





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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


UpperCamelCase__   : Any					  = 16
UpperCamelCase__   : Dict					  = 32




def    A_(			A				,      A = 16   ):
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	UpperCAmelCase_								=     load_dataset("""glue"""				,      """mrpc"""   )

	def tokenize_function(A   ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_								=     tokenizer(examples["""sentence1"""]				,      examples["""sentence2"""]				,      truncation=lowercase_				,      max_length=lowercase_   )
		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():
		UpperCAmelCase_								=     datasets.map(
		    lowercase_				,      batched=lowercase_				,      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
	UpperCAmelCase_								=     tokenized_datasets.rename_column("""label"""				,      """labels"""   )

	def collate_fn(A   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_								=     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":
			UpperCAmelCase_								=     16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_								=     8
		else:
			UpperCAmelCase_								=     None

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

	# Instantiate dataloaders.
	UpperCAmelCase_								=     DataLoader(
	    tokenized_datasets["""train"""]				,      shuffle=lowercase_				,      collate_fn=lowercase_				,      batch_size=lowercase_   )
	UpperCAmelCase_								=     DataLoader(
	    tokenized_datasets["""validation"""]				,      shuffle=lowercase_				,      collate_fn=lowercase_				,      batch_size=lowercase_   )

	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

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




def    A_(			A				,      A   ):

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

	UpperCAmelCase_								=     evaluate.load("""glue"""				,      """mrpc"""   )

	# If the batch size is too big we use gradient accumulation
	UpperCAmelCase_								=     1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		UpperCAmelCase_								=     batch_size // MAX_GPU_BATCH_SIZE
		UpperCAmelCase_								=     MAX_GPU_BATCH_SIZE

	set_seed(lowercase_   )
	UpperCAmelCase_								=     get_dataloaders(lowercase_				,      lowercase_   )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,      return_dict=lowercase_   )

	# 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).
	UpperCAmelCase_								=     model.to(accelerator.device   )

	# Instantiate optimizer
	UpperCAmelCase_								=     AdamW(params=model.parameters()				,      lr=lowercase_   )

	# Instantiate scheduler
	UpperCAmelCase_								=     get_linear_schedule_with_warmup(
	    optimizer=lowercase_				,      num_warmup_steps=100				,      num_training_steps=(len(lowercase_   ) * 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.
	UpperCAmelCase_								=     accelerator.prepare(
	    lowercase_				,      lowercase_				,      lowercase_				,      lowercase_				,      lowercase_   )

	# Now we train the model
	for epoch in range(lowercase_   ):
		model.train()
		for step, batch in enumerate(lowercase_   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			UpperCAmelCase_								=     model(**lowercase_   )
			UpperCAmelCase_								=     outputs.loss
			UpperCAmelCase_								=     loss / gradient_accumulation_steps
			accelerator.backward(lowercase_   )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		UpperCAmelCase_								=     0
		for step, batch in enumerate(lowercase_   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			with torch.no_grad():
				UpperCAmelCase_								=     model(**lowercase_   )
			UpperCAmelCase_								=     outputs.logits.argmax(dim=-1   )
			UpperCAmelCase_								=     accelerator.gather((predictions, batch["""labels"""])   )
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(lowercase_   ) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					UpperCAmelCase_								=     predictions[: len(eval_dataloader.dataset   ) - samples_seen]
					UpperCAmelCase_								=     references[: len(eval_dataloader.dataset   ) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=lowercase_				,      references=lowercase_				,      )

		UpperCAmelCase_								=     metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""				,      lowercase_   )




def    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser(description="""Simple example of training script."""   )
	parser.add_argument(
	    """--mixed_precision"""				,      type=lowercase_				,      default=lowercase_				,      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."""   )
	UpperCAmelCase_								=     parser.parse_args()
	UpperCAmelCase_								=     {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(lowercase_				,      lowercase_   )


if __name__ == "__main__":
				main()









from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase__   : Any					  = logging.get_logger(__name__)

UpperCamelCase__   : int					  = {
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Tuple			    =       'trajectory_transformer'
					lowerCamelCase     :     Optional[Any]			    =       ['past_key_values']
					lowerCamelCase     :     Tuple			    =       {
					    'hidden_size': 'n_embd',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}





					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  Optional[Any]=1_00 ,					__lowercase    :  int=5 ,					__lowercase    :  str=1 ,					__lowercase    :  Union[str, Any]=1 ,					__lowercase    :  Optional[Any]=2_49 ,					__lowercase    :  Tuple=6 ,					__lowercase    :  Tuple=17 ,					__lowercase    :  List[str]=25 ,					__lowercase    :  Tuple=4 ,					__lowercase    :  Dict=4 ,					__lowercase    :  int=1_28 ,					__lowercase    :  List[str]=0.1 ,					__lowercase    :  Optional[Any]=0.1 ,					__lowercase    :  Union[str, Any]=0.1 ,					__lowercase    :  Tuple=0.0006 ,					__lowercase    :  Optional[int]=5_12 ,					__lowercase    :  Any=0.02 ,					__lowercase    :  Tuple=1e-12 ,					__lowercase    :  Optional[Any]=1 ,					__lowercase    :  Dict=True ,					__lowercase    :  Optional[Any]=1 ,					__lowercase    :  str=5_02_56 ,					__lowercase    :  Any=5_02_56 ,					**__lowercase    :  str ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     vocab_size
						UpperCAmelCase_								=     action_weight
						UpperCAmelCase_								=     reward_weight
						UpperCAmelCase_								=     value_weight
						UpperCAmelCase_								=     max_position_embeddings
						UpperCAmelCase_								=     block_size
						UpperCAmelCase_								=     action_dim
						UpperCAmelCase_								=     observation_dim
						UpperCAmelCase_								=     transition_dim
						UpperCAmelCase_								=     learning_rate
						UpperCAmelCase_								=     n_layer
						UpperCAmelCase_								=     n_head
						UpperCAmelCase_								=     n_embd
						UpperCAmelCase_								=     embd_pdrop
						UpperCAmelCase_								=     attn_pdrop
						UpperCAmelCase_								=     resid_pdrop
						UpperCAmelCase_								=     initializer_range
						UpperCAmelCase_								=     layer_norm_eps
						UpperCAmelCase_								=     kaiming_initializer_range
						UpperCAmelCase_								=     use_cache
						super().__init__(pad_token_id=__lowercase ,					bos_token_id=__lowercase ,					eos_token_id=__lowercase ,					**__lowercase		)


