





'''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
#
########################################################################

lowerCAmelCase_					:    Any      	=			16
lowerCAmelCase_					:    Dict      	=			32






def  _lowerCamelCase				( lowercase						:      Accelerator    ,							lowercase						:      int = 16						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained("bert-base-cased"						)
				_a								= load_dataset("glue"    ,							"mrpc"						)

				def tokenize_function(lowercase						:      List[str]						):
								# max_length=None => use the model max length (it's actually the default)
								_a								= 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():
								_a								= 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
				_a								= tokenized_datasets.rename_column("label"    ,							"labels"						)

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

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

				# Instantiate dataloaders.
				_a								= DataLoader(
				    tokenized_datasets["train"]    ,							shuffle=lowercase    ,							collate_fn=lowercase    ,							batch_size=lowercase						)
				_a								= 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

					lowerCAmelCase_					:    Dict      	=			mocked_dataloaders  # noqa: F811






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

				_a	,					_a								= get_dataloaders(lowercase    ,							lowercase						)
				_a								= evaluate.load("glue"    ,							"mrpc"						)

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

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_a								= 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).
				_a								= model.to(accelerator.device						)

				# Instantiate optimizer
				_a								= AdamW(params=model.parameters()    ,							lr=lowercase						)

				# Instantiate scheduler
				_a								= 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.
				_a	,					_a	,					_a	,					_a	,					_a								= accelerator.prepare(
				    lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

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

				# Now we train the model
				for epoch in range(lowercase						):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												_a								= 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						)
												_a								= model(**lowercase						)
												_a								= outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												_a								= loss / gradient_accumulation_steps
												accelerator.backward(lowercase						)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

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

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

    # 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				( )	->	Dict:
				_a								= 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."						)
				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=lowercase    ,							default="logs"    ,							help="Location on where to store experiment tracking logs` and relevent project information"    ,							)
				_a								= parser.parse_args()
				_a								= {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(lowercase    ,							lowercase						)


if __name__ == "__main__":
					main()







'''simple docstring'''







import argparse
import os
import re


lowerCAmelCase_					:    Any      	=			'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase_					:    List[str]      	=			re.compile(R'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
lowerCAmelCase_					:    Tuple      	=			re.compile(R'\s*\(\s*"(\S[^"]+)"')






def  _lowerCamelCase				( lowercase						:      Any    ,							lowercase						:      bool = False						)	->	Optional[Any]:
				with open(lowercase    ,							"r"    ,							encoding="utf-8"						) as f:
								_a								= f.read()

				_a								= content.split("\n"						)
				_a								= []
				_a								= 0
				while line_idx < len(lowercase						):
								if _re_intro_mapping.search(lines[line_idx]						) is not None:
												_a								= len(re.search(r"^(\s*)\S"    ,							lines[line_idx]						).groups()[0]						) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(" " * indent + "("						):
																new_lines.append(lines[line_idx]						)
																line_idx += 1

												_a								= []
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				_a								= line_idx
																				while not lines[line_idx].startswith(" " * indent + ")"						):
																								line_idx += 1
																				blocks.append("\n".join(lines[start_idx : line_idx + 1]						)						)
																else:
																				blocks.append(lines[line_idx]						)
																line_idx += 1

												# Sort blocks by their identifiers
												_a								= sorted(lowercase    ,							key=lambda lowercase						: _re_identifier.search(lowercase						).groups()[0]						)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]						)
												line_idx += 1

				if overwrite:
								with open(lowercase    ,							"w"    ,							encoding="utf-8"						) as f:
												f.write("\n".join(lowercase						)						)
				elif "\n".join(lowercase						) != content:
								return True






def  _lowerCamelCase				( lowercase						:      bool = False						)	->	List[str]:
				_a								= [os.path.join(lowercase    ,							lowercase						) for f in os.listdir(lowercase						) if f.endswith(".py"						)]
				_a								= [sort_auto_mapping(lowercase    ,							overwrite=lowercase						) for fname in fnames]

				if not overwrite and any(lowercase						):
								_a								= [f for f, d in zip(lowercase    ,							lowercase						) if d]
								raise ValueError(
								    F'The following files have auto mappings that need sorting: {", ".join(lowercase						)}. Run `make style` to fix'
								    " this."						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Any      	=			argparse.ArgumentParser()
					parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
					lowerCAmelCase_					:    Optional[int]      	=			parser.parse_args()

					sort_all_auto_mappings(not args.check_only)
