



"""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
# and perform gradient accumulation
#
# 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 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__   :					List[Any]												=				1_6
UpperCAmelCase__   :					str												=				3_2

def lowercase_						(     _snake_case     ,_snake_case = 16		):
							SCREAMING_SNAKE_CASE__		: str     =     AutoTokenizer.from_pretrained("""bert-base-cased"""		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     load_dataset("""glue"""     ,"""mrpc"""		)

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

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

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

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     DataLoader(
							    tokenized_datasets["""train"""]     ,shuffle=_snake_case     ,collate_fn=_snake_case     ,batch_size=_snake_case		)
							SCREAMING_SNAKE_CASE__		: str     =     DataLoader(
							    tokenized_datasets["""validation"""]     ,shuffle=_snake_case     ,collate_fn=_snake_case     ,batch_size=_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

					UpperCAmelCase__   :					List[str]												=				mocked_dataloaders  # noqa: F811

def lowercase_						(     _snake_case     ,_snake_case		):
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,_snake_case		) == "1":
														SCREAMING_SNAKE_CASE__		: str     =     2
							# New Code #
							SCREAMING_SNAKE_CASE__		: List[str]     =     int(args.gradient_accumulation_steps		)
							# Initialize accelerator
							SCREAMING_SNAKE_CASE__		: Dict     =     Accelerator(
							    cpu=args.cpu     ,mixed_precision=args.mixed_precision     ,gradient_accumulation_steps=_snake_case		)
							if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
														raise NotImplementedError(
														    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""		)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							SCREAMING_SNAKE_CASE__		: int     =     config["""lr"""]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     int(config["""num_epochs"""]		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     int(config["""seed"""]		)
							SCREAMING_SNAKE_CASE__		: int     =     int(config["""batch_size"""]		)

							SCREAMING_SNAKE_CASE__		: str     =     evaluate.load("""glue"""     ,"""mrpc"""		)

							set_seed(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     get_dataloaders(_snake_case     ,_snake_case		)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""     ,return_dict=_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).
							SCREAMING_SNAKE_CASE__		: int     =     model.to(accelerator.device		)

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     AdamW(params=model.parameters()     ,lr=_snake_case		)

							# Instantiate scheduler
							SCREAMING_SNAKE_CASE__		: Any     =     get_linear_schedule_with_warmup(
							    optimizer=_snake_case     ,num_warmup_steps=100     ,num_training_steps=(len(_snake_case		) * num_epochs)     ,)

							# 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.
							SCREAMING_SNAKE_CASE__		: Dict     =     accelerator.prepare(
							    _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)

							# Now we train the model
							for epoch in range(_snake_case		):
														model.train()
														for step, batch in enumerate(_snake_case		):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device		)
																					# New code #
																					# We use the new `accumulate` context manager to perform gradient accumulation
																					# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																					with accelerator.accumulate(_snake_case		):
																												SCREAMING_SNAKE_CASE__		: Any     =     model(**_snake_case		)
																												SCREAMING_SNAKE_CASE__		: str     =     output.loss
																												accelerator.backward(_snake_case		)
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

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

def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     argparse.ArgumentParser(description="""Simple example of training script."""		)
							parser.add_argument(
							    """--mixed_precision"""     ,type=_snake_case     ,default=_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."""     ,)
							# New Code #
							parser.add_argument(
							    """--gradient_accumulation_steps"""     ,type=_snake_case     ,default=1     ,help="""The number of minibatches to be ran before gradients are accumulated."""     ,)
							parser.add_argument("""--cpu"""     ,action="""store_true"""     ,help="""If passed, will train on the CPU."""		)
							SCREAMING_SNAKE_CASE__		: str     =     parser.parse_args()
							SCREAMING_SNAKE_CASE__		: List[Any]     =     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					main()






"""simple docstring"""




import math
import os
import sys

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
							try:
														with open(_snake_case     ,"""rb"""		) as binary_file:
																					SCREAMING_SNAKE_CASE__		: str     =     binary_file.read()
														for dat in data:
																					SCREAMING_SNAKE_CASE__		: int     =     f'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							lexicon.pop(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     last_match_id

							if math.loga(_snake_case		).is_integer():
														for curr_key in lexicon:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """0""" + lexicon[curr_key]

							SCREAMING_SNAKE_CASE__		: Tuple     =     bin(_snake_case		)[2:]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {"""0""": """0""", """1""": """1"""}
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: str     =     """""", """"""
							SCREAMING_SNAKE_CASE__		: int     =     len(_snake_case		)

							for i in range(len(_snake_case		)		):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														SCREAMING_SNAKE_CASE__		: Dict     =     lexicon[curr_string]
														result += last_match_id
														add_key_to_lexicon(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														index += 1
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""

							while curr_string != "" and curr_string not in lexicon:
														curr_string += "0"

							if curr_string != "":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     lexicon[curr_string]
														result += last_match_id

							return result

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     os.path.getsize(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     bin(_snake_case		)[2:]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     len(_snake_case		)

							return "0" * (length_length - 1) + file_length_binary + compressed

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     8
							try:
														with open(_snake_case     ,"""wb"""		) as opened_file:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																					    to_write[i : i + byte_length]
																					    for i in range(0     ,len(_snake_case		)     ,_snake_case		)
																					]

																					if len(result_byte_array[-1]		) % byte_length == 0:
																												result_byte_array.append("""10000000"""		)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]		) - 1
																												)

																					for elem in result_byte_array:
																												opened_file.write(int(_snake_case     ,2		).to_bytes(1     ,byteorder="""big"""		)		)
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     read_file_binary(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     compress_data(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     add_file_length(_snake_case     ,_snake_case		)
							write_file_binary(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])




