






"""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							=  16
__lowerCamelCase							=  32



def   a    (     __UpperCAmelCase      :    Accelerator      ,   __UpperCAmelCase      :    int = 1_6  )    ->							int:
						__magic_name__:	Optional[int]							=       AutoTokenizer.from_pretrained("""bert-base-cased"""  )
						__magic_name__:	Any							=       load_dataset("""glue"""      ,   """mrpc"""  )

						def tokenize_function(__UpperCAmelCase      :    Optional[Any]  ):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__:	Dict							=       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():
												__magic_name__:	int							=       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
						__magic_name__:	Tuple							=       tokenized_datasets.rename_column("""label"""      ,   """labels"""  )

						def collate_fn(__UpperCAmelCase      :    Union[str, Any]  ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__:	str							=       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__magic_name__:	Union[str, Any]							=       1_6
												elif accelerator.mixed_precision != "no":
																		__magic_name__:	List[str]							=       8
												else:
																		__magic_name__:	int							=       None

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

						# Instantiate dataloaders.
						__magic_name__:	List[Any]							=       DataLoader(
						    tokenized_datasets["""train"""]      ,   shuffle=__UpperCAmelCase      ,   collate_fn=__UpperCAmelCase      ,   batch_size=__UpperCAmelCase  )
						__magic_name__:	str							=       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

							__lowerCamelCase							=  mocked_dataloaders  # noqa: F811



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

						__magic_name__,			__magic_name__:	Tuple							=       get_dataloaders(__UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	str							=       evaluate.load("""glue"""      ,   """mrpc"""  )

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

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

						# Instantiate optimizer
						__magic_name__:	List[str]							=       AdamW(params=model.parameters()      ,   lr=__UpperCAmelCase  )

						# Instantiate scheduler
						__magic_name__:	Optional[Any]							=       get_linear_schedule_with_warmup(
						    optimizer=__UpperCAmelCase      ,   num_warmup_steps=1_0_0      ,   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.
						__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       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:
												__magic_name__:	List[str]							=       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:
																		__magic_name__:	int							=       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  )
																		__magic_name__:	Any							=       model(**__UpperCAmelCase  )
																		__magic_name__:	int							=       outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		__magic_name__:	Tuple							=       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():
																								__magic_name__:	int							=       model(**__UpperCAmelCase  )
																		__magic_name__:	int							=       outputs.logits.argmax(dim=-1  )
																		__magic_name__,			__magic_name__:	List[str]							=       accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
																		metric.add_batch(
																		    predictions=__UpperCAmelCase      ,   references=__UpperCAmelCase      ,   )

												__magic_name__:	Union[str, Any]							=       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   a    (     )    ->							str:
						__magic_name__:	Optional[int]							=       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"""      ,   )
						__magic_name__:	Union[str, Any]							=       parser.parse_args()
						__magic_name__:	int							=       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
						training_function(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							main()










