




'''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							:    Tuple     , __snake_case							:    Union[str, Any]     , __snake_case							:    Tuple     , __snake_case							:    str     , __snake_case							:    Dict     , __snake_case							:    List[Any]     )							->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							=states.reshape(1     , -1     , model.config.state_dim     )
       lowerCamelCase_							=actions.reshape(1     , -1     , model.config.act_dim     )
       lowerCamelCase_							=returns_to_go.reshape(1     , -1     , 1     )
       lowerCamelCase_							=timesteps.reshape(1     , -1     )

       if model.config.max_length is not None:
              lowerCamelCase_							=states[:, -model.config.max_length :]
              lowerCamelCase_							=actions[:, -model.config.max_length :]
              lowerCamelCase_							=returns_to_go[:, -model.config.max_length :]
              lowerCamelCase_							=timesteps[:, -model.config.max_length :]

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

       lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=model(
           states=lowercase__     , actions=lowercase__     , rewards=lowercase__     , returns_to_go=lowercase__     , timesteps=lowercase__     , attention_mask=lowercase__     , return_dict=lowercase__     , )

       return action_preds[0, -1]


# build the environment

a_   :	str							   = gym.make("""Hopper-v3""")
a_   :	Optional[Any]							   = env.observation_space.shape[0]
a_   :	str							   = env.action_space.shape[0]
a_   :	Union[str, Any]							   = 10_00
a_   :	Optional[Any]							   = """cuda"""
a_   :	Any							   = 10_00.0  # normalization for rewards/returns
a_   :	str							   = 36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a_   :	Optional[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,
    ]
)
a_   :	Union[str, 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,
    ]
)
a_   :	Tuple							   = torch.from_numpy(state_mean).to(device=device)
a_   :	Any							   = torch.from_numpy(state_std).to(device=device)

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

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

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

          a_   :	List[Any]							   = 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_   :	Dict							   = action
          a_   :	int							   = action.detach().cpu().numpy()

          a_   , a_   , a_   , a_   :	Union[str, Any]							   = env.step(action)

          a_   :	Dict							   = torch.from_numpy(state).to(device=device).reshape(1, state_dim)
          a_   :	Optional[int]							   = torch.cat([states, cur_state], dim=0)
          a_   :	Dict							   = reward

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

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a_   :	Any							   = 32





def   a_							(   __snake_case							:    Accelerator     , __snake_case							:    int = 16     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=AutoTokenizer.from_pretrained('''bert-base-cased'''     )
       lowerCamelCase_							=load_dataset('''glue'''     , '''mrpc'''     )

       def tokenize_function(__snake_case							:    str     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCamelCase_							=tokenizer(examples['''sentence1''']     , examples['''sentence2''']     , truncation=__snake_case     , max_length=__snake_case     )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              lowerCamelCase_							=datasets.map(
                  __snake_case     , batched=__snake_case     , remove_columns=['''idx''', '''sentence1''', '''sentence2''']     , )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowerCamelCase_							=tokenized_datasets.rename_column('''label'''     , '''labels'''     )

       def collate_fn(__snake_case							:    Union[str, Any]     ):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCamelCase_							=16
              elif accelerator.mixed_precision != "no":
                     lowerCamelCase_							=8
              else:
                     lowerCamelCase_							=None

              return tokenizer.pad(
                  __snake_case     , padding='''longest'''     , pad_to_multiple_of=__snake_case     , return_tensors='''pt'''     , )

       # Instantiate dataloaders.
       lowerCamelCase_							=DataLoader(
           tokenized_datasets['''train''']     , shuffle=__snake_case     , collate_fn=__snake_case     , batch_size=__snake_case     )
       lowerCamelCase_							=DataLoader(
           tokenized_datasets['''validation''']     , shuffle=__snake_case     , collate_fn=__snake_case     , batch_size=__snake_case     )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     a_   :	List[Any]							   = mocked_dataloaders  # noqa: F811





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    List[str]     )							->		Tuple:



       """simple docstring"""
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     , __snake_case     ) == "1":
              lowerCamelCase_							=2
       # Initialize accelerator
       lowerCamelCase_							=Accelerator(cpu=args.cpu     , mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCamelCase_							=config['''lr''']
       lowerCamelCase_							=int(config['''num_epochs''']     )
       lowerCamelCase_							=int(config['''seed''']     )
       lowerCamelCase_							=int(config['''batch_size''']     )

       lowerCamelCase_							=evaluate.load('''glue'''     , '''mrpc'''     )

       # New Code #
       # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
       # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
       # half each time. From this, we can calculate the number of gradient accumulation steps needed
       # and modify the Accelerator object as a result
       @find_executable_batch_size(starting_batch_size=int(__snake_case     )     )
       def inner_training_loop(__snake_case							:    Tuple     ):
              # Since we need to modify the outside accelerator object, we need to bring it
              # to the local scope
              nonlocal accelerator

              # We can calculate the number of gradient accumulation steps based on the current
              # batch size vs the starting batch size
              lowerCamelCase_							=observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              lowerCamelCase_							=num_gradient_accumulation_steps

              # Next we need to free all of the stored model references in the Accelerator each time
              accelerator.free_memory()

              # And set the seed so our results are reproducable each reset
              set_seed(__snake_case     )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowerCamelCase_							=AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     , return_dict=__snake_case     )

              # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
              # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
              # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
              lowerCamelCase_							=model.to(accelerator.device     )

              # Instantiate optimizer
              lowerCamelCase_							=AdamW(params=model.parameters()     , lr=__snake_case     )
              lowerCamelCase_,   lowerCamelCase_							=get_dataloaders(__snake_case     , __snake_case     )

              # Instantiate scheduler
              lowerCamelCase_							=get_linear_schedule_with_warmup(
                  optimizer=__snake_case     , num_warmup_steps=100     , num_training_steps=(len(__snake_case     ) * num_epochs)     , )

              # Prepare everything
              # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
              # prepare method.
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=accelerator.prepare(
                  __snake_case     , __snake_case     , __snake_case     , __snake_case     , __snake_case     )

              # Now we train the model
              for epoch in range(__snake_case     ):
                     model.train()
                     for step, batch in enumerate(__snake_case     ):
                            # And perform gradient accumulation
                            with accelerator.accumulate(__snake_case     ):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device     )
                                   lowerCamelCase_							=model(**__snake_case     )
                                   lowerCamelCase_							=outputs.loss
                                   accelerator.backward(__snake_case     )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     model.eval()
                     for step, batch in enumerate(__snake_case     ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device     )
                            with torch.no_grad():
                                   lowerCamelCase_							=model(**__snake_case     )
                            lowerCamelCase_							=outputs.logits.argmax(dim=-1     )
                            lowerCamelCase_,   lowerCamelCase_							=accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
                            metric.add_batch(
                                predictions=__snake_case     , references=__snake_case     , )

                     lowerCamelCase_							=metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F'''epoch {epoch}:'''     , __snake_case     )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()





def   a_							(   )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=argparse.ArgumentParser(description='''Simple example of training script.'''     )
       parser.add_argument(
           '''--mixed_precision'''     , type=__snake_case     , default=__snake_case     , choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     , help='''Whether to use mixed precision. Choose'''
           '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
           '''and an Nvidia Ampere GPU.'''     , )
       parser.add_argument('''--cpu'''     , action='''store_true'''     , help='''If passed, will train on the CPU.'''     )
       lowerCamelCase_							=parser.parse_args()
       # New Code #
       # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
       lowerCamelCase_							={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
       training_function(__snake_case     , __snake_case     )


if __name__ == "__main__":
     main()




