


'''simple docstring'''




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 				a_							(    lowercase				,    lowercase				,    lowercase=0     )      ->List[Any]:






				'''simple docstring'''

				if name is None:
								__lowerCamelCase		:   Any           =       None
				else:
								__lowerCamelCase		:   Optional[int]           =       "." * max(0				,    spaces - 2     ) + "# {:" + str(50 - spaces     ) + "s}"
								__lowerCamelCase		:   Optional[int]           =       fmt.format(SCREAMING_SNAKE_CASE__     )

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





def 				a_							(    lowercase				,    lowercase				,    lowercase				,    lowercase     )      ->Tuple:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       param.size()
				# other versions store [num_heads * num_splits * hidden_size, :]
				__lowerCamelCase		:   Union[str, Any]           =       (num_heads, num_splits, hidden_size) + input_shape[1:]
				__lowerCamelCase		:   str           =       param.view(*SCREAMING_SNAKE_CASE__     )
				__lowerCamelCase		:   List[str]           =       param.transpose(0				,    1     ).contiguous()
				__lowerCamelCase		:   int           =       param.view(*SCREAMING_SNAKE_CASE__     )
				return param





def 				a_							(    lowercase				,    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Union[str, Any]           =       config.n_positions
				__lowerCamelCase		:   List[str]           =       config.n_layer
				__lowerCamelCase		:   Union[str, Any]           =       config.vocab_size
				__lowerCamelCase		:   Any           =       config.n_head
				__lowerCamelCase		:   List[Any]           =       config.n_embd // config.n_head

				__lowerCamelCase		:   Union[str, Any]           =       sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
				__lowerCamelCase		:   List[str]           =       {
				    "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"],
				}

				__lowerCamelCase		:   str           =       "model.language_model.encoder.layers."
				for i in range(SCREAMING_SNAKE_CASE__     ):
								__lowerCamelCase		:   Tuple           =       torch.tril(torch.ones((n_positions, n_positions)				,    dtype=torch.bool     )     )
								__lowerCamelCase		:   str           =       causal_mask.view(1				,    1				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__     )
								__lowerCamelCase		:   Tuple           =       causal_mask
								__lowerCamelCase		:   Any           =       torch.tensor(-1E4				,    dtype=torch.bfloataa     )

								__lowerCamelCase		:   Any           =       sd_megatron[F'{pf}{i}.input_layernorm.weight']
								__lowerCamelCase		:   Optional[int]           =       sd_megatron[F'{pf}{i}.input_layernorm.bias']

								__lowerCamelCase		:   List[str]           =       sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
								__lowerCamelCase		:   Any           =       fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__				,    3				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__     )
								__lowerCamelCase		:   int           =       vala.transpose(0				,    1     ).contiguous()

								__lowerCamelCase		:   List[Any]           =       sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
								__lowerCamelCase		:   Optional[Any]           =       fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__				,    3				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__     )
								__lowerCamelCase		:   str           =       vala

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

				# For LM head, transformers' wants the matrix to weight embeddings.
				__lowerCamelCase		:   Optional[int]           =       word_embeddings

				return sd_hf





def 				a_							(    lowercase				,    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   Union[str, Any]           =       6_4000
				__lowerCamelCase		:   Tuple           =       config_megatron["encoder_seq_length"]
				__lowerCamelCase		:   Optional[int]           =       config_megatron["hidden_size"]
				__lowerCamelCase		:   Optional[Any]           =       config_megatron["num_layers"]
				__lowerCamelCase		:   Union[str, Any]           =       config_megatron["num_attention_heads"]
				__lowerCamelCase		:   Dict           =       config_megatron["ffn_hidden_size"]
				__lowerCamelCase		:   List[str]           =       "gelu"
				__lowerCamelCase		:   int           =       0.1
				__lowerCamelCase		:   Dict           =       0.1
				__lowerCamelCase		:   List[Any]           =       0.1
				__lowerCamelCase		:   Union[str, Any]           =       config_megatron["layernorm_epsilon"]  # 1e-5
				__lowerCamelCase		:   str           =       config_megatron["init_method_std"]  # 0.02
				__lowerCamelCase		:   Dict           =       config_megatron["apply_query_key_layer_scaling"]  # True
				__lowerCamelCase		:   str           =       True
				__lowerCamelCase		:   List[str]           =       True

				# This identifies the 6.7B (7B) model which uses a different tokenizer
				if config_megatron["hidden_size"] == 4096:
								__lowerCamelCase		:   Any           =       1  # <|endoftext|>
								__lowerCamelCase		:   List[Any]           =       1  # <|endoftext|>
								__lowerCamelCase		:   Optional[int]           =       0  # <unk>
				else:
								__lowerCamelCase		:   Dict           =       2  # <s>
								__lowerCamelCase		:   Optional[Any]           =       3  # <|endoftext|>
								__lowerCamelCase		:   Union[str, Any]           =       0  # <pad>

				return config_hf





def 				a_							(    lowercase     )      ->Optional[int]:






				'''simple docstring'''

				print(SCREAMING_SNAKE_CASE__     )

				__lowerCamelCase		:   str           =       args.checkpoint_path
				__lowerCamelCase		:   List[str]           =       args.save_path
				if isfile(SCREAMING_SNAKE_CASE__     ):
								raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'     )

				# Load the model.
				__lowerCamelCase		:   List[Any]           =       torch.load(SCREAMING_SNAKE_CASE__				,    map_location="cpu"     )

				# Load the config.
				__lowerCamelCase		:   int           =       checkpoint["hyper_parameters"]["cfg"]
				__lowerCamelCase		:   List[str]           =       GPTaConfig()
				__lowerCamelCase		:   List[Any]           =       copy_config(config_hf=SCREAMING_SNAKE_CASE__				,    config_megatron=SCREAMING_SNAKE_CASE__     )
				__lowerCamelCase		:   Dict           =       ["GPT2LMHeadModel"]

				__lowerCamelCase		:   List[str]           =       checkpoint["state_dict"]

				# Convert.
				print("Converting"     )
				__lowerCamelCase		:   Optional[Any]           =       convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__     )

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

				__lowerCamelCase		:   int           =       "GPTSw3Tokenizer"

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

				# Store the state_dict to file.
				__lowerCamelCase		:   str           =       os.path.join(SCREAMING_SNAKE_CASE__				,    "pytorch_model.bin"     )
				print(F'Saving checkpoint to "{output_checkpoint_file}"'     )
				torch.save(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
				__lowercase	:   List[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	:   Any   		=					parser.parse_args()
				main(_args)









_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





