



"""simple docstring"""
import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


_lowerCAmelCase       :Union[str, Any]	=   """Run commands across TPU VMs for initial setup before running `accelerate launch`."""





def 					lowerCamelCase_		(UpperCamelCase__						:						Dict=None				):
    if subparsers is not None:
        _UpperCAmelCase				:    List[Any]											=       subparsers.add_parser('''tpu-config'''	,			description=_description				)
    else:
        _UpperCAmelCase				:    List[str]											=       argparse.ArgumentParser('''Accelerate tpu-config command'''	,			description=_description				)
    # Core arguments
    _UpperCAmelCase				:    Dict											=       parser.add_argument_group(
        '''Config Arguments'''	,			'''Arguments that can be configured through `accelerate config`.'''				)
    config_args.add_argument(
        '''--config_file'''	,			type=UpperCamelCase__	,			default=UpperCamelCase__	,			help='''Path to the config file to use for accelerate.'''	,			)
    config_args.add_argument(
        '''--tpu_name'''	,			default=UpperCamelCase__	,			help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''	,			)
    config_args.add_argument(
        '''--tpu_zone'''	,			default=UpperCamelCase__	,			help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''	,			)
    _UpperCAmelCase				:    List[Any]											=       parser.add_argument_group('''TPU Arguments'''	,			'''Arguments for options ran inside the TPU.'''				)
    pod_args.add_argument(
        '''--use_alpha'''	,			action='''store_true'''	,			help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''	,			)
    pod_args.add_argument(
        '''--command_file'''	,			default=UpperCamelCase__	,			help='''The path to the file containing the commands to run on the pod on startup.'''	,			)
    pod_args.add_argument(
        '''--command'''	,			action='''append'''	,			nargs='''+'''	,			help='''A command to run on the pod. Can be passed multiple times.'''	,			)
    pod_args.add_argument(
        '''--install_accelerate'''	,			action='''store_true'''	,			help='''Whether to install accelerate on the pod. Defaults to False.'''	,			)
    pod_args.add_argument(
        '''--accelerate_version'''	,			default='''latest'''	,			help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''	,			)
    pod_args.add_argument(
        '''--debug'''	,			action='''store_true'''	,			help='''If set, will print the command that would be run instead of running it.'''				)

    if subparsers is not None:
        parser.set_defaults(func=UpperCamelCase__				)
    return parser





def 					lowerCamelCase_		(UpperCamelCase__						:						List[Any]				):
    _UpperCAmelCase				:    Optional[Any]											=       None

    # Get the default from the config file if it exists.
    if args.config_file is not None or os.path.isfile(UpperCamelCase__				):
        _UpperCAmelCase				:    Optional[int]											=       load_config_from_file(args.config_file				)
        if not args.command_file and defaults.command_file is not None and not args.command:
            _UpperCAmelCase				:    List[str]											=       defaults.command_file
        if not args.command and defaults.commands is not None:
            _UpperCAmelCase				:    int											=       defaults.commands
        if not args.tpu_name:
            _UpperCAmelCase				:    Any											=       defaults.tpu_name
        if not args.tpu_zone:
            _UpperCAmelCase				:    Union[str, Any]											=       defaults.tpu_zone
    if args.accelerate_version == "dev":
        _UpperCAmelCase				:    Union[str, Any]											=       "git+https://github.com/huggingface/accelerate.git"
    elif args.accelerate_version == "latest":
        _UpperCAmelCase				:    str											=       "accelerate -U"
    elif isinstance(parse(args.accelerate_version				)	,			UpperCamelCase__				):
        _UpperCAmelCase				:    Any											=       F'accelerate=={args.accelerate_version}'

    if not args.command_file and not args.command:
        raise ValueError('''You must specify either a command file or a command to run on the pod.'''				)

    if args.command_file:
        with open(args.command_file	,			'''r'''				) as f:
            _UpperCAmelCase				:    List[str]											=       [f.read().splitlines()]

    # To turn list of lists into list of strings
    if isinstance(args.command[0]	,			UpperCamelCase__				):
        _UpperCAmelCase				:    List[Any]											=       [line for cmd in args.command for line in cmd]
    # Default to the shared folder and install accelerate
    _UpperCAmelCase				:    Dict											=       ["cd /usr/share"]
    if args.install_accelerate:
        new_cmd += [F'pip install {args.accelerate_version}']
    new_cmd += args.command
    _UpperCAmelCase				:    Dict											=       "; ".join(UpperCamelCase__				)

