

import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


_UpperCAmelCase	:     Optional[int]		       = re.compile(R"""\s+""")





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							return {"hash": hashlib.mda(re.sub(UpperCamelCase__     ,							''     ,							example['content']   ).encode('utf-8'   )   ).hexdigest()}





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					[len(UpperCamelCase__   ) for line in example['content'].splitlines()]
							return {"line_mean": np.mean(UpperCamelCase__   ), "line_max": max(UpperCamelCase__   )}





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					np.mean([c.isalnum() for c in example['content']]   )
							return {"alpha_frac": alpha_frac}





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							if example["hash"] in uniques:
														uniques.remove(example['hash']   )
														return True
							else:
														return False





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=5   ):







							'''simple docstring'''






							snake_case_           =					['auto-generated', 'autogenerated', 'automatically generated']
							snake_case_           =					example['content'].splitlines()
							for _, line in zip(range(UpperCamelCase__   )     ,							UpperCamelCase__   ):
														for keyword in keywords:
																					if keyword in line.lower():
																												return {"autogenerated": True}
							else:
														return {"autogenerated": False}





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=5     ,							UpperCamelCase__=0.05   ):







							'''simple docstring'''






							snake_case_           =					['unit tests', 'test file', 'configuration file']
							snake_case_           =					example['content'].splitlines()
							snake_case_           =					0
							snake_case_           =					0
							# first test
							for _, line in zip(range(UpperCamelCase__   )     ,							UpperCamelCase__   ):
														for keyword in keywords:
																					if keyword in line.lower():
																												return {"config_or_test": True}
    # second test
							snake_case_           =					example['content'].count('\n'   )
							snake_case_           =					int(coeff * nlines   )
							for line in lines:
														count_config += line.lower().count('config'   )
														count_test += line.lower().count('test'   )
														if count_config > threshold or count_test > threshold:
																					return {"config_or_test": True}
							return {"config_or_test": False}





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					['def ', 'class ', 'for ', 'while ']
							snake_case_           =					example['content'].splitlines()
							for line in lines:
														for keyword in keywords:
																					if keyword in line.lower():
																												return {"has_no_keywords": False}
							return {"has_no_keywords": True}





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=4   ):







							'''simple docstring'''






							snake_case_           =					example['content'].splitlines()
							snake_case_           =					0
							for line in lines:
														counter += line.lower().count('='   )
														if counter > minimum:
																					return {"has_few_assignments": False}
							return {"has_few_assignments": True}





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					tokenizer(example['content']     ,							truncation=UpperCamelCase__   )['input_ids']
							snake_case_           =					len(example['content']   ) / len(UpperCamelCase__   )
							return {"ratio": ratio}





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					{}
							results.update(get_hash(UpperCamelCase__   )   )
							results.update(line_stats(UpperCamelCase__   )   )
							results.update(alpha_stats(UpperCamelCase__   )   )
							results.update(char_token_ratio(UpperCamelCase__   )   )
							results.update(is_autogenerated(UpperCamelCase__   )   )
							results.update(is_config_or_test(UpperCamelCase__   )   )
							results.update(has_no_keywords(UpperCamelCase__   )   )
							results.update(has_few_assignments(UpperCamelCase__   )   )
							return results





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							if not check_uniques(UpperCamelCase__     ,							UpperCamelCase__   ):
														return False
							elif example["autogenerated"]:
														return False
							elif example["line_max"] > args.line_max:
														return False
							elif example["line_mean"] > args.line_mean:
														return False
							elif example["alpha_frac"] < args.alpha_frac:
														return False
							elif example["ratio"] < args.min_token_ratio:
														return False
							elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
														return False
							elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
														return False
							elif example["has_few_assignments"]:
														return False
							else:
														return True





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							with open(UpperCamelCase__     ,							'rb'   ) as f_in:
														with gzip.open(str(UpperCamelCase__   ) + '.gz'     ,							'wb'     ,							compresslevel=6   ) as f_out:
																					shutil.copyfileobj(UpperCamelCase__     ,							UpperCamelCase__   )
							os.unlink(UpperCamelCase__   )


