


"""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 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__		)				->     str:
			_UpperCamelCase				:Optional[Any]				    =  states.reshape(1					,				-1					,				model.config.state_dim		)
			_UpperCamelCase				:List[Any]				    =  actions.reshape(1					,				-1					,				model.config.act_dim		)
			_UpperCamelCase				:List[Any]				    =  returns_to_go.reshape(1					,				-1					,				1		)
			_UpperCamelCase				:Dict				    =  timesteps.reshape(1					,				-1		)

			if model.config.max_length is not None:
						_UpperCamelCase				:Optional[Any]				    =  states[:, -model.config.max_length :]
						_UpperCamelCase				:Union[str, Any]				    =  actions[:, -model.config.max_length :]
						_UpperCamelCase				:Dict				    =  returns_to_go[:, -model.config.max_length :]
						_UpperCamelCase				:List[str]				    =  timesteps[:, -model.config.max_length :]

						# pad all tokens to sequence length
						_UpperCamelCase				:List[str]				    =  torch.cat(
						    [torch.zeros(model.config.max_length - states.shape[1]		), torch.ones(states.shape[1]		)]		)
						_UpperCamelCase				:List[Any]				    =  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				:int				    =  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				:Union[str, 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				:Optional[Any]				    =  None

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

			return action_preds[0, -1]


# build the environment

UpperCamelCase__							:Dict       =  gym.make("""Hopper-v3""")
UpperCamelCase__							:Any       =  env.observation_space.shape[0]
UpperCamelCase__							:Optional[Any]       =  env.action_space.shape[0]
UpperCamelCase__							:Any       =  1_000
UpperCamelCase__							:Tuple       =  '''cuda'''
UpperCamelCase__							:Optional[int]       =  1_000.0  # normalization for rewards/returns
UpperCamelCase__							:Optional[Any]       =  3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__							:str       =  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,
    ]
)
UpperCamelCase__							:Dict       =  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,
    ]
)
UpperCamelCase__							:int       =  torch.from_numpy(state_mean).to(device=device)
UpperCamelCase__							:Tuple       =  torch.from_numpy(state_std).to(device=device)

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

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

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

										UpperCamelCase__							:int       =  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),
										)
										UpperCamelCase__							:List[str]       =  action
										UpperCamelCase__							:List[str]       =  action.detach().cpu().numpy()

										UpperCamelCase__							:List[str]       =  env.step(action)

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

										UpperCamelCase__							:str       =  target_return[0, -1] - (reward / scale)
										UpperCamelCase__							:Dict       =  torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
										UpperCamelCase__							:List[Any]       =  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







"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
					from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
					raise ImportError("""You need to install the version of fairseq from the Mega repo!""")






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Optional[int]:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:List[str]				    =  mega_args
					_UpperCamelCase				:Dict				    =  nn.Embedding(SCREAMING_SNAKE_CASE__				,							self.mega_args.encoder_embed_dim      )
					_UpperCamelCase				:Optional[Any]				    =  nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(SCREAMING_SNAKE_CASE__      )]      )
					_UpperCamelCase				:Tuple				    =  depth





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  Dict:

					"""simple docstring"""



					if batch_first:
								_UpperCamelCase				:Tuple				    =  input_ids.T

					# to make things more confusing, Mega expects the attention mask to
					# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
					# which is the opposite of what HF returns
					if ignore_mask_value == 0:
								_UpperCamelCase				:str				    =  1 - attention_mask

					# get token embeddings from IDs
					_UpperCamelCase				:Union[str, Any]				    =  self.embedding_layer(SCREAMING_SNAKE_CASE__      )

					# pass through the Mega layers
					# input is (time, batch, encoder dim) and output is the same
					for encoder in self.encoders:
								_UpperCamelCase				:Union[str, Any]				    =  encoder(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )

					# return according to the shape specified
					if batch_first:
								# (T, B, H) --> (B, T, H)
								return torch.transpose(SCREAMING_SNAKE_CASE__				,							0				,							1      )
					else:
								return embeds






class     A(     nn.Module		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )			->  Any:

					"""simple docstring"""



					super().__init__()
					_UpperCamelCase				:Dict				    =  MegaLM(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  nn.Linear(mega_args.encoder_embed_dim				,							SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:int				    =  nn.Dropout(p=0.1      )





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=True				,							SCREAMING_SNAKE_CASE__=0      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.mega(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
					return self.mlm_head(self.dropout(SCREAMING_SNAKE_CASE__      )      )





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__		)				->     Union[str, Any]:
			with open(os.path.join(snake_case__					,				'''model_args.pkl'''		)					,				'''rb'''		) as f:
						_UpperCamelCase				:List[str]				    =  pkl.load(snake_case__		)

