



import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				__a       =		[]
				for line in lines:
								__a       =		re.sub(r"""#.*"""				,     """"""				,     _SCREAMING_SNAKE_CASE    )  # remove comments
								if line:
												filtered_lines.append(_SCREAMING_SNAKE_CASE    )
				__a       =		"""\n""".join(_SCREAMING_SNAKE_CASE    )

				# Make a hash from all this code
				__a       =		full_str.encode("""utf-8"""    )
				return shaaaa(_SCREAMING_SNAKE_CASE    ).hexdigest()


# get importable module names and hash for caching
lowerCamelCase__			= {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
lowerCamelCase__			= {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCamelCase__			= {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
lowerCamelCase__			= {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
						_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")





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__			= 16
lowerCamelCase__			= 32

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Accelerator				,     _SCREAMING_SNAKE_CASE       :			int = 16    ):
				"""simple docstring"""







				__a       =		AutoTokenizer.from_pretrained("""bert-base-cased"""    )
				__a       =		load_dataset("""glue"""				,     """mrpc"""    )

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

				def collate_fn(_SCREAMING_SNAKE_CASE       :			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(
								    _SCREAMING_SNAKE_CASE				,     padding="""longest"""				,     max_length=_SCREAMING_SNAKE_CASE				,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""				,     )

				# Instantiate dataloaders.
				__a       =		DataLoader(
				    tokenized_datasets["""train"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE    )
				__a       =		DataLoader(
				    tokenized_datasets["""validation"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_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

						lowerCamelCase__			= mocked_dataloaders  # noqa: F811

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
				"""simple docstring"""







				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,     _SCREAMING_SNAKE_CASE    ) == "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(_SCREAMING_SNAKE_CASE    )

				__a     ,					__a       =		get_dataloaders(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				__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=_SCREAMING_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).
				__a       =		model.to(accelerator.device    )

				# Instantiate optimizer
				__a       =		AdamW(params=model.parameters()				,     lr=_SCREAMING_SNAKE_CASE    )

				# Instantiate scheduler
				__a       =		get_linear_schedule_with_warmup(
				    optimizer=_SCREAMING_SNAKE_CASE				,     num_warmup_steps=100				,     num_training_steps=(len(_SCREAMING_SNAKE_CASE    ) * 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(
				    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

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

				# Now we train the model
				for epoch in range(_SCREAMING_SNAKE_CASE    ):
								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(_SCREAMING_SNAKE_CASE    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												__a       =		model(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__a       =		loss / gradient_accumulation_steps
												accelerator.backward(_SCREAMING_SNAKE_CASE    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
												# 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(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.logits.argmax(dim=-1    )
												__a     ,					__a       =		accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
												metric.add_batch(
												    predictions=_SCREAMING_SNAKE_CASE				,     references=_SCREAMING_SNAKE_CASE				,     )

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

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

    # 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__						(							):
				"""simple docstring"""







				__a       =		argparse.ArgumentParser(description="""Simple example of training script."""    )
				parser.add_argument(
				    """--mixed_precision"""				,     type=_SCREAMING_SNAKE_CASE				,     default=_SCREAMING_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."""    )
				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=_SCREAMING_SNAKE_CASE				,     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(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						main()