# Settings
_UpperCAmelCase	:     Optional[Any]		       = HfArgumentParser(PreprocessingArguments)
_UpperCAmelCase	:     List[Any]		       = parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase	:     int		       = multiprocessing.cpu_count()
_UpperCAmelCase	:     Optional[int]		       = AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
_UpperCAmelCase	:     Any		       = time.time()
_UpperCAmelCase	:     int		       = load_dataset(args.dataset_name, split="""train""")
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
_UpperCAmelCase	:     Dict		       = time.time()
_UpperCAmelCase	:     str		       = ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
_UpperCAmelCase	:     Optional[int]		       = set(ds.unique("""hash"""))
_UpperCAmelCase	:     Tuple		       = len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
_UpperCAmelCase	:     List[Any]		       = time.time()
_UpperCAmelCase	:     Optional[int]		       = ds.filter(filter, fn_kwargs={"""uniques""": uniques, """args""": args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
					_UpperCAmelCase	:     str		       = time.time()
					_UpperCAmelCase       ,		_UpperCAmelCase	:     int		       = deduplicate_dataset(ds_filter, args.jaccard_threshold)
					print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
					print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
_UpperCAmelCase	:     Tuple		       = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
					with open(output_dir / """duplicate_clusters.json""", """w""") as f:
										json.dump(duplicate_clusters, f)

_UpperCAmelCase	:     str		       = output_dir / """data"""
data_dir.mkdir(exist_ok=True)

_UpperCAmelCase	:     List[str]		       = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
					_UpperCAmelCase	:     List[str]		       = str(data_dir / F'''file-{file_number+1:012}.json''')
					_UpperCAmelCase	:     str		       = min(len(ds_filter), index + args.samples_per_file)
					ds_filter.select(list(range(index, end_index))).to_json(file_path)
					compress_file(file_path)


print(F'''Time to save dataset: {time.time()-t_start:.2f}''')







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							for param, grad_param in zip(model_a.parameters()     ,							model_b.parameters()   ):
														if not param.requires_grad:
																					continue
														if not did_step:
																					# Grads should not be in sync
																					assert (
																					    torch.allclose(param.grad     ,							grad_param.grad   ) is False
																					), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
														else:
																					# Grads should be in sync
																					assert (
																					    torch.allclose(param.grad     ,							grad_param.grad   ) is True
																					), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=True   ):







							'''simple docstring'''






							model.train()
							snake_case_           =					model(UpperCamelCase__   )
							snake_case_           =					F.mse_loss(UpperCamelCase__     ,							target.to(output.device   )   )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							set_seed(42   )
							snake_case_           =					RegressionModel()
							snake_case_           =					deepcopy(UpperCamelCase__   )
							snake_case_           =					RegressionDataset(length=80   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							model.to(accelerator.device   )
							if sched:
														snake_case_           =					AdamW(params=model.parameters()     ,							lr=1E-3   )
														snake_case_           =					AdamW(params=ddp_model.parameters()     ,							lr=1E-3   )
														snake_case_           =					LambdaLR(UpperCamelCase__     ,							lr_lambda=lambda UpperCamelCase__   : epoch**0.65   )
														snake_case_           =					LambdaLR(UpperCamelCase__     ,							lr_lambda=lambda UpperCamelCase__   : epoch**0.65   )
							# Make a copy of `model`
							if sched:
														snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					accelerator.prepare(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
							else:
														snake_case_       ,					snake_case_           =					accelerator.prepare(UpperCamelCase__     ,							UpperCamelCase__   )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							# Use a single batch
							snake_case_       ,					snake_case_           =					next(iter(UpperCamelCase__   )   ).values()
							for iteration in range(3   ):
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__   ):
																												step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														for param, ddp_param in zip(model.parameters()     ,							ddp_model.parameters()   ):
																					if not param.requires_grad:
																												continue
																					assert torch.allclose(
																					    param.grad     ,							ddp_param.grad   ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							# Use a single batch
							snake_case_       ,					snake_case_           =					next(iter(UpperCamelCase__   )   ).values()
							for iteration in range(3   ):
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCamelCase__   ):
																												step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														else:
																					# Sync grads
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()     ,							ddp_model.parameters()   ):
																					if not param.requires_grad:
																												continue
																					if iteration % 2 == 0:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is False
																												), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is True
																												), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]





def       __lowerCamelCase			(				UpperCamelCase__=False     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					Accelerator(
							    split_batches=UpperCamelCase__     ,							dispatch_batches=UpperCamelCase__     ,							gradient_accumulation_steps=2   )
							# Test that context manager behaves properly
							snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__   )
							for iteration, batch in enumerate(UpperCamelCase__   ):
														snake_case_       ,					snake_case_           =					batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(UpperCamelCase__   ):
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()     ,							ddp_model.parameters()   ):
																					if not param.requires_grad:
																												continue
																					if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase__   ) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is True
																												), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad     ,							ddp_param.grad   ) is False
																												), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
														snake_case_           =					ddp_input[torch.randperm(len(UpperCamelCase__   )   )]
							GradientState._reset_state()





