'''simple docstring'''
import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 			_UpperCamelCase			(  _a						:						List[str]					,  _a						:						Tuple					,  _a						:						List[Any]=0      ):




    """simple docstring"""



    if name is None:
        __UpperCamelCase						:						Optional[Any]		     =	None
    else:
        __UpperCamelCase						:						Any		     =	'.' * max(0					,  spaces - 2      ) + '# {:' + str(5_0 - spaces      ) + 's}'
        __UpperCamelCase						:						Optional[int]		     =	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 			_UpperCamelCase			(  _a						:						Tuple					,  _a						:						Tuple					,  _a						:						Any					,  _a						:						int      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						str					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						List[Any]		     =	config.n_positions
    __UpperCamelCase						:						List[Any]		     =	config.n_layer
    __UpperCamelCase						:						List[Any]		     =	config.vocab_size
    __UpperCamelCase						:						str		     =	config.n_head
    __UpperCamelCase						:						List[Any]		     =	config.n_embd // config.n_head

    __UpperCamelCase						:						Union[str, Any]		     =	sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    __UpperCamelCase						:						List[Any]		     =	{
        '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						:						str		     =	'model.language_model.encoder.layers.'
    for i in range(_a      ):
        __UpperCamelCase						:						str		     =	torch.tril(torch.ones((n_positions, n_positions)					,  dtype=torch.bool      )      )
        __UpperCamelCase						:						List[Any]		     =	causal_mask.view(1					,  1					,  _a					,  _a      )
        __UpperCamelCase						:						Optional[Any]		     =	causal_mask
        __UpperCamelCase						:						Union[str, Any]		     =	torch.tensor(-1E4					,  dtype=torch.bfloataa      )

        __UpperCamelCase						:						Union[str, Any]		     =	sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
        __UpperCamelCase						:						List[Any]		     =	sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

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

        __UpperCamelCase						:						str		     =	sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
        __UpperCamelCase						:						Optional[int]		     =	fix_query_key_value_ordering(_a					,  3					,  _a					,  _a      )
        __UpperCamelCase						:						Union[str, Any]		     =	vala

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

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

    return sd_hf







def 			_UpperCamelCase			(  _a						:						Optional[Any]					,  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[Any]		     =	6_4_0_0_0
    __UpperCamelCase						:						List[str]		     =	config_megatron['encoder_seq_length']
    __UpperCamelCase						:						Any		     =	config_megatron['hidden_size']
    __UpperCamelCase						:						Optional[int]		     =	config_megatron['num_layers']
    __UpperCamelCase						:						str		     =	config_megatron['num_attention_heads']
    __UpperCamelCase						:						Optional[Any]		     =	config_megatron['ffn_hidden_size']
    __UpperCamelCase						:						List[str]		     =	'gelu'
    __UpperCamelCase						:						List[str]		     =	0.1
    __UpperCamelCase						:						str		     =	0.1
    __UpperCamelCase						:						Optional[Any]		     =	0.1
    __UpperCamelCase						:						Any		     =	config_megatron['layernorm_epsilon']  # 1e-5
    __UpperCamelCase						:						Any		     =	config_megatron['init_method_std']  # 0.02
    __UpperCamelCase						:						Tuple		     =	config_megatron['apply_query_key_layer_scaling']  # True
    __UpperCamelCase						:						str		     =	True
    __UpperCamelCase						:						Optional[Any]		     =	True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_0_9_6:
        __UpperCamelCase						:						Optional[int]		     =	1  # <|endoftext|>
        __UpperCamelCase						:						Any		     =	1  # <|endoftext|>
        __UpperCamelCase						:						str		     =	0  # <unk>
    else:
        __UpperCamelCase						:						Union[str, Any]		     =	2  # <s>
        __UpperCamelCase						:						Tuple		     =	3  # <|endoftext|>
        __UpperCamelCase						:						Dict		     =	0  # <pad>

    return config_hf







def 			_UpperCamelCase			(  _a						:						Tuple      ):




    """simple docstring"""



    print(_a      )

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

    # Load the model.
    __UpperCamelCase						:						Any		     =	torch.load(_a					,  map_location='cpu'      )

    # Load the config.
    __UpperCamelCase						:						Optional[Any]		     =	checkpoint['hyper_parameters']['cfg']
    __UpperCamelCase						:						List[Any]		     =	GPTaConfig()
    __UpperCamelCase						:						Union[str, Any]		     =	copy_config(config_hf=_a					,  config_megatron=_a      )
    __UpperCamelCase						:						Tuple		     =	['GPT2LMHeadModel']

