



"""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
#
########################################################################

lowercase_						=       16
lowercase_						=       32



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = 16					):
			lowercase__   					=			AutoTokenizer.from_pretrained("bert-base-cased"					)
			lowercase__   					=			load_dataset("glue"  ,					"mrpc"					)

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

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

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

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

			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

							lowercase_						=       mocked_dataloaders  # noqa: F811



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,					_UpperCAmelCase					) == "1":
						lowercase__   					=			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:
						lowercase__   					=			Accelerator(
						    cpu=args.cpu  ,					mixed_precision=args.mixed_precision  ,					log_with="all"  ,					project_dir=args.project_dir					)
			else:
						lowercase__   					=			Accelerator(cpu=args.cpu  ,					mixed_precision=args.mixed_precision					)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowercase__   					=			config["lr"]
			lowercase__   					=			int(config["num_epochs"]					)
			lowercase__   					=			int(config["seed"]					)
			lowercase__   					=			int(config["batch_size"]					)
			set_seed(_UpperCAmelCase					)

			lowercase__   					=			get_dataloaders(_UpperCAmelCase  ,					_UpperCAmelCase					)
			lowercase__   					=			evaluate.load("glue"  ,					"mrpc"					)

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

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

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

			# Instantiate optimizer
			lowercase__   					=			AdamW(params=model.parameters()  ,					lr=_UpperCAmelCase					)

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

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

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

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

						lowercase__   					=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''  ,					_UpperCAmelCase					)

						# 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(_UpperCAmelCase					),
									        "epoch": epoch,
									    }  ,					step=_UpperCAmelCase  ,					)

    # 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 __lowerCAmelCase	(       ):
			lowercase__   					=			argparse.ArgumentParser(description="Simple example of training script."					)
			parser.add_argument(
			    "--mixed_precision"  ,					type=_UpperCAmelCase  ,					default=_UpperCAmelCase  ,					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=_UpperCAmelCase  ,					default="logs"  ,					help="Location on where to store experiment tracking logs` and relevent project information"  ,					)
			lowercase__   					=			parser.parse_args()
			lowercase__   					=			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(_UpperCAmelCase  ,					_UpperCAmelCase					)


if __name__ == "__main__":
							main()









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


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Optional[Any]       						="""transfo-xl"""
				UpperCamelCase__			:   Dict       						=["""mems"""]
				UpperCamelCase__			:   Optional[int]       						={
				    """n_token""": """vocab_size""",
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}




				def __init__(  self   :  Optional[Any],   __lowercase   :  Optional[Any]=26_7735,   __lowercase   :  int=[2_0000, 4_0000, 20_0000],   __lowercase   :  Union[str, Any]=1024,   __lowercase   :  Tuple=1024,   __lowercase   :  Tuple=16,   __lowercase   :  Optional[Any]=64,   __lowercase   :  str=4096,   __lowercase   :  Optional[int]=4,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  Union[str, Any]=18,   __lowercase   :  List[str]=1600,   __lowercase   :  List[Any]=1000,   __lowercase   :  Union[str, Any]=True,   __lowercase   :  Tuple=True,   __lowercase   :  Optional[Any]=0,   __lowercase   :  List[str]=-1,   __lowercase   :  int=True,   __lowercase   :  Dict=0.1,   __lowercase   :  Union[str, Any]=0.0,   __lowercase   :  str=True,   __lowercase   :  Optional[Any]="normal",   __lowercase   :  str=0.01,   __lowercase   :  Tuple=0.01,   __lowercase   :  List[Any]=0.02,   __lowercase   :  Any=1e-5,   __lowercase   :  Union[str, Any]=0,   **__lowercase   :  Union[str, Any],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			[]
							self.cutoffs.extend(__lowercase  )
							if proj_share_all_but_first:
										lowercase__   					=			[False] + [True] * len(self.cutoffs  )
							else:
										lowercase__   					=			[False] + [False] * len(self.cutoffs  )
							lowercase__   					=			d_model
							lowercase__   					=			d_embed
							lowercase__   					=			d_head
							lowercase__   					=			d_inner
							lowercase__   					=			div_val
							lowercase__   					=			pre_lnorm
							lowercase__   					=			n_layer
							lowercase__   					=			n_head
							lowercase__   					=			mem_len
							lowercase__   					=			same_length
							lowercase__   					=			attn_type
							lowercase__   					=			clamp_len
							lowercase__   					=			sample_softmax
							lowercase__   					=			adaptive
							lowercase__   					=			dropout
							lowercase__   					=			dropatt
							lowercase__   					=			untie_r
							lowercase__   					=			init
							lowercase__   					=			init_range
							lowercase__   					=			proj_init_std
							lowercase__   					=			init_std
							lowercase__   					=			layer_norm_epsilon
							super().__init__(eos_token_id=__lowercase,   **__lowercase  )




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							# Message copied from Transformer-XL documentation
							logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )
							return -1




				@max_position_embeddings.setter
				def 							A__ (  self   :  List[str],   __lowercase   :  Union[str, Any]  ):
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''  )




