






"""simple docstring"""



import argparse
import os

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, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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__	:    Dict			 = 16
A__	:    str			 = 32

def 			_snake_case    (					lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   List[Any] = 16  )    ->     List[Any]:
							lowerCamelCase_	:							int									=AutoTokenizer.from_pretrained("bert-base-cased"  )
							lowerCamelCase_	:							Optional[int]									=load_dataset("glue"    ,						"mrpc"  )

							def tokenize_function(lowerCamelCase__     :   Any  ):
														# max_length=None => use the model max length (it's actually the default)
														lowerCamelCase_	:							Optional[int]									=tokenizer(examples["sentence1"]    ,						examples["sentence2"]    ,						truncation=a__    ,						max_length=a__  )
														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_	:							Union[str, Any]									=datasets.map(
														    a__    ,						batched=a__    ,						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_	:							Union[str, Any]									=tokenized_datasets.rename_column("label"    ,						"labels"  )

							def collate_fn(lowerCamelCase__     :   List[str]  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														lowerCamelCase_	:							List[str]									=128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					lowerCamelCase_	:							List[Any]									=16
														elif accelerator.mixed_precision != "no":
																					lowerCamelCase_	:							Optional[int]									=8
														else:
																					lowerCamelCase_	:							Optional[Any]									=None

														return tokenizer.pad(
														    a__    ,						padding="longest"    ,						max_length=a__    ,						pad_to_multiple_of=a__    ,						return_tensors="pt"    ,						)

							# Instantiate dataloaders.
							lowerCamelCase_	:							List[str]									=DataLoader(
							    tokenized_datasets["train"]    ,						shuffle=a__    ,						collate_fn=a__    ,						batch_size=a__  )
							lowerCamelCase_	:							Tuple									=DataLoader(
							    tokenized_datasets["validation"]    ,						shuffle=a__    ,						collate_fn=a__    ,						batch_size=a__  )

							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[str]			 = mocked_dataloaders  # noqa: F811

def 			_snake_case    (					lowerCamelCase__     :   Union[str, Any]    ,						lowerCamelCase__     :   int  )    ->     int:



							if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,						a__  ) == "1":
														lowerCamelCase_	:							List[Any]									=2
							# Initialize Accelerator

							# New Code #
							# We pass in "all" to `log_with` to grab all available trackers in the environment
							# Note: If using a custom `Tracker` class, should be passed in here such as:
							# >>> log_with = ["all", MyCustomTrackerClassInstance()]
							if args.with_tracking:
														lowerCamelCase_	:							Dict									=Accelerator(
														    cpu=args.cpu    ,						mixed_precision=args.mixed_precision    ,						log_with="all"    ,						project_dir=args.project_dir  )
							else:
														lowerCamelCase_	:							Tuple									=Accelerator(cpu=args.cpu    ,						mixed_precision=args.mixed_precision  )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCamelCase_	:							str									=config['''lr''']
							lowerCamelCase_	:							Optional[int]									=int(config["num_epochs"]  )
							lowerCamelCase_	:							Union[str, Any]									=int(config["seed"]  )
							lowerCamelCase_	:							int									=int(config["batch_size"]  )
							set_seed(a__  )

							lowerCamelCase_	:							List[Any]									=get_dataloaders(a__    ,						a__  )
							lowerCamelCase_	:							Optional[Any]									=evaluate.load("glue"    ,						"mrpc"  )

							# If the batch size is too big we use gradient accumulation
							lowerCamelCase_	:							List[Any]									=1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														lowerCamelCase_	:							Tuple									=batch_size // MAX_GPU_BATCH_SIZE
														lowerCamelCase_	:							Dict									=MAX_GPU_BATCH_SIZE

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

							# 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_	:							List[str]									=model.to(accelerator.device  )

							# Instantiate optimizer
							lowerCamelCase_	:							Any									=AdamW(params=model.parameters()    ,						lr=a__  )

							# Instantiate scheduler
							lowerCamelCase_	:							Any									=get_linear_schedule_with_warmup(
							    optimizer=a__    ,						num_warmup_steps=100    ,						num_training_steps=(len(a__  ) * num_epochs) // gradient_accumulation_steps    ,						)

							# 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_	:							Optional[Any]									=accelerator.prepare(
							    a__    ,						a__    ,						a__    ,						a__    ,						a__  )

							# New Code #
							# We need to initialize the trackers we use. Overall configurations can also be stored
							if args.with_tracking:
														lowerCamelCase_	:							str									=os.path.split(a__  )[-1].split("."  )[0]
														accelerator.init_trackers(a__    ,						a__  )

							# Now we train the model
							for epoch in range(a__  ):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					lowerCamelCase_	:							Dict									=0
														for step, batch in enumerate(a__  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					lowerCamelCase_	:							Any									=model(**a__  )
																					lowerCamelCase_	:							int									=outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					lowerCamelCase_	:							Any									=loss / gradient_accumulation_steps
																					accelerator.backward(a__  )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

														lowerCamelCase_	:							List[Any]									=metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"""epoch {epoch}:"""    ,						a__  )

														# New Code #
														# To actually log, we call `Accelerator.log`
														# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        "accuracy": eval_metric["accuracy"],
																					        "f1": eval_metric["f1"],
																					        "train_loss": total_loss.item() / len(a__  ),
																					        "epoch": epoch,
																					    }    ,						step=a__    ,						)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
							if args.with_tracking:
														accelerator.end_training()

def 			_snake_case    (					)    ->     Union[str, Any]:
							lowerCamelCase_	:							Union[str, Any]									=argparse.ArgumentParser(description="Simple example of training script."  )
							parser.add_argument(
							    "--mixed_precision"    ,						type=a__    ,						default=a__    ,						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."  )
							parser.add_argument(
							    "--with_tracking"    ,						action="store_true"    ,						help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,						)
							parser.add_argument(
							    "--project_dir"    ,						type=a__    ,						default="logs"    ,						help="Location on where to store experiment tracking logs` and relevent project information"    ,						)
							lowerCamelCase_	:							Any									=parser.parse_args()
							lowerCamelCase_	:							Union[str, Any]									={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(a__    ,						a__  )


if __name__ == "__main__":
	main()











"""simple docstring"""



import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A__	:    int			 = {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

						A__	:    List[Any]			 = ValueGuidedRLPipeline.from_pretrained(
						    'bglick13/hopper-medium-v2-value-function-hor32',
						    env=env,
						)

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__	:    Union[str, Any]			 = pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																		    f' {total_score}'
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		A__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

						print(f'Total reward: {total_reward}')



