






"""simple docstring"""




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_snake_case          =   re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
_snake_case          =   10
_snake_case          =   256

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  if len(UpperCamelCase__				) < MIN_NUM_TOKENS:
    return None
  _a  :       Dict             =	MinHash(num_perm=UpperCamelCase__				)
  for token in set(UpperCamelCase__				):
    min_hash.update(token.encode()				)
  return min_hash

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  return {t for t in NON_ALPHA.split(UpperCamelCase__				) if len(t.strip()				) > 0}






class        UpperCamelCase			:


 def __init__(     self		:						Dict							,						*,
     UpperCAmelCase__		:						float = 0.8_5							,						)					->       Union[str, Any]:
   _a  :       Dict             =	duplication_jaccard_threshold
   _a  :       Dict             =	NUM_PERM
   _a  :       Any             =	MinHashLSH(threshold=self._duplication_jaccard_threshold							,						num_perm=self._num_perm							)

   _a  :       List[str]             =	defaultdict(__a							)


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						MinHash							)					->       Any:
   _a  :       Dict             =	self._index.query(__a							)
   if code_key in self._index.keys:
     print(f"""Duplicate key {code_key}"""							)
     return

   self._index.insert(__a							,						__a							)
   if len(__a							) > 0:
     for base_duplicate in close_duplicates:
       if base_duplicate in self._duplicate_clusters:
         self._duplicate_clusters[base_duplicate].add(__a							)
         break
     else:
       self._duplicate_clusters[close_duplicates[0]].add(__a							)


 def _lowercase	(     self		:						str							)					->       Dict:
   _a  :       Dict             =	[]
   for base, duplicates in self._duplicate_clusters.items():
     _a  :       str             =	[base] + list(__a							)
     # reformat the cluster to be a list of dict
     _a  :       Optional[int]             =	[{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
     duplicate_clusters.append(__a							)
   return duplicate_clusters







 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Any							)					->       Union[str, Any]:
   _a  :       str             =	self.get_duplicate_clusters()
   with open(__a							,						"""w"""							) as f:
     json.dump(__a							,						__a							)







def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a      ,  _a  :       Tuple             =	element
  _a  :       Dict             =	get_min_hash([t for t in NON_ALPHA.split(data["""content"""]				) if len(t.strip()				) > 0]				)
  if min_hash is not None:
    return (index, data["repo_name"], data["path"]), min_hash

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash     ,   ThreadedIterator(UpperCamelCase__     ,   max_queue_size=1_0_0_0_0				)     ,   chunksize=1_0_0     ,   ):
      if data is not None:
        yield data

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       List[Any]             =	DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase__				)

  for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(UpperCamelCase__				)				)     ,   max_queue_size=1_0_0				)				):
    di.add(UpperCamelCase__     ,   UpperCamelCase__				)

  # Returns a List[Cluster] where Cluster is List[str] with the filenames.
  return di.get_duplicate_clusters()

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	get_tokens(UpperCamelCase__				)
  _a  :       Any             =	get_tokens(UpperCamelCase__				)
  return len(tokensa & tokensa				) / len(tokensa | tokensa				)


_snake_case          =   None

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Any             =	[]
  for elementa in cluster:
    _a  :       Dict             =	_shared_dataset[elementa["""base_index"""]]["""content"""]
    for elementa in extremes:
      _a  :       Optional[int]             =	_shared_dataset[elementa["""base_index"""]]["""content"""]
      if jaccard_similarity(UpperCamelCase__     ,   UpperCamelCase__				) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      _a  :       List[str]             =	1
      extremes.append(UpperCamelCase__				)
  return extremes

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  global _shared_dataset
  _a  :       Dict             =	dataset
  _a  :       Optional[int]             =	[]
  _a  :       Optional[int]             =	partial(_find_cluster_extremes_shared     ,   jaccard_threshold=UpperCamelCase__				)
  with mp.Pool() as pool:
    for extremes in tqdm(
        pool.imap_unordered(
            UpperCamelCase__     ,   UpperCamelCase__     ,   )     ,   total=len(UpperCamelCase__				)     ,   ):
      extremes_list.append(UpperCamelCase__				)
  return extremes_list



def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__ = 0.85				):