			# load the original encoder
			_UpperCamelCase				:Tuple				    =  OriginalMegaForMaskedLM(**snake_case__		).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''					,				original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''encoder_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)
			print(
			    '''Original Mega MLM layer:'''					,				original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# create a new config from the old one
			_UpperCamelCase				:Optional[int]				    =  MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']					,				vocab_size=mega_original_args['''vocab_size''']					,				hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim					,				shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim					,				intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim					,				ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim					,				dropout_prob=mega_original_args['''mega_args'''].dropout					,				attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout					,				hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout					,				activation=mega_original_args['''mega_args'''].activation_fn					,				attention_activation=mega_original_args['''mega_args'''].attention_activation_fn					,				bidirectional=mega_original_args['''mega_args'''].bidirectional					,				use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0					,				chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size					,				truncation=mega_original_args['''mega_args'''].truncation_length					,				normalization_type=mega_original_args['''mega_args'''].normalization_type					,				normalize_before_mega=snake_case__					,				norm_affine=snake_case__					,				use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout					,				relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias					,				max_positions=mega_original_args['''mega_args'''].max_source_positions					,				nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim					,				normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before					,				nffn_activation_dropout_prob=0.0					,				add_token_type_embeddings=snake_case__					,				add_lm_hidden_dense_layer=snake_case__					,				)

			_UpperCamelCase				:Optional[Any]				    =  MegaForMaskedLM(snake_case__		).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			_UpperCamelCase				:Dict				    =  original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			_UpperCamelCase				:Union[str, Any]				    =  original_mlm.mega.encoders.state_dict()
			_UpperCamelCase				:List[str]				    =  {}
			for module_name in original_state_dict.keys():
						_UpperCamelCase				:Any				    =  None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												_UpperCamelCase				:List[Any]				    =  module_name.replace('''move.beta'''					,				'''ema_gate.ema_expansion_matrix'''		)
									elif "mega_layer.beta" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''beta'''					,				'''qk_bias'''		)
									else:
												_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''beta'''					,				'''b_param'''		)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''move.gamma'''					,				'''ema_gate.kernel_projection_matrix'''		)
									elif "mega_layer.gamma" in module_name:
												_UpperCamelCase				:str				    =  module_name.replace('''gamma'''					,				'''qk_weight'''		)
									else:
												_UpperCamelCase				:List[str]				    =  module_name.replace('''gamma'''					,				'''g_param'''		)
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									_UpperCamelCase				:Optional[Any]				    =  module_name.replace('''move.alpha'''					,				'''ema_gate.decay_factor'''		)
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									_UpperCamelCase				:Union[str, Any]				    =  module_name.replace('''move.delta'''					,				'''ema_gate.damping_factor'''		)
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									_UpperCamelCase				:int				    =  module_name.replace('''move.omega'''					,				'''ema_gate.residual_weight'''		)

						if new_module_name:
									_UpperCamelCase				:List[Any]				    =  new_module_name

			if len(snake_case__		) != 0:
						print(f"Renaming these keys: {updated_keys.keys()}"		)
			else:
						print('''No need to rename state dict entries'''		)
			for old, new in updated_keys.items():
						_UpperCamelCase				:List[str]				    =  original_state_dict.pop(snake_case__		)

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''					,				hf_mlm.mega.layers.load_state_dict(snake_case__		)		)

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''					,				hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(snake_case__					,				'''mlm_head_weights.pt'''		)					,				map_location='''cpu'''		)		)					,				)

			# test on a randomly generated input sequence
			_UpperCamelCase				:Dict				    =  torch.randint(0					,				hf_config.vocab_size					,				size=(4, 2_56)		)
			_UpperCamelCase				:Tuple				    =  torch.ones_like(snake_case__		)
			# mask a few tokens to make sure masking is applied appropriately :)
			_UpperCamelCase				:str				    =  0

			# run forward passes
			_UpperCamelCase				:str				    =  original_mlm(snake_case__					,				snake_case__					,				batch_first=snake_case__					,				ignore_mask_value=0		)
			_UpperCamelCase				:str				    =  hf_mlm(snake_case__					,				snake_case__		)[0]

			# print shapes and diff
			print(f"original output {original_output.shape}"		)
			print(f"hf output {hf_output.shape}"		)
			print(f"max diff: {(original_output - hf_output).max()}"		)  # 0.0
			_UpperCamelCase				:str				    =  torch.allclose(snake_case__					,				snake_case__					,				atol=1E-3		)

			if success:
						print('''Yay!'''		)
						hf_mlm.save_pretrained(snake_case__		)
			else:
						raise RuntimeError(f"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"		)

			if includes_tokenizer:
						print('''Transferring tokenizer'''		)
						_UpperCamelCase				:Optional[Any]				    =  AutoTokenizer.from_pretrained(snake_case__		)
						tokenizer.save_pretrained(snake_case__		)


if __name__ == "__main__":
					UpperCamelCase__							:Optional[int]       =  argparse.ArgumentParser()

					parser.add_argument(
					    """--pretrained_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Point to the directory containing your model weights using the official Mega repo""",
					)

					parser.add_argument(
					    """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
					)

					parser.add_argument(
					    """--includes_tokenizer""",
					    action="""store_true""",
					    help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
					)

					UpperCamelCase__							:Dict       =  parser.parse_args()

					convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)

