAP_ARGS.copy()

											lowercase       :    int											=  '''summarization'''
											lowercase       :    str											=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

											lowercase       :    List[str]											=  BART_TINY
											lowercase       :    Optional[int]											=  self.get_auto_remove_tmp_dir()

											args_d.update(
											    data_dir=lowerCAmelCase ,      model_name_or_path=lowerCAmelCase ,      output_dir=lowerCAmelCase ,      tokenizer_name=lowerCAmelCase ,      train_batch_size=2 ,      eval_batch_size=2 ,      do_predict=lowerCAmelCase ,      task=lowerCAmelCase ,      src_lang='''en_XX''' ,      tgt_lang='''ro_RO''' ,      freeze_encoder=lowerCAmelCase ,      freeze_embeds=lowerCAmelCase ,      )

											# emulate finetune.py
											lowercase       :    int											=  argparse.ArgumentParser()
											lowercase       :    List[str]											=  pl.Trainer.add_argparse_args(lowerCAmelCase					)
											lowercase       :    Dict											=  SummarizationModule.add_model_specific_args(lowerCAmelCase ,      os.getcwd()					)
											lowercase       :    Union[str, Any]											=  {'''--help''': True}

											# --help test
											with pytest.raises(lowerCAmelCase					) as excinfo:
																	with CaptureStdout() as cs:
																							lowercase       :    int											=  parser.parse_args(lowerCAmelCase					)
																	assert False, "--help is expected to sys.exit"
											assert excinfo.type == SystemExit
											lowercase       :    int											=  lightning_base.arg_to_scheduler_metavar
											assert expected in cs.out, "--help is expected to list the supported schedulers"

											# --lr_scheduler=non_existing_scheduler test
											lowercase       :    Dict											=  '''non_existing_scheduler'''
											lowercase       :    List[str]											=  {f'''--lr_scheduler={unsupported_param}'''}
											with pytest.raises(lowerCAmelCase					) as excinfo:
																	with CaptureStderr() as cs:
																							lowercase       :    Tuple											=  parser.parse_args(lowerCAmelCase					)
																	assert False, "invalid argument is expected to sys.exit"
											assert excinfo.type == SystemExit
											lowercase       :    Optional[Any]											=  f'''invalid choice: \'{unsupported_param}\''''
											assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

											# --lr_scheduler=existing_scheduler test
											lowercase       :    Union[str, Any]											=  '''cosine'''
											lowercase       :    Tuple											=  args_d.copy()
											lowercase       :    List[str]											=  supported_param
											lowercase       :    Optional[Any]											=  argparse.Namespace(**lowerCAmelCase					)
											lowercase       :    Dict											=  main(lowerCAmelCase					)
											assert (
											    getattr(model.hparams ,      '''lr_scheduler'''					) == supported_param
											), f'''lr_scheduler={supported_param} shouldn\'t fail'''




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Optional[Any]							,			UpperCAmelCase_							:				int=0       ):
						# Format the message.
						if name is None:
												lowercase       :    List[str]											=  None
						else:
												lowercase       :    str											=  '''.''' * max(0							,			spaces - 2       ) + '''# {:''' + str(50 - spaces       ) + '''s}'''
												lowercase       :    List[str]											=  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_							:				Dict							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				Tuple       ):
						# 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.
						lowercase       :    Optional[Any]											=  param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						lowercase       :    Any											=  (num_heads, num_splits, hidden_size) + input_shape[1:]
						lowercase       :    Optional[int]											=  param.view(*UpperCAmelCase_       )
						lowercase       :    List[Any]											=  param.transpose(0							,			1       ).contiguous()
						lowercase       :    int											=  param.view(*UpperCAmelCase_       )
						return param

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    str											=  config.n_positions
						lowercase       :    Union[str, Any]											=  config.n_layer
						lowercase       :    Optional[int]											=  config.vocab_size
						lowercase       :    Union[str, Any]											=  config.n_head
						lowercase       :    int											=  config.n_embd // config.n_head

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

						lowercase       :    Dict											=  '''model.language_model.encoder.layers.'''
						for i in range(UpperCAmelCase_       ):
												lowercase       :    List[str]											=  torch.tril(torch.ones((n_positions, n_positions)							,			dtype=torch.bool       )       )
												lowercase       :    List[str]											=  causal_mask.view(1							,			1							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    List[str]											=  causal_mask
												lowercase       :    Optional[int]											=  torch.tensor(-1E4							,			dtype=torch.bfloataa       )

												lowercase       :    List[str]											=  sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

												lowercase       :    Any											=  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
												lowercase       :    Optional[int]											=  fix_query_key_value_ordering(UpperCAmelCase_							,			3							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    Tuple											=  vala.transpose(0							,			1       ).contiguous()

												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
												lowercase       :    Any											=  fix_query_key_value_ordering(UpperCAmelCase_							,			3							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    int											=  vala

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

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

						return sd_hf

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				str       ):
						lowercase       :    str											=  6_40_00
						lowercase       :    Union[str, Any]											=  config_megatron['''encoder_seq_length''']
						lowercase       :    Union[str, Any]											=  config_megatron['''hidden_size''']
						lowercase       :    Optional[Any]											=  config_megatron['''num_layers''']
						lowercase       :    int											=  config_megatron['''num_attention_heads''']
						lowercase       :    str											=  config_megatron['''ffn_hidden_size''']
						lowercase       :    Union[str, Any]											=  '''gelu'''
						lowercase       :    Tuple											=  0.1
						lowercase       :    str											=  0.1
						lowercase       :    str											=  0.1
						lowercase       :    Dict											=  config_megatron['''layernorm_epsilon''']  # 1e-5
						lowercase       :    str											=  config_megatron['''init_method_std''']  # 0.02
						lowercase       :    Optional[int]											=  config_megatron['''apply_query_key_layer_scaling''']  # True
						lowercase       :    Union[str, Any]											=  True
						lowercase       :    int											=  True

						# This identifies the 6.7B (7B) model which uses a different tokenizer
						if config_megatron["hidden_size"] == 40_96:
												lowercase       :    Dict											=  1  # <|endoftext|>
												lowercase       :    str											=  1  # <|endoftext|>
												lowercase       :    Dict											=  0  # <unk>
						else:
												lowercase       :    List[Any]											=  2  # <s>
												lowercase       :    List[str]											=  3  # <|endoftext|>
												lowercase       :    Optional[Any]											=  0  # <pad>

						return config_hf

def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple       ):
						print(UpperCAmelCase_       )

						lowercase       :    List[Any]											=  args.checkpoint_path
						lowercase       :    Dict											=  args.save_path
						if isfile(UpperCAmelCase_       ):
												raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''       )

						# Load the model.
						lowercase       :    Optional[int]											=  torch.load(UpperCAmelCase_							,			map_location='''cpu'''       )

						# Load the config.
						lowercase       :    Any											=  checkpoint['''hyper_parameters''']['''cfg''']
						lowercase       :    Dict											=  GPTaConfig()
						lowercase       :    Dict											=  copy_config(config_hf=UpperCAmelCase_							,			config_megatron=UpperCAmelCase_       )
						lowercase       :    Any											=  ['''GPT2LMHeadModel''']

						lowercase       :    List[Any]											=  checkpoint['''state_dict''']

						# Convert.
						print('''Converting'''       )
						lowercase       :    Dict											=  convert_megatron_checkpoint(UpperCAmelCase_							,			UpperCAmelCase_       )

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

						lowercase       :    List[Any]											=  '''GPTSw3Tokenizer'''

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

						# Store the state_dict to file.
						lowercase       :    Union[str, Any]											=  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)


