



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)

A_		:  int										=  logging.getLogger()
A_		:  Tuple										=  torch.cuda.is_available()
A_		:  Dict										=  {
    '''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-08,
    '''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 						UpperCAmelCase__   (   UpperCAmelCase__			:Path			,       UpperCAmelCase__			:list       ):






 '''simple docstring'''

 a							=		"\n".join(UpperCAmelCase__       )
 Path(UpperCAmelCase__       ).open("w"       ).writelines(UpperCAmelCase__       )


A_		:  Union[str, Any]										=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
A_		:  Any										=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
A_		:  List[Any]										=  '''patrickvonplaten/t5-tiny-random'''
A_		:  Any										=  '''sshleifer/t5-tinier-random'''
A_		:  Tuple										=  '''sshleifer/bart-tiny-random'''
A_		:  Dict										=  '''sshleifer/tiny-mbart'''
A_		:  Tuple										=  '''sshleifer/tiny-marian-en-de'''
A_		:  Optional[Any]										=  '''stas/tiny-wmt19-en-de'''


A_		:  Union[str, Any]										=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[Any]       ):






 '''simple docstring'''

 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 		_lowercase		(							UpperCAmelCase__						):

   @classmethod
   def 		A				(   cls      :							List[str]				)							->					List[Any]:


    """simple docstring"""






    logging.disable(logging.CRITICAL				)  # remove noisy download output from tracebacks
    return cls

   @slow
   @require_torch_gpu
   def 		A				(   self      :							List[str]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		list_models()
    a							=		"sshleifer"
    a							=		[x.modelId for x in model_list if x.modelId.startswith(__lowerCAmelCase				)]
    a							=		["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
    a							=		[]
    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 		A				(   self      :							List[str]				)							->					int:


    """simple docstring"""






    a							=		{"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
    self._test_distiller_cli(__lowerCAmelCase				)

   def 		A				(   self      :							Union[str, Any]				)							->					Dict:


    """simple docstring"""






    a							=		{
        "student_encoder_layers": 2,
        "student_decoder_layers": 1,
        "max_epochs": 4,
        "val_check_interval": 0.2_5,
        "alpha_hid": 2.0,
        "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
    }
    a							=		self._test_distiller_cli(__lowerCAmelCase				, check_contents=__lowerCAmelCase				)

    a							=		list(Path(model.output_dir				).glob("*.ckpt"				)				)
    self.assertEqual(1				, len(__lowerCAmelCase				)				)
    a							=		list(Path(model.output_dir				).glob("**/*.bin"				)				)
    self.assertEqual(len(__lowerCAmelCase				)				, 2				)
    a							=		lmap(str.strip				, Path(model.hparams.data_dir				).joinpath("test.source"				).open().readlines()				)
    a							=		tempfile.mktemp()  # XXX: not being cleaned up
    generate_summaries_or_translations(__lowerCAmelCase				, __lowerCAmelCase				, str(model.output_dir / "best_tfmr"				)				)
    self.assertTrue(Path(__lowerCAmelCase				).exists()				)

    a							=		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 		A				(   self      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    a							=		AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase				)
    a		,	a							=		model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
    a							=		torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]				, dtype=torch.long				, device=model.device				)
    a							=		target_ids[:, :-1].contiguous()  # Why this line?
    a							=		target_ids[:, 1:].clone()  # why clone?
    a							=		model(
        __lowerCAmelCase				, attention_mask=__lowerCAmelCase				, decoder_input_ids=__lowerCAmelCase				, labels=__lowerCAmelCase				, use_cache=__lowerCAmelCase				).loss

    a							=		model(__lowerCAmelCase				, attention_mask=__lowerCAmelCase				, decoder_input_ids=__lowerCAmelCase				, use_cache=__lowerCAmelCase				).logits

    a							=		nn.functional.log_softmax(__lowerCAmelCase				, dim=-1				)
    a		,	a							=		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 		A				(   self      :							int				)							->					Optional[int]:


    """simple docstring"""






    a							=		{
        "student_encoder_layers": 2,
        "student_decoder_layers": 1,
        "num_train_epochs": 4,
        "val_check_interval": 0.2_5,
        "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",
    }
    a							=		self._test_distiller_cli(__lowerCAmelCase				, check_contents=__lowerCAmelCase				)
    assert model.model.config.model_type == "mbart"

    a							=		list(Path(model.output_dir				).glob("*.ckpt"				)				)
    self.assertEqual(1				, len(__lowerCAmelCase				)				)
    a							=		list(Path(model.output_dir				).glob("**/*.bin"				)				)
    a							=		list(Path(model.output_dir				).glob("best_tfmr/*"				)				)
    assert len(__lowerCAmelCase				) > 2
    self.assertEqual(len(__lowerCAmelCase				)				, 2				)

   def 		A				(   self      :							Optional[Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		{
        "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 		A				(   self      :							List[str]				)							->					str:


    """simple docstring"""






    a							=		{
        "teacher": T5_TINY,
        "student": T5_TINIER,
        "model_name_or_path": T5_TINIER,
        "tokenizer_name": T5_TINIER,
    }
    self._test_distiller_cli(__lowerCAmelCase				)




   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Any=True				)							->					str:


    """simple docstring"""






    a							=		{
        "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				)
    a							=		CHEAP_ARGS.copy()
    a							=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
    a							=		self.get_auto_remove_tmp_dir()

    args_d.update(data_dir=__lowerCAmelCase				, output_dir=__lowerCAmelCase				, **__lowerCAmelCase				)
    a							=		distill_main(argparse.Namespace(**__lowerCAmelCase				)				)
    if not check_contents:
     return model
    a							=		os.listdir(__lowerCAmelCase				)
    a							=		{os.path.basename(__lowerCAmelCase				) for p in contents}
    a							=		[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				)

    a							=		load_json(model.metrics_save_path				)
    a							=		metrics["val"][-1]
    self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]				, 0.0_1				)
    self.assertGreaterEqual(1.0				, last_step_stats["val_avg_gen_time"]				)
    self.assertIsInstance(last_step_stats[f"""val_avg_{model.val_metric}"""]				, __lowerCAmelCase				)
    a							=		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 		_lowercase		(							UpperCAmelCase__						):

   @parameterized.expand(
       [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]				, )
   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							int				)							->					Any:


    """simple docstring"""






    a							=		CHEAP_ARGS.copy()
    a							=		"translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
    a							=		0.1 if task == "translation" else 0

    a							=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
    a							=		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
    a							=		argparse.Namespace(**__lowerCAmelCase				)
    a							=		main(__lowerCAmelCase				)

    a							=		module.model.get_input_embeddings()
    assert not input_embeds.weight.requires_grad
    if model == T5_TINY:
     a							=		module.model.lm_head
     assert not lm_head.weight.requires_grad
     assert (lm_head.weight == input_embeds.weight).all().item()
    elif model == FSMT_TINY:
     a							=		module.model.model
     a							=		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:
     a							=		module.model.model
     a							=		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

    a							=		load_json(module.output_dir / "text_batch.json"				)
    assert isinstance(__lowerCAmelCase				, __lowerCAmelCase				)
    assert len(__lowerCAmelCase				) >= 4

   def 		A				(   self      :							Union[str, Any]				)							->					Tuple:


    """simple docstring"""






    a							=		CHEAP_ARGS.copy()

    a							=		"summarization"
    a							=		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
    a							=		BART_TINY
    a							=		self.get_auto_remove_tmp_dir()
    a							=		args_d.copy()
    args_da.update(
        model_name_or_path=__lowerCAmelCase				, output_dir=__lowerCAmelCase				, )
    a							=		("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
    for p in extra_model_params:
     a							=		0.5
    a							=		argparse.Namespace(**__lowerCAmelCase				)
    a							=		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
    a							=		T5_TINY
    a							=		self.get_auto_remove_tmp_dir()
    a							=		args_d.copy()
    args_da.update(
        model_name_or_path=__lowerCAmelCase				, output_dir=__lowerCAmelCase				, )
    a							=		"encoder_layerdrop"
    a							=		0.5
    a							=		argparse.Namespace(**__lowerCAmelCase				)
    with pytest.raises(__lowerCAmelCase				) as excinfo:
     a							=		main(__lowerCAmelCase				)
    assert str(excinfo.value				) == f"""model config doesn't have a `{unsupported_param}` attribute"""




   def 		A				(   self      :							Union[str, Any]				)							->					Tuple:


    """simple docstring"""






    a							=		CHEAP_ARGS.copy()

    a							=		"summarization"
    a							=		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)

    a							=		BART_TINY
    a							=		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
    a							=		argparse.ArgumentParser()
    a							=		pl.Trainer.add_argparse_args(__lowerCAmelCase				)
    a							=		SummarizationModule.add_model_specific_args(__lowerCAmelCase				, os.getcwd()				)
    a							=		{"--help": True}

