





import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig




def _a					(  SCREAMING_SNAKE_CASE__			:  str ,      SCREAMING_SNAKE_CASE__			:  Optional[Any] ,      SCREAMING_SNAKE_CASE__			:  Optional[Any]=0   )       ->				Union[str, Any]:

							'''simple docstring'''






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

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			str      =						param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						(num_heads, hidden_size, num_splits) + input_shape[1:]
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						param.view(*SCREAMING_SNAKE_CASE__   )
														SCREAMING_SNAKE_CASE__   :			Tuple      =						param.transpose(0 ,      2   )
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						param.transpose(1 ,      2   ).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						(num_heads, num_splits, hidden_size) + input_shape[1:]
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						param.view(*SCREAMING_SNAKE_CASE__   )
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						param.transpose(0 ,      1   ).contiguous()
							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						param.view(*SCREAMING_SNAKE_CASE__   )
							return param




def _a					(  SCREAMING_SNAKE_CASE__			:  Optional[int] ,      SCREAMING_SNAKE_CASE__			:  Any ,      SCREAMING_SNAKE_CASE__			:  Union[str, Any]   )       ->				Optional[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[str]      =						{}

							# old versions did not store training args
							SCREAMING_SNAKE_CASE__   :			Tuple      =						input_state_dict.get("args" ,      SCREAMING_SNAKE_CASE__   )
							if ds_args is not None:
														# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
														# from pprint import pprint
														# pprint(vars(ds_args))

														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						ds_args.tokenizer_type
														SCREAMING_SNAKE_CASE__   :			str      =						ds_args.padded_vocab_size
														SCREAMING_SNAKE_CASE__   :			Any      =						ds_args.max_position_embeddings
														SCREAMING_SNAKE_CASE__   :			str      =						ds_args.hidden_size
														SCREAMING_SNAKE_CASE__   :			List[str]      =						ds_args.num_layers
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						ds_args.num_attention_heads
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
														# pprint(config)

							# The number of heads.
							SCREAMING_SNAKE_CASE__   :			List[Any]      =						config.num_attention_heads
							# The hidden_size per head.
							SCREAMING_SNAKE_CASE__   :			Dict      =						config.hidden_size // heads
							# Megatron-LM checkpoint version
							if "checkpoint_version" in input_state_dict.keys():
														SCREAMING_SNAKE_CASE__   :			int      =						input_state_dict["checkpoint_version"]
							else:
														SCREAMING_SNAKE_CASE__   :			List[str]      =						0.0

							# The model.
							SCREAMING_SNAKE_CASE__   :			Tuple      =						input_state_dict["model"]
							# The language model.
							SCREAMING_SNAKE_CASE__   :			Tuple      =						model["language_model"]
							# The embeddings.
							SCREAMING_SNAKE_CASE__   :			List[str]      =						lm["embedding"]

							# The word embeddings.
							SCREAMING_SNAKE_CASE__   :			List[str]      =						embeddings["word_embeddings"]["weight"]
							# Truncate the embedding table to vocab_size rows.
							SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						word_embeddings[: config.vocab_size, :]
							# Store the word embeddings.
							SCREAMING_SNAKE_CASE__   :			Dict      =						word_embeddings

							# The position embeddings.
							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						embeddings["position_embeddings"]["weight"]
							assert pos_embeddings.size(0   ) == config.max_position_embeddings and pos_embeddings.size(1   ) == config.hidden_size
							# Store the position embeddings.
							SCREAMING_SNAKE_CASE__   :			int      =						pos_embeddings

							# The token-type embeddings.
							SCREAMING_SNAKE_CASE__   :			Dict      =						embeddings["tokentype_embeddings"]["weight"]
							# Store the position embeddings.
							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						tokentype_embeddings

							# The transformer.
							SCREAMING_SNAKE_CASE__   :			str      =						lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

							# The regex to extract layer names.
							SCREAMING_SNAKE_CASE__   :			int      =						re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"   )

							# The simple map of names for "automated" rules.
							SCREAMING_SNAKE_CASE__   :			str      =						{
							    "attention.dense": ".attention.output.dense.",
							    "self_attention.dense": ".attention.output.dense.",
							    "mlp.dense_h_to_4h": ".intermediate.dense.",
							    "mlp.dense_4h_to_h": ".output.dense.",
							}

							# Keep track of the attention/query/value tensor.
							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						None

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														SCREAMING_SNAKE_CASE__   :			int      =						layer_re.match(SCREAMING_SNAKE_CASE__   )