    # Then send it to gcloud
    # Eventually try to use google-api-core to do this instead of subprocess
    _UpperCAmelCase				:    Dict											=       ["gcloud"]
    if args.use_alpha:
        cmd += ["alpha"]
    cmd += [
        "compute",
        "tpus",
        "tpu-vm",
        "ssh",
        args.tpu_name,
        "--zone",
        args.tpu_zone,
        "--command",
        args.command,
        "--worker",
        "all",
    ]
    if args.debug:
        print(F'Running {" ".join(UpperCamelCase__				)}'				)
        return
    subprocess.run(UpperCamelCase__				)
    print('''Successfully setup pod.'''				)





def 					lowerCamelCase_		():
    _UpperCAmelCase				:    Any											=       tpu_command_parser()
    _UpperCAmelCase				:    Optional[int]											=       parser.parse_args()

    tpu_command_launcher(UpperCamelCase__				)






'''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


lowerCAmelCase   : List[str]             =				re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
lowerCAmelCase   : Union[str, Any]             =				1_0
lowerCAmelCase   : Optional[Any]             =				2_5_6




def 	_A  (       A							)  ->				Optional[MinHash]:
	if len(A							) < MIN_NUM_TOKENS:
		return None
	lowercase						:		List[Any]   					=		MinHash(num_perm=A							)
	for token in set(A							):
		min_hash.update(token.encode()							)
	return min_hash




def 	_A  (       A							)  ->				Set[str]:
	return {t for t in NON_ALPHA.split(A							) if len(t.strip()							) > 0}




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      *,
				    a_ = 0.85							,      )    ->  List[str]:
					lowercase						:		Any   					=		duplication_jaccard_threshold
					lowercase						:		str   					=		NUM_PERM
					lowercase						:		Union[str, Any]   					=		MinHashLSH(threshold=self._duplication_jaccard_threshold							,      num_perm=self._num_perm							)

					lowercase						:		Any   					=		defaultdict(a_							)

				def 	a__		(     self							,      a_							,      a_							)    ->  None:
					lowercase						:		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 	a__		(     self							)    ->  List[List[Dict]]:
					lowercase						:		str   					=		[]
					for base, duplicates in self._duplicate_clusters.items():
						lowercase						:		str   					=		[base] + list(a_							)
						# reformat the cluster to be a list of dict
						lowercase						:		Optional[Any]   					=		[{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
						duplicate_clusters.append(a_							)
					return duplicate_clusters


				def 	a__		(     self							,      a_							)    ->  None:
					lowercase						:		Tuple   					=		self.get_duplicate_clusters()
					with open(a_							,      "w"							) as f:
						json.dump(a_							,      a_							)




def 	_A  (       A							)  ->				Dict:
	lowercase					,		lowercase						:		List[str]   					=		element
	lowercase						:		int   					=		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 	_A  (       A							)  ->				Any:
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,ThreadedIterator(A							,max_queue_size=1_0_0_0_0							)							,chunksize=1_0_0							,):
			if data is not None:
				yield data




def 	_A  (       A							,A							)  ->				List[str]:
	lowercase						:		Dict   					=		DuplicationIndex(duplication_jaccard_threshold=A							)

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

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




def 	_A  (       A							,A							)  ->				float:
	lowercase						:		int   					=		get_tokens(A							)
	lowercase						:		Dict   					=		get_tokens(A							)
	return len(tokensa & tokensa							) / len(tokensa | tokensa							)


lowerCAmelCase   : List[str]             =				None




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	lowercase						:		int   					=		[]
	for elementa in cluster:
		lowercase						:		Any   					=		_shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			lowercase						:		Optional[int]   					=		_shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(A							,A							) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			lowercase						:		List[Any]   					=		1
			extremes.append(A							)
	return extremes




def 	_A  (       A							,A							,A							)  ->				Optional[Any]:
	global _shared_dataset
	lowercase						:		Dict   					=		dataset
	lowercase						:		int   					=		[]
	lowercase						:		int   					=		partial(_find_cluster_extremes_shared							,jaccard_threshold=A							)
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        A							,A							,)							,total=len(A							)							,):
			extremes_list.append(A							)
	return extremes_list






def 	_A  (       A							,A = 0.85							)  ->				Tuple[Type[Dataset], List[List[Dict]]]:
	lowercase						:		Dict   					=		make_duplicate_clusters(A							,A							)
	lowercase						:		List[str]   					=		{x["base_index"] for cluster in duplicate_clusters for x in cluster}
	lowercase						:		Any   					=		{}
	lowercase						:		int   					=		find_extremes(A							,A							,A							)
	for extremes in extremes_clusters:
		for element in extremes:
			lowercase						:		str   					=		element
	lowercase						:		str   					=		duplicate_indices - set(extreme_dict.keys()							)
	lowercase						:		Any   					=		dataset.filter(lambda A							,A							: idx not in remove_indices							,with_indices=A							)

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

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

	return ds_filter, duplicate_clusters