def       __lowerCamelCase			(				UpperCamelCase__=False     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					Accelerator(
							    split_batches=UpperCamelCase__     ,							dispatch_batches=UpperCamelCase__     ,							gradient_accumulation_steps=2   )
							# Test that context manager behaves properly
							snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					get_training_setup(UpperCamelCase__     ,							UpperCamelCase__   )
							for iteration, batch in enumerate(UpperCamelCase__   ):
														snake_case_       ,					snake_case_           =					batch.values()
														# Gather the distributed inputs and targs for the base model
														snake_case_       ,					snake_case_           =					accelerator.gather((ddp_input, ddp_target)   )
														snake_case_       ,					snake_case_           =					input.to(accelerator.device   ), target.to(accelerator.device   )
														# Perform our initial ground truth step in non "DDP"
														model.train()
														ddp_model.train()
														step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__   )):
																					if split_batches:
																												sched.step()
																					else:
																												for _ in range(accelerator.num_processes   ):
																																			sched.step()
														opt.zero_grad()
														# Perform gradient accumulation under wrapper
														with accelerator.accumulate(UpperCamelCase__   ):
																					step_model(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
																					ddp_opt.step()
																					ddp_sched.step()
																					ddp_opt.zero_grad()

														# Learning rates should be the same
														assert (
														    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
														), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'''
														snake_case_           =					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__   ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__   )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1337 + iteration   )
							GradientState._reset_state()





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Accelerator()

							snake_case_           =					RegressionDataset(length=80   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							snake_case_           =					RegressionDataset(length=96   )
							snake_case_           =					DataLoader(UpperCamelCase__     ,							batch_size=16   )
							snake_case_       ,					snake_case_           =					accelerator.prepare(UpperCamelCase__     ,							UpperCamelCase__   )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(UpperCamelCase__   ):
														assert id(accelerator.gradient_state.active_dataloader   ) == id(UpperCamelCase__   )
														if iteration < len(UpperCamelCase__   ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(UpperCamelCase__   ):
																																			assert id(accelerator.gradient_state.active_dataloader   ) == id(UpperCamelCase__   )
																																			if batch_num < len(UpperCamelCase__   ) - 1:
																																										assert not accelerator.gradient_state.end_of_dataloader
																																			else:
																																										assert accelerator.gradient_state.end_of_dataloader
														else:
																					assert accelerator.gradient_state.end_of_dataloader
							assert accelerator.gradient_state.active_dataloader is None





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					Accelerator()
							snake_case_           =					accelerator.state
							if state.local_process_index == 0:
														print('**Test `accumulate` gradient accumulation with dataloader break**'   )
							test_dataloader_break()
							if state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print('**Test NOOP `no_sync` context manager**'   )
														test_noop_sync(UpperCamelCase__   )
							if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
														if state.local_process_index == 0:
																					print('**Test Distributed `no_sync` context manager**'   )
														test_distributed_sync(UpperCamelCase__   )
							if state.distributed_type == DistributedType.MULTI_GPU:
														for split_batch in [True, False]:
																					for dispatch_batches in [True, False]:
																												if state.local_process_index == 0:
																																			print(
																																			    '**Test `accumulate` gradient accumulation, '     ,							F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''     ,							)
																												test_gradient_accumulation(UpperCamelCase__     ,							UpperCamelCase__   )

    # Currently will break on torch 2.0 +, need to investigate why
							if is_torch_version('<'     ,							'2.0'   ) or state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print(
																					    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,							'`split_batches=False`, `dispatch_batches=False`**'     ,							)
														test_gradient_accumulation_with_opt_and_scheduler()
														if state.distributed_type == DistributedType.MULTI_GPU:
																					for split_batch in [True, False]:
																												for dispatch_batches in [True, False]:
																																			if not split_batch and not dispatch_batches:
																																										continue
																																			if state.local_process_index == 0:
																																										print(
																																										    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,							F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''     ,							)
																																			test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase__     ,							UpperCamelCase__   )





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							main()


if __name__ == "__main__":
					main()




