
"""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__			(					lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict		):






     '''simple docstring'''
     # we don't care about the past rewards in this model

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

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

          # pad all tokens to sequence length
          UpperCAmelCase__				:			Tuple							=    torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]		), torch.ones(states.shape[1]		)]		)
          UpperCAmelCase__				:			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__				:			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__				:			Dict							=    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__				:			int							=    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__				:			Tuple							=    None

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

     return action_preds[0, -1]


# build the environment

A__   :						Optional[Any] 							=     gym.make("""Hopper-v3""")
A__   :						Optional[int] 							=     env.observation_space.shape[0]
A__   :						List[str] 							=     env.action_space.shape[0]
A__   :						List[Any] 							=     1_000
A__   :						Tuple 							=     """cuda"""
A__   :						List[Any] 							=     1000.0  # normalization for rewards/returns
A__   :						Optional[Any] 							=     3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
A__   :						Optional[Any] 							=     np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
A__   :						int 							=     np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
A__   :						Optional[int] 							=     torch.from_numpy(state_mean).to(device=device)
A__   :						List[str] 							=     torch.from_numpy(state_std).to(device=device)

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

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

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

        A__   :						str 							=     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__   :						List[str] 							=     action
        A__   :						Optional[Any] 							=     action.detach().cpu().numpy()

        A__							,    A__							,    A__							,    A__   :						Optional[int] 							=     env.step(action)

        A__   :						Optional[Any] 							=     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
        A__   :						Optional[Any] 							=     torch.cat([states, cur_state], dim=0)
        A__   :						Tuple 							=     reward

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

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			int							=    FNetConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    FNetForPreTraining(lowerCAmelCase		)

     UpperCAmelCase__				:			int							=    restore_checkpoint(lowerCAmelCase							,     lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     UpperCAmelCase__				:			Union[str, Any]							=    fnet_pretraining_model.state_dict()

     UpperCAmelCase__				:			Optional[Any]							=    state_dict["fnet.embeddings.position_ids"]
     UpperCAmelCase__				:			Dict							=    {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     UpperCAmelCase__				:			Dict							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]		)
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]		).T
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]		)
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]		)
     UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]		)

     # Encoder Layers
     for layer in range(config.num_hidden_layers		):
          UpperCAmelCase__				:			Any							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["kernel"]		).T
          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["bias"]		)

          UpperCAmelCase__				:			Dict							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["kernel"]		).T
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["bias"]		)

     # Pooler Layers
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]		).T
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]		)

     # Masked LM Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]		).T
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]		)
     UpperCAmelCase__				:			List[str]							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]		)
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]		)

     # Seq Relationship Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]		)
     UpperCAmelCase__				:			str							=    torch.tensor(
         pretrained_model_params["classification"]["output_bias"]		)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase		)

     # Save PreTrained
     print(F"Saving pretrained model to {save_path}"		)
     fnet_pretraining_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--fnet_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained FNet model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
    A__   :						Dict 							=     parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