    __UpperCamelCase						:						Tuple		     =	checkpoint['state_dict']

    # Convert.
    print('Converting'      )
    __UpperCamelCase						:						Tuple		     =	convert_megatron_checkpoint(_a					,  _a      )

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

    __UpperCamelCase						:						Dict		     =	'GPTSw3Tokenizer'

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

    # Store the state_dict to file.
    __UpperCamelCase						:						int		     =	os.path.join(_a					,  'pytorch_model.bin'      )
    print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""      )
    torch.save(_a					,  _a      )


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




'''simple docstring'''
import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.







def 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						Dict					,  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	states.reshape(1					,  -1					,  model.config.state_dim      )
    __UpperCamelCase						:						Tuple		     =	actions.reshape(1					,  -1					,  model.config.act_dim      )
    __UpperCamelCase						:						Optional[Any]		     =	returns_to_go.reshape(1					,  -1					,  1      )
    __UpperCamelCase						:						Optional[int]		     =	timesteps.reshape(1					,  -1      )

    if model.config.max_length is not None:
        __UpperCamelCase						:						Any		     =	states[:, -model.config.max_length :]
        __UpperCamelCase						:						int		     =	actions[:, -model.config.max_length :]
        __UpperCamelCase						:						Any		     =	returns_to_go[:, -model.config.max_length :]
        __UpperCamelCase						:						Tuple		     =	timesteps[:, -model.config.max_length :]

        # pad all tokens to sequence length
        __UpperCamelCase						:						str		     =	torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
        __UpperCamelCase						:						int		     =	attention_mask.to(dtype=torch.long					,  device=states.device      ).reshape(1					,  -1      )
        __UpperCamelCase						:						Any		     =	torch.cat(
            [
                torch.zeros(
                    (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)					,  device=states.device					,  ),
                states,
            ]					,  dim=1					,  ).to(dtype=torch.floataa      )
        __UpperCamelCase						:						Tuple		     =	torch.cat(
            [
                torch.zeros(
                    (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)					,  device=actions.device					,  ),
                actions,
            ]					,  dim=1					,  ).to(dtype=torch.floataa      )
        __UpperCamelCase						:						Optional[Any]		     =	torch.cat(
            [
                torch.zeros(
                    (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)					,  device=returns_to_go.device					,  ),
                returns_to_go,
            ]					,  dim=1					,  ).to(dtype=torch.floataa      )
        __UpperCamelCase						:						Tuple		     =	torch.cat(
            [
                torch.zeros(
                    (timesteps.shape[0], model.config.max_length - timesteps.shape[1])					,  device=timesteps.device      ),
                timesteps,
            ]					,  dim=1					,  ).to(dtype=torch.long      )
    else:
        __UpperCamelCase						:						List[str]		     =	None

    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	model(
        states=_a					,  actions=_a					,  rewards=_a					,  returns_to_go=_a					,  timesteps=_a					,  attention_mask=_a					,  return_dict=_a					,  )

    return action_preds[0, -1]


# build the environment

a=	gym.make('''Hopper-v3''')
a=	env.observation_space.shape[0]
a=	env.action_space.shape[0]
a=	1_0_0_0
a=	'''cuda'''
a=	1_000.0  # normalization for rewards/returns
a=	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a=	np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
a=	np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
a=	torch.from_numpy(state_mean).to(device=device)
a=	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
a=	DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
a=	model.to(device)
model.eval()

for ep in range(1_0):
  a,      a=	0, 0
  a=	env.reset()
  a=	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  a=	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  a=	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  a=	torch.zeros(0, device=device, dtype=torch.floataa)

  a=	torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
  for t in range(max_ep_len):
    env.render()
    # add padding
    a=	torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    a=	torch.cat([rewards, torch.zeros(1, device=device)])

    a=	get_action(
        model,
        (states.to(dtype=torch.floataa) - state_mean) / state_std,
        actions.to(dtype=torch.floataa),
        rewards.to(dtype=torch.floataa),
        target_return.to(dtype=torch.floataa),
        timesteps.to(dtype=torch.long),
    )
    a=	action
    a=	action.detach().cpu().numpy()

    a,      a,      a,      a=	env.step(action)

    a=	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    a=	torch.cat([states, cur_state], dim=0)
    a=	reward

    a=	target_return[0, -1] - (reward / scale)
    a=	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    a=	torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

    episode_return += reward
    episode_length += 1

    if done:
      break



