



'''simple docstring'''






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[Any] ,       snake_case_    :     int ,       snake_case_    :     Dict=0  )		->Any:






							'''simple docstring'''



							if name is None:
														_lowercase							:		Any				=   None
							else:
														_lowercase							:		int				=   '''.''' * max(0 ,       spaces - 2  ) + '''# {:''' + str(50 - spaces  ) + '''s}'''
														_lowercase							:		List[Any]				=   fmt.format(a__  )

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





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Optional[int] ,       snake_case_    :     Tuple ,       snake_case_    :     List[Any] ,       snake_case_    :     List[str]  )		->str:






							'''simple docstring'''
							_lowercase							:		List[str]				=   param.size()
							# other versions store [num_heads * num_splits * hidden_size, :]
							_lowercase							:		Optional[int]				=   (num_heads, num_splits, hidden_size) + input_shape[1:]
							_lowercase							:		List[str]				=   param.view(*a__  )
							_lowercase							:		Dict				=   param.transpose(0 ,       1  ).contiguous()
							_lowercase							:		Union[str, Any]				=   param.view(*a__  )
							return param





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Tuple ,       snake_case_    :     Optional[int]  )		->List[str]:






							'''simple docstring'''
							_lowercase							:		str				=   config.n_positions
							_lowercase							:		List[Any]				=   config.n_layer
							_lowercase							:		str				=   config.vocab_size
							_lowercase							:		Union[str, Any]				=   config.n_head
							_lowercase							:		List[str]				=   config.n_embd // config.n_head

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

														_lowercase							:		Union[str, Any]				=   sd_megatron[F"{pf}{i}.input_layernorm.weight"]
														_lowercase							:		Any				=   sd_megatron[F"{pf}{i}.input_layernorm.bias"]

														_lowercase							:		Optional[int]				=   sd_megatron[F"{pf}{i}.self_attention.query_key_value.weight"]
														_lowercase							:		Optional[int]				=   fix_query_key_value_ordering(a__ ,       3 ,       a__ ,       a__  )
														_lowercase							:		Any				=   vala.transpose(0 ,       1  ).contiguous()

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

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

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

							return sd_hf





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Any ,       snake_case_    :     List[Any]  )		->List[Any]:






							'''simple docstring'''
							_lowercase							:		Optional[Any]				=   6_40_00
							_lowercase							:		int				=   config_megatron['''encoder_seq_length''']
							_lowercase							:		Union[str, Any]				=   config_megatron['''hidden_size''']
							_lowercase							:		List[str]				=   config_megatron['''num_layers''']
							_lowercase							:		Union[str, Any]				=   config_megatron['''num_attention_heads''']
							_lowercase							:		Optional[int]				=   config_megatron['''ffn_hidden_size''']
							_lowercase							:		Optional[Any]				=   '''gelu'''
							_lowercase							:		int				=   0.1
							_lowercase							:		Tuple				=   0.1
							_lowercase							:		List[str]				=   0.1
							_lowercase							:		int				=   config_megatron['''layernorm_epsilon''']  # 1e-5
							_lowercase							:		Tuple				=   config_megatron['''init_method_std''']  # 0.02
							_lowercase							:		int				=   config_megatron['''apply_query_key_layer_scaling''']  # True
							_lowercase							:		str				=   True
							_lowercase							:		int				=   True

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

							return config_hf





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict  )		->Union[str, Any]:






							'''simple docstring'''
							print(a__  )

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

							# Load the model.
							_lowercase							:		int				=   torch.load(a__ ,       map_location='''cpu'''  )

							# Load the config.
							_lowercase							:		int				=   checkpoint['''hyper_parameters''']['''cfg''']
							_lowercase							:		str				=   GPTaConfig()
							_lowercase							:		str				=   copy_config(config_hf=a__ ,       config_megatron=a__  )
							_lowercase							:		Optional[Any]				=   ['''GPT2LMHeadModel''']

							_lowercase							:		Union[str, Any]				=   checkpoint['''state_dict''']

							# Convert.
							print('''Converting'''  )
							_lowercase							:		str				=   convert_megatron_checkpoint(a__ ,       a__  )

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

							_lowercase							:		Optional[int]				=   '''GPTSw3Tokenizer'''

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

							# Store the state_dict to file.
							_lowercase							:		Optional[int]				=   os.path.join(a__ ,       '''pytorch_model.bin'''  )
							print(F"Saving checkpoint to \"{output_checkpoint_file}\""  )
							torch.save(a__ ,       a__  )


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






"""simple docstring"""

import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


a__			:						Union[str, Any]							=			torch.cuda.is_available()
a__			:						Union[str, Any]							=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__			:						List[Any]							=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__			:						str							=			{
    """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 (     a__:			Path			,						a__:			list      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      '''\n'''.join(a__      )
					Path(a__      ).open('''w'''      ).writelines(a__      )





def 					__UpperCamelCase (     a__:			int      )					->      List[str]:




					"""simple docstring"""



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


class       _snake_case  (    UpperCamelCase							):







						@classmethod
						def 							_UpperCamelCase     (cls				)      ->    List[str]:
											return cls







						@require_torch_multi_gpu
						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      {
											    '''no_teacher''': True,
											    '''freeze_encoder''': True,
											    '''gpus''': 2,
											    '''overwrite_output_dir''': True,
											    '''sortish_sampler''': True,
											}
											self._test_distiller_cli_fork(UpperCAmelCase_  ,				check_contents=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=True				)      ->    Tuple:
											__lowercase        =      {
											    '''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(UpperCAmelCase_				)
											__lowercase        =      CHEAP_ARGS.copy()
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase        =      self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				**UpperCAmelCase_				)

											def convert(UpperCAmelCase_  ,				UpperCAmelCase_				):
																if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																					return ""
																if v is False or v is None:
																					return ""
																if v is True:  # or len(str(v))==0:
																					return f'''--{k}'''
																return f'''--{k}={v}'''

											__lowercase        =      [x for x in (convert(UpperCAmelCase_  ,				UpperCAmelCase_				) for k, v in args_d.items()) if len(UpperCAmelCase_				)]
											__lowercase        =      [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
											execute_subprocess_async(UpperCAmelCase_  ,				env=self.get_env()				)

											__lowercase        =      os.listdir(UpperCAmelCase_				)
											__lowercase        =      {os.path.basename(UpperCAmelCase_				) for p in contents}
											__lowercase        =      [p for p in contents if p.endswith('''ckpt'''				)]
											assert len(UpperCAmelCase_				) > 0

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

											# get the following from the module, (we don't have access to `model` here)
											__lowercase        =      os.path.join(UpperCAmelCase_  ,				'''metrics.json'''				)
											__lowercase        =      '''rouge2'''

											__lowercase        =      load_json(UpperCAmelCase_				)
											# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
											print(UpperCAmelCase_				)
											__lowercase        =      metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']  ,				0.0_1				)
											self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']  ,				UpperCAmelCase_				)
											self.assertEqual(len(metrics['''test''']				)  ,				1				)
											__lowercase        =      int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1				)
											self.assertEqual(len(metrics['''val''']				)  ,				UpperCAmelCase_				)