  '''simple docstring'''
  _a  :       str             =	make_duplicate_clusters(UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       Optional[Any]             =	{x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
  _a  :       List[Any]             =	{}
  _a  :       List[str]             =	find_extremes(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  for extremes in extremes_clusters:
    for element in extremes:
      _a  :       List[str]             =	element
  _a  :       Tuple             =	duplicate_indices - set(extreme_dict.keys()				)
  _a  :       Tuple             =	dataset.filter(lambda UpperCamelCase__     ,   UpperCamelCase__				: idx not in remove_indices     ,   with_indices=UpperCamelCase__				)

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      _a  :       List[str]             =	element["""base_index"""] in extreme_dict
      if element["is_extreme"]:
        _a  :       List[Any]             =	extreme_dict[element["""base_index"""]]["""copies"""]

  print(F"""Original dataset size: {len(UpperCamelCase__				)}"""				)
  print(F"""Number of duplicate clusters: {len(UpperCamelCase__				)}"""				)
  print(F"""Files in duplicate cluster: {len(UpperCamelCase__				)}"""				)
  print(F"""Unique files in duplicate cluster: {len(UpperCamelCase__				)}"""				)
  print(F"""Filtered dataset size: {len(UpperCamelCase__				)}"""				)

  return ds_filter, duplicate_clusters








'''simple docstring'''







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				( lowercase						:      Optional[Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	str:
				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				( lowercase						:      Optional[Any]    ,							lowercase						:      int    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]    ,							lowercase						:      int=True						)	->	Any:
				model.train()
				_a								= model(lowercase						)
				_a								= F.mse_loss(lowercase    ,							target.to(output.device						)						)
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowercase						)






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      Tuple=False						)	->	List[str]:
				set_seed(42						)
				_a								= RegressionModel()
				_a								= deepcopy(lowercase						)
				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				model.to(accelerator.device						)
				if sched:
								_a								= AdamW(params=model.parameters()    ,							lr=1E-3						)
								_a								= AdamW(params=ddp_model.parameters()    ,							lr=1E-3						)
								_a								= LambdaLR(lowercase    ,							lr_lambda=lambda lowercase						: epoch**0.65						)
								_a								= LambdaLR(lowercase    ,							lr_lambda=lambda lowercase						: epoch**0.65						)
				# Make a copy of `model`
				if sched:
								_a	,					_a	,					_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
				else:
								_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase						)
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader






def  _lowerCamelCase				( lowercase						:      Optional[Any]						)	->	Optional[int]:
				# Test when on a single CPU or GPU that the context manager does nothing
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowercase						):
																step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								else:
												# Sync grads
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								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						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      Tuple						)	->	Tuple:
				# Test on distributed setup that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowercase						):
																step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								else:
												# Sync grads
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# 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						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      List[Any]=False    ,							lowercase						:      Optional[int]=False						)	->	Any:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_a	,					_a								= batch.values()
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(lowercase						):
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# 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(lowercase						) - 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						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]
				GradientState._reset_state()






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

								if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase						)):
												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(lowercase						):
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
												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'
								_a								= (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase						))
								if accelerator.num_processes > 1:
												check_model_parameters(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
				GradientState._reset_state()






def  _lowerCamelCase				( )	->	Any:
				_a								= Accelerator()

				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a								= RegressionDataset(length=96						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase						)
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(lowercase						):
								assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase						)
								if iteration < len(lowercase						) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(lowercase						):
																				assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase						)
																				if batch_num < len(lowercase						) - 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				( )	->	Optional[Any]:
				_a								= Accelerator()
				_a								= 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(lowercase						)
				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(lowercase						)
				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(lowercase    ,							lowercase						)

    # 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(lowercase    ,							lowercase						)






def  _lowerCamelCase				( lowercase						:      Any						)	->	Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()