														# Stop if that's not a layer
														if m is None:
																					break

														# The index of the layer.
														SCREAMING_SNAKE_CASE__   :			Tuple      =						int(m.group(1   )   )
														# The name of the operation.
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						m.group(2   )
														# Is it a weight or a bias?
														SCREAMING_SNAKE_CASE__   :			Dict      =						m.group(3   )

														# The name of the layer.
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						f'''bert.encoder.layer.{layer_idx}'''

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"   ):
																					SCREAMING_SNAKE_CASE__   :			int      =						"attention.ln" if op_name.startswith("input"   ) else "ln"
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						val

														# Transpose the QKV matrix.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "weight":
																					# Make sure the QKV pointer is nil.
																					assert attention_qkv_weight is None, ""

																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      3 ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
																					# Store the tensor as we need the bias as well to interleave QKV and biases.
																					SCREAMING_SNAKE_CASE__   :			Tuple      =						out_val

														# Transpose the bias.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "bias":
																					# Make sure we read the weight tensor.
																					assert attention_qkv_weight is not None, ""

																					# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																					SCREAMING_SNAKE_CASE__   :			List[Any]      =						attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																					SCREAMING_SNAKE_CASE__   :			Dict      =						fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      3 ,      SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
																					# Split the bias.
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						out_val[0 * config.hidden_size : 1 * config.hidden_size]
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						out_val[1 * config.hidden_size : 2 * config.hidden_size]
																					SCREAMING_SNAKE_CASE__   :			Optional[int]      =						out_val[2 * config.hidden_size : 3 * config.hidden_size]

																					# Store.
																					SCREAMING_SNAKE_CASE__   :			Dict      =						q
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						q_bias
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						k
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						k_bias
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						v
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						v_bias

																					# Clear the stored tensor.
																					SCREAMING_SNAKE_CASE__   :			str      =						None

														# Copy weights and biases as is.
														elif weight_or_bias in ["weight", "bias"]:
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						megatron_to_transformers[op_name]
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						val

    # The final layernorm.
							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						transformer["final_layernorm.weight"]
							SCREAMING_SNAKE_CASE__   :			str      =						transformer["final_layernorm.bias"]

							# The pooler.
							SCREAMING_SNAKE_CASE__   :			str      =						lm["pooler"]

							# Store the matrix and the bias.
							SCREAMING_SNAKE_CASE__   :			Dict      =						pooler["dense.weight"]
							SCREAMING_SNAKE_CASE__   :			Tuple      =						pooler["dense.bias"]

							# The LM head from Megatron (for RACE).
							SCREAMING_SNAKE_CASE__   :			List[Any]      =						model["lm_head"]

							# The transform matrix.
							SCREAMING_SNAKE_CASE__   :			List[str]      =						lm_head["dense.weight"]
							SCREAMING_SNAKE_CASE__   :			str      =						lm_head["dense.bias"]

							# The transform LN.
							SCREAMING_SNAKE_CASE__   :			Dict      =						lm_head["layernorm.weight"]
							SCREAMING_SNAKE_CASE__   :			List[str]      =						lm_head["layernorm.bias"]

							# For the decoder, we replicate the weights.
							SCREAMING_SNAKE_CASE__   :			Dict      =						word_embeddings
							SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						lm_head["bias"]

							# The classifier from Megatron (for MLNI).
							SCREAMING_SNAKE_CASE__   :			Dict      =						model["binary_head"]

							# Store the classifier.
							SCREAMING_SNAKE_CASE__   :			Tuple      =						binary_head["weight"]
							SCREAMING_SNAKE_CASE__   :			Tuple      =						binary_head["bias"]

							# It should be done!
							return output_state_dict




def _a					(  )       ->				Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Dict      =						argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure" ,      action="store_true"   )
							parser.add_argument("path_to_checkpoint" ,      type=SCREAMING_SNAKE_CASE__ ,      help="Path to the ZIP file containing the checkpoint"   )
							parser.add_argument(
							    "--config_file" ,      default="" ,      type=SCREAMING_SNAKE_CASE__ ,      help="An optional config json file describing the pre-trained model." ,      )
							SCREAMING_SNAKE_CASE__   :			List[Any]      =						parser.parse_args()