    # --help test
    with pytest.raises(__lowerCAmelCase				) as excinfo:
     with CaptureStdout() as cs:
      a							=		parser.parse_args(__lowerCAmelCase				)
     assert False, "--help is expected to sys.exit"
    assert excinfo.type == SystemExit
    a							=		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
    a							=		"non_existing_scheduler"
    a							=		{f"""--lr_scheduler={unsupported_param}"""}
    with pytest.raises(__lowerCAmelCase				) as excinfo:
     with CaptureStderr() as cs:
      a							=		parser.parse_args(__lowerCAmelCase				)
     assert False, "invalid argument is expected to sys.exit"
    assert excinfo.type == SystemExit
    a							=		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
    a							=		"cosine"
    a							=		args_d.copy()
    a							=		supported_param
    a							=		argparse.Namespace(**__lowerCAmelCase				)
    a							=		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       (				snake_case__   :      List[str]				,					snake_case__   :      List[str]				,					snake_case__   :      Optional[int]=0 )->			Union[str, Any]:
    # Format the message.
    if name is None:
        A_								=					None
    else:
        A_								=					"." * max(0				,					spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
        A_								=					fmt.format(snake_case__ )

    # Print and recurse (if needed).
    if isinstance(snake_case__				,					snake_case__ ):
        if msg is not None:
            print(snake_case__ )
        for k in val.keys():
            recursive_print(snake_case__				,					val[k]				,					spaces + 2 )
    elif isinstance(snake_case__				,					torch.Tensor ):
        print(snake_case__				,					":"				,					val.size() )
    else:
        print(snake_case__				,					":"				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      List[Any]				,					snake_case__   :      str				,					snake_case__   :      str )->			Union[str, Any]:
    # 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_								=					param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A_								=					(num_heads, num_splits, hidden_size) + input_shape[1:]
    A_								=					param.view(*snake_case__ )
    A_								=					param.transpose(0				,					1 ).contiguous()
    A_								=					param.view(*snake_case__ )
    return param





def        lowerCAmelCase       (				snake_case__   :      Dict				,					snake_case__   :      str )->			Optional[int]:
    A_								=					config.n_positions
    A_								=					config.n_layer
    A_								=					config.vocab_size
    A_								=					config.n_head
    A_								=					config.n_embd // config.n_head

    A_								=					sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
    A_								=					{
        "transformer.wte.weight": word_embeddings,
        "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
        "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
        "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
    }

    A_								=					"model.language_model.encoder.layers."
    for i in range(snake_case__ ):
        A_								=					torch.tril(torch.ones((n_positions, n_positions)				,					dtype=torch.bool ) )
        A_								=					causal_mask.view(1				,					1				,					snake_case__				,					snake_case__ )
        A_								=					causal_mask
        A_								=					torch.tensor(-1e4				,					dtype=torch.bfloataa )

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

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

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

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

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

    return sd_hf





def        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      List[Any] )->			Dict:
    A_								=					64000
    A_								=					config_megatron["encoder_seq_length"]
    A_								=					config_megatron["hidden_size"]
    A_								=					config_megatron["num_layers"]
    A_								=					config_megatron["num_attention_heads"]
    A_								=					config_megatron["ffn_hidden_size"]
    A_								=					"gelu"
    A_								=					0.1
    A_								=					0.1
    A_								=					0.1
    A_								=					config_megatron["layernorm_epsilon"]  # 1e-5
    A_								=					config_megatron["init_method_std"]  # 0.02
    A_								=					config_megatron["apply_query_key_layer_scaling"]  # True
    A_								=					True
    A_								=					True

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

    return config_hf





def        lowerCAmelCase       (				snake_case__   :      Tuple )->			Dict:
    print(snake_case__ )

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

    # Load the model.
    A_								=					torch.load(snake_case__				,					map_location="cpu" )

    # Load the config.
    A_								=					checkpoint["hyper_parameters"]["cfg"]
    A_								=					GPTaConfig()
    A_								=					copy_config(config_hf=snake_case__				,					config_megatron=snake_case__ )
    A_								=					["GPT2LMHeadModel"]

    A_								=					checkpoint["state_dict"]

    # Convert.
    print("Converting" )
    A_								=					convert_megatron_checkpoint(snake_case__				,					snake_case__ )

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

    A_								=					"GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
     __magic_name__  :  Union[str, 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')
     __magic_name__  :  str     			=   parser.parse_args()
     main(_args)






