
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")





class 			_lowerCAmelCase      :




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = True       ,       _UpperCamelCase = False  )       ->   str:
       lowerCAmelCase_      			=			scheduler
       lowerCAmelCase_      			=			optimizers if isinstance(_UpperCamelCase       ,       (list, tuple)  ) else [optimizers]
       lowerCAmelCase_      			=			split_batches
       lowerCAmelCase_      			=			step_with_optimizer
       lowerCAmelCase_      			=			GradientState()




    def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
       if not self.step_with_optimizer:
          # No link between scheduler and optimizer -> just step
          self.scheduler.step(*_UpperCamelCase       ,       **_UpperCamelCase  )
          return

       # Otherwise, first make sure the optimizer was stepped.
       if not self.gradient_state.sync_gradients:
          if self.gradient_state.adjust_scheduler:
             self.scheduler._step_count += 1
          return

       for opt in self.optimizers:
          if opt.step_was_skipped:
             return
       if self.split_batches:
          # Split batches -> the training dataloader batch size is not changed so one step per training step
          self.scheduler.step(*_UpperCamelCase       ,       **_UpperCamelCase  )
       else:
          # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
          # num_processes steps per training step
          lowerCAmelCase_      			=			AcceleratorState().num_processes
          for _ in range(_UpperCamelCase  ):
             # Special case when using OneCycle and `drop_last` was not used
             if hasattr(self.scheduler       ,       "total_steps"  ):
                if self.scheduler._step_count <= self.scheduler.total_steps:
                   self.scheduler.step(*_UpperCamelCase       ,       **_UpperCamelCase  )
             else:
                self.scheduler.step(*_UpperCamelCase       ,       **_UpperCamelCase  )




    def   __a		(      self  )       ->   Optional[Any]:
       return self.scheduler.get_last_lr()




    def   __a		(      self  )       ->   Any:
       return self.scheduler.state_dict()




    def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
       self.scheduler.load_state_dict(_UpperCamelCase  )




    def   __a		(      self  )       ->   Dict:
       return self.scheduler.get_lr()




    def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Tuple:
       return self.scheduler.print_lr(*_UpperCamelCase       ,       **_UpperCamelCase  )





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


_A     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_A     =  10
_A     =  2_56






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			if len(__lowerCAmelCase       ) < MIN_NUM_TOKENS:
						return None
			lowerCAmelCase_      			=			MinHash(num_perm=__lowerCAmelCase       )
			for token in set(__lowerCAmelCase       ):
						min_hash.update(token.encode()       )
			return min_hash






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			return {t for t in NON_ALPHA.split(__lowerCAmelCase       ) if len(t.strip()       ) > 0}





class 			_lowerCAmelCase      :




				def __init__(      self       ,       *,
				    _UpperCamelCase = 0.85       ,       )       ->   Dict:
							lowerCAmelCase_      			=			duplication_jaccard_threshold
							lowerCAmelCase_      			=			NUM_PERM
							lowerCAmelCase_      			=			MinHashLSH(threshold=self._duplication_jaccard_threshold       ,       num_perm=self._num_perm  )

							lowerCAmelCase_      			=			defaultdict(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self._index.query(_UpperCamelCase  )
							if code_key in self._index.keys:
										print(f"""Duplicate key {code_key}"""  )
										return

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




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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.get_duplicate_clusters()
							with open(_UpperCamelCase       ,       "w"  ) as f:
										json.dump(_UpperCamelCase       ,       _UpperCamelCase  )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			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__       (       __lowerCAmelCase						:						Type[Dataset]       ):




			"""simple docstring"""


			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash					,							ThreadedIterator(__lowerCAmelCase					,							max_queue_size=10000       )					,							chunksize=100					,							):
									if data is not None:
												yield data






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase       )

			for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase       )       )					,							max_queue_size=100       )       ):
						di.add(__lowerCAmelCase					,							__lowerCAmelCase       )

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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_A     =  None






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			for elementa in cluster:
						lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
						for elementa in extremes:
									lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
									if jaccard_similarity(__lowerCAmelCase					,							__lowerCAmelCase       ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									lowerCAmelCase_      			=			1
									extremes.append(__lowerCAmelCase       )
			return extremes






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			global _shared_dataset
			lowerCAmelCase_      			=			dataset
			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			partial(_find_cluster_extremes_shared					,							jaccard_threshold=__lowerCAmelCase       )
			with mp.Pool() as pool:
						for extremes in tqdm(
						    pool.imap_unordered(
						        __lowerCAmelCase					,							__lowerCAmelCase					,							)					,							total=len(__lowerCAmelCase       )					,							):
									extremes_list.append(__lowerCAmelCase       )
			return extremes_list






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float = 0.85       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			make_duplicate_clusters(__lowerCAmelCase					,							__lowerCAmelCase       )
			lowerCAmelCase_      			=			{x["base_index"] for cluster in duplicate_clusters for x in cluster}
			lowerCAmelCase_      			=			{}
			lowerCAmelCase_      			=			find_extremes(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			for extremes in extremes_clusters:
						for element in extremes:
									lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			duplicate_indices - set(extreme_dict.keys()       )
			lowerCAmelCase_      			=			dataset.filter(lambda __lowerCAmelCase					,							__lowerCAmelCase       : idx not in remove_indices					,							with_indices=__lowerCAmelCase       )

			# update duplicate_clusters
			for cluster in duplicate_clusters:
						for element in cluster:
									lowerCAmelCase_      			=			element["base_index"] in extreme_dict
									if element["is_extreme"]:
												lowerCAmelCase_      			=			extreme_dict[element["base_index"]]["copies"]

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

			return ds_filter, duplicate_clusters