							# Extract the basename.
							SCREAMING_SNAKE_CASE__   :			str      =						os.path.dirname(args.path_to_checkpoint   )

							# Load the model.
							# the .zip is very optional, let's keep it for backward compatibility
							print(f'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'''   )
							if args.path_to_checkpoint.endswith(".zip"   ):
														with zipfile.ZipFile(args.path_to_checkpoint ,      "r"   ) as checkpoint:
																					with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"   ) as pytorch_dict:
																												SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						torch.load(SCREAMING_SNAKE_CASE__ ,      map_location="cpu"   )
							else:
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						torch.load(args.path_to_checkpoint ,      map_location="cpu"   )

							if args.config_file == "":
														# Default config of megatron-bert 345m
														SCREAMING_SNAKE_CASE__   :			Any      =						MegatronBertConfig()

														# different megatron-bert-*-345m models have different vocab sizes, so override the default
														# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
														SCREAMING_SNAKE_CASE__   :			str      =						input_state_dict["model"]["lm_head"]["bias"].numel()
							else:
														SCREAMING_SNAKE_CASE__   :			str      =						MegatronBertConfig.from_json_file(args.config_file   )

							# Convert.
							print("Converting"   )
							SCREAMING_SNAKE_CASE__   :			Tuple      =						convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__ ,      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__   )

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

							# Store the state_dict to file.
							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						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__":
		main()

####################################################################################################














"""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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any       , snake_case    :      Optional[int]       , snake_case    :      Tuple       , snake_case    :      Union[str, Any]       , snake_case    :      Dict       , snake_case    :      str							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				states.reshape(1       , -1       , model.config.state_dim							)
     UpperCAmelCase__		:  str				=				actions.reshape(1       , -1       , model.config.act_dim							)
     UpperCAmelCase__		:  Any				=				returns_to_go.reshape(1       , -1       , 1							)
     UpperCAmelCase__		:  Union[str, Any]				=				timesteps.reshape(1       , -1							)

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

          # pad all tokens to sequence length
          UpperCAmelCase__		:  Optional[Any]				=				torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]							), torch.ones(states.shape[1]							)]							)
          UpperCAmelCase__		:  Optional[Any]				=				attention_mask.to(dtype=torch.long       , device=states.device							).reshape(1       , -1							)
          UpperCAmelCase__		:  Optional[int]				=				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__		:  Optional[Any]				=				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__		:  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__		:  str				=				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__		:  Any				=				None

     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				model(
         states=snake_case       , actions=snake_case       , rewards=snake_case       , returns_to_go=snake_case       , timesteps=snake_case       , attention_mask=snake_case       , return_dict=snake_case       , )

     return action_preds[0, -1]


# build the environment

_lowerCAmelCase     :     Dict            =	gym.make("""Hopper-v3""")
_lowerCAmelCase     :     Any            =	env.observation_space.shape[0]
_lowerCAmelCase     :     List[Any]            =	env.action_space.shape[0]
_lowerCAmelCase     :     Any            =	1_000
_lowerCAmelCase     :     Union[str, Any]            =	"""cuda"""
_lowerCAmelCase     :     Optional[Any]            =	10_00.0  # normalization for rewards/returns
_lowerCAmelCase     :     Optional[int]            =	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase     :     Any            =	np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_lowerCAmelCase     :     Optional[Any]            =	np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_lowerCAmelCase     :     Any            =	torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase     :     List[Any]            =	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowerCAmelCase     :     List[str]            =	DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
_lowerCAmelCase     :     Optional[int]            =	model.to(device)
model.eval()

for ep in range(10):
 _lowerCAmelCase       ,   _lowerCAmelCase     :     Dict            =	0, 0
 _lowerCAmelCase     :     Dict            =	env.reset()
 _lowerCAmelCase     :     str            =	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 _lowerCAmelCase     :     Union[str, Any]            =	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 _lowerCAmelCase     :     List[Any]            =	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 _lowerCAmelCase     :     Dict            =	torch.zeros(0, device=device, dtype=torch.floataa)

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

  _lowerCAmelCase     :     Tuple            =	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),
  )
  _lowerCAmelCase     :     List[str]            =	action
  _lowerCAmelCase     :     Optional[int]            =	action.detach().cpu().numpy()

  _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase     :     int            =	env.step(action)

  _lowerCAmelCase     :     Union[str, Any]            =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([states, cur_state], dim=0)
  _lowerCAmelCase     :     List[Any]            =	reward

  _lowerCAmelCase     :     List[str]            =	target_return[0, -1] - (reward / scale)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  _lowerCAmelCase     :     Tuple            =	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


