"""simple docstring"""


import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def        __a (		A     ,					A     ,					A=0   ):






     '''simple docstring'''



     if name is None:
          lowercase__								=				None
     else:
          lowercase__								=				"." * max(0     ,					spaces - 2   ) + "# {:" + str(50 - spaces   ) + "s}"
          lowercase__								=				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        __a (		A     ,					A     ,					A     ,					A   ):






     '''simple docstring'''



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







def        __a (		A     ,					A   ):






     '''simple docstring'''



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

     lowercase__								=				sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
     lowercase__								=				{
         "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__								=				"model.language_model.encoder.layers."
     for i in range(A   ):
          lowercase__								=				torch.tril(torch.ones((n_positions, n_positions)     ,					dtype=torch.bool   )   )
          lowercase__								=				causal_mask.view(1     ,					1     ,					A     ,					A   )
          lowercase__								=				causal_mask
          lowercase__								=				torch.tensor(-1e4     ,					dtype=torch.bfloataa   )

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

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

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

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

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

     return sd_hf







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				6_40_00
     lowercase__								=				config_megatron["encoder_seq_length"]
     lowercase__								=				config_megatron["hidden_size"]
     lowercase__								=				config_megatron["num_layers"]
     lowercase__								=				config_megatron["num_attention_heads"]
     lowercase__								=				config_megatron["ffn_hidden_size"]
     lowercase__								=				"gelu"
     lowercase__								=				0.1
     lowercase__								=				0.1
     lowercase__								=				0.1
     lowercase__								=				config_megatron["layernorm_epsilon"]  # 1e-5
     lowercase__								=				config_megatron["init_method_std"]  # 0.02
     lowercase__								=				config_megatron["apply_query_key_layer_scaling"]  # True
     lowercase__								=				True
     lowercase__								=				True

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

     return config_hf







def        __a (		A   ):






     '''simple docstring'''



     print(A   )

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

     # Load the model.
     lowercase__								=				torch.load(A     ,					map_location="cpu"   )

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

     lowercase__								=				checkpoint["state_dict"]

     # Convert.
     print("Converting"   )
     lowercase__								=				convert_megatron_checkpoint(A     ,					A   )

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

     lowercase__								=				"GPTSw3Tokenizer"

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

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


if __name__ == "__main__":
 lowerCAmelCase_:       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")
 lowerCAmelCase_:       Optional[Any]			     =       parser.parse_args()
 main(_args)





"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

     # Create a list of size equal to the maximum deadline
     # and initialize it with -1
     lowercase__								=				max(A     ,					key=lambda A   : value[1]   )[1]
     lowercase__								=				[-1] * max_deadline

     # Finding the maximum profit and the count of jobs
     lowercase__								=				0
     lowercase__								=				0
     for job in jobs:
          # Find a free time slot for this job
          # (Note that we start from the last possible slot)
          for i in range(job[1] - 1     ,					-1     ,					-1   ):
               if time_slots[i] == -1:
                    lowercase__								=				job[0]
                    count += 1
                    max_profit += job[2]
                    break
     return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