"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional
@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be trained."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""./"""							,	metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path of training dataset."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size for training."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size for evaluation."""} )
				__snake_case :							Optional[float]									=		field(default=0.1							,	metadata={"""help""": """Value of weight decay."""} )
				__snake_case :							Optional[int]									=		field(
				    default=1_00_00							,	metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""} )
				__snake_case :							Optional[float]									=		field(default=2e-4							,	metadata={"""help""": """Learning rate fo training."""} )
				__snake_case :							Optional[str]									=		field(default="""cosine"""							,	metadata={"""help""": """Learning rate."""} )
				__snake_case :							Optional[int]									=		field(
				    default=7_50							,	metadata={"""help""": """Number of warmup steps in the learning rate schedule."""} )
				__snake_case :							Optional[int]									=		field(
				    default=16							,	metadata={"""help""": """Number of gradient accumulation steps."""} )
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""} )
				__snake_case :							Optional[int]									=		field(default=5_00_00							,	metadata={"""help""": """Maximum number of training steps."""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=10_24							,	metadata={"""help""": """Sequence lengths used for training."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Training seed."""} )
				__snake_case :							Optional[int]									=		field(
				    default=10_24							,	metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,	)
				__snake_case :							Optional[str]									=		field(
				    default=snake_case__							,	metadata={"""help""": """States path if the training should continue from a checkpoint folder."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """If True the data is pretokenized."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""} )
				__snake_case :							Optional[int]									=		field(default=2							,	metadata={"""help""": """Batch size used for evaluation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=10_24							,	metadata={"""help""": """Length of sequences to be evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""} )
				__snake_case :							Optional[int]									=		field(default=snake_case__							,	metadata={"""help""": """Number of workers used for code evaluation."""} )
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,	)
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """Sample from the language model's output distribution."""} )
				__snake_case :							Optional[float]									=		field(default=0.2							,	metadata={"""help""": """Sampling temperature used for generation."""} )
				__snake_case :							Optional[int]									=		field(default=2_56							,	metadata={"""help""": """Maximum number of newly generated tokens."""} )
				__snake_case :							Optional[int]									=		field(default=0							,	metadata={"""help""": """Top-k parameter used for generation."""} )
				__snake_case :							Optional[float]									=		field(default=0.95							,	metadata={"""help""": """Top-p parameter used for nucleus sampling."""} )
				__snake_case :							Optional[int]									=		field(default=10							,	metadata={"""help""": """Number of generations to run in parallel."""} )
				__snake_case :							Optional[int]									=		field(
				    default=2_00							,	metadata={"""help""": """Number of completions to generate for each sample."""} )
				__snake_case :							Optional[int]									=		field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""eval_results.json"""							,	metadata={"""help""": """Random seed used for evaluation."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""0"""							,	metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""} )
				__snake_case :							Optional[int]									=		field(
				    default=-1							,	metadata={
				        """help""": (
				            """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
				            """ number corresponds to which GPU device id to run on."""
				        )
				    }							,	)

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[int]									=		field(
				    default=snake_case__							,	metadata={
				        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
				    }							,	)
				__snake_case :							Optional[str]									=		field(
				    default="""transformersbook/codeparrot"""							,	metadata={"""help""": """Folder or name of dataset to process."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot-clean"""							,	metadata={"""help""": """Folder to save processed processed dataset."""} )
				__snake_case :							Optional[int]									=		field(
				    default=10_00_00							,	metadata={"""help""": """Number of files to save per JSON output file."""} )
				__snake_case :							Optional[str]									=		field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""} )
				__snake_case :							Optional[float]									=		field(
				    default=10_00							,	metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=1_00							,	metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.25							,	metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=1.5							,	metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.7							,	metadata={"""help""": """Probability for filtering config, test and uncommon files."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""}							,	)
				__snake_case :							Optional[bool]									=		field(
				    default=snake_case__							,	metadata={"""help""": """If True, near-duplicate samples are removed."""} )
				__snake_case :							Optional[float]									=		field(
				    default=0.85							,	metadata={"""help""": """Jaccard threshold for near-duplicate samples."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""gpt2"""							,	metadata={"""help""": """Base tokenizer to build new tokenizer from."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""transformersbook/codeparrot-train"""							,	metadata={"""help""": """Dataset to train tokenizer on."""} )
				__snake_case :							Optional[str]									=		field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""} )
				__snake_case :							Optional[int]									=		field(default=20_00_00							,	metadata={"""help""": """Number of examples to train tokenizer on."""} )
				__snake_case :							Optional[int]									=		field(
				    default=3_27_68							,	metadata={"""help""": """Number of examples to train the tokenizer on."""} )
				__snake_case :							Optional[str]									=		field(default="""codeparrot"""							,	metadata={"""help""": """Name of new tokenizer."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """Push saved tokenizer to the hub."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path to the dataset to pretokenize."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""tokenized-codeparrot-train"""							,	metadata={"""help""": """Repo name of the pretokenized data."""} )
				__snake_case :							Optional[int]									=		field(default=snake_case__							,	metadata={"""help""": """Number of workers used for code evaluation."""} )

@dataclass
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""
				__snake_case :							Optional[str]									=		field(
				    default="""gpt2-large"""							,	metadata={"""help""": """Configuration to use for model initialization."""} )
				__snake_case :							Optional[str]									=		field(
				    default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Tokenizer attached to model."""} )
				__snake_case :							Optional[str]									=		field(default="""codeparrot"""							,	metadata={"""help""": """Name of the created model."""} )
				__snake_case :							Optional[bool]									=		field(default=snake_case__							,	metadata={"""help""": """Push saved tokenizer to the hub."""} )




