
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			,   snake_case				: Any=0			)   ->     List[str]:



						'''simple docstring'''

						if name is None:
												__UpperCamelCase  	=      None
						else:
												__UpperCamelCase  	=      '''.''' * max(0			,   spaces - 2			) + '''# {:''' + str(50 - spaces			) + '''s}'''
												__UpperCamelCase  	=      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 						A_			(				snake_case				: Optional[int]			,   snake_case				: List[Any]			,   snake_case				: Dict			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						__UpperCamelCase  	=      (num_heads, num_splits, hidden_size) + input_shape[1:]
						__UpperCamelCase  	=      param.view(*snake_case			)
						__UpperCamelCase  	=      param.transpose(0			,   1			).contiguous()
						__UpperCamelCase  	=      param.view(*snake_case			)
						return param




def 						A_			(				snake_case				: str			,   snake_case				: str			)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      config.n_positions
						__UpperCamelCase  	=      config.n_layer
						__UpperCamelCase  	=      config.vocab_size
						__UpperCamelCase  	=      config.n_head
						__UpperCamelCase  	=      config.n_embd // config.n_head

						__UpperCamelCase  	=      sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
						__UpperCamelCase  	=      {
						    '''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'''],
						}

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

												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.input_layernorm.weight"]
												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.input_layernorm.bias"]

												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.weight"]
												__UpperCamelCase  	=      fix_query_key_value_ordering(snake_case			,   3			,   snake_case			,   snake_case			)
												__UpperCamelCase  	=      vala.transpose(0			,   1			).contiguous()

												__UpperCamelCase  	=      sd_megatron[f"{pf}{i}.self_attention.query_key_value.bias"]
												__UpperCamelCase  	=      fix_query_key_value_ordering(snake_case			,   3			,   snake_case			,   snake_case			)
												__UpperCamelCase  	=      vala

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

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

						return sd_hf




def 						A_			(				snake_case				: Tuple			,   snake_case				: str			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      64000
						__UpperCamelCase  	=      config_megatron['''encoder_seq_length''']
						__UpperCamelCase  	=      config_megatron['''hidden_size''']
						__UpperCamelCase  	=      config_megatron['''num_layers''']
						__UpperCamelCase  	=      config_megatron['''num_attention_heads''']
						__UpperCamelCase  	=      config_megatron['''ffn_hidden_size''']
						__UpperCamelCase  	=      '''gelu'''
						__UpperCamelCase  	=      0.1
						__UpperCamelCase  	=      0.1
						__UpperCamelCase  	=      0.1
						__UpperCamelCase  	=      config_megatron['''layernorm_epsilon''']  # 1e-5
						__UpperCamelCase  	=      config_megatron['''init_method_std''']  # 0.02
						__UpperCamelCase  	=      config_megatron['''apply_query_key_layer_scaling''']  # True
						__UpperCamelCase  	=      True
						__UpperCamelCase  	=      True

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

						return config_hf




def 						A_			(				snake_case				: str			)   ->     Any:



						'''simple docstring'''

						print(snake_case			)

						__UpperCamelCase  	=      args.checkpoint_path
						__UpperCamelCase  	=      args.save_path
						if isfile(snake_case			):
												raise FileNotFoundError(f"ERROR! could not find file {checkpoint_path}"			)

						# Load the model.
						__UpperCamelCase  	=      torch.load(snake_case			,   map_location='''cpu'''			)

						# Load the config.
						__UpperCamelCase  	=      checkpoint['''hyper_parameters''']['''cfg''']
						__UpperCamelCase  	=      GPTaConfig()
						__UpperCamelCase  	=      copy_config(config_hf=snake_case			,   config_megatron=snake_case			)
						__UpperCamelCase  	=      ['''GPT2LMHeadModel''']

						__UpperCamelCase  	=      checkpoint['''state_dict''']

						# Convert.
						print('''Converting'''			)
						__UpperCamelCase  	=      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			)

						__UpperCamelCase  	=      '''GPTSw3Tokenizer'''

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

						# Store the state_dict to file.
						__UpperCamelCase  	=      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__":
				lowercase__				:				Optional[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")
				lowercase__				:				List[Any]						=      parser.parse_args()
				main(_args)








"""simple docstring"""


import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


a_											=     2 << 20  # 2mb
a_											=     get_logger(__name__)
class snake_case   (	_UpperCamelCase):







		def 			a_			(       self   :							Tuple		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      self._options.get_all_options().get("runner"		)
						return runner in [None, "DirectRunner", "PortableRunner"]






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Any:
				_A       	=      FileSystems
				if fs.exists(__lowercase ):
								if force_upload:
												logger.info(f"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.""" )
								else:
												logger.info(f"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.""" )
												return
				with fs.create(__lowercase ) as remote_file:
								with open(__lowercase  ,      "rb" ) as local_file:
												_A       	=      local_file.read(__lowercase )
												while chunk:
																remote_file.write(__lowercase )
																_A       	=      local_file.read(__lowercase )






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase=False )						->						Union[str, Any]:
				_A       	=      FileSystems
				if os.path.exists(__lowercase ):
								if force_download:
												logger.info(f"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.""" )
								else:
												logger.info(f"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False.""" )
												return
				with fs.open(__lowercase ) as remote_file:
								with open(__lowercase  ,      "wb" ) as local_file:
												_A       	=      remote_file.read(__lowercase )
												while chunk:
																local_file.write(__lowercase )
																_A       	=      remote_file.read(__lowercase )