

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available

def  lowerCamelCase__ (					_a=None):
	if subparsers is not None:
		SCREAMING_SNAKE_CASE   :      Any				     =       subparsers.add_parser("env")
	else:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       argparse.ArgumentParser("Accelerate env command")

	parser.add_argument(
	    "--config_file"							,			default=_a							,			help="The config file to use for the default values in the launching script.")

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

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       torch.__version__
	SCREAMING_SNAKE_CASE   :      str				     =       torch.cuda.is_available()
	SCREAMING_SNAKE_CASE   :      Any				     =       is_xpu_available()
	SCREAMING_SNAKE_CASE   :      str				     =       is_npu_available()

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "Not found"
	# Get the default from the config file.
	if args.config_file is not None or os.path.isfile(_a):
		SCREAMING_SNAKE_CASE   :      str				     =       load_config_from_file(args.config_file).to_dict()

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       {
	    "`Accelerate` version": version,
	    "Platform": platform.platform(),
	    "Python version": platform.python_version(),
	    "Numpy version": np.__version__,
	    "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
	    "PyTorch XPU available": str(_a),
	    "PyTorch NPU available": str(_a),
	    "System RAM": f"{psutil.virtual_memory().total / 1024 ** 3:.2f} GB",
	}
	if pt_cuda_available:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cuda.get_device_name()

	print("\nCopy-and-paste the text below in your GitHub issue\n")
	print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]))

	print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:")
	SCREAMING_SNAKE_CASE   :      str				     =       (
	    "\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()])
	    if isinstance(_a							,			_a)
	    else f"\t{accelerate_config}"
	)
	print(_a)

	SCREAMING_SNAKE_CASE   :      int				     =       accelerate_config

	return info

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Any				     =       env_command_parser()
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       parser.parse_args()
	env_command(_a)
	return 0


if __name__ == "__main__":
			raise SystemExit(main())


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_						     =    256

def  lowerCamelCase__ (					_a):
	if len(_a) < MIN_NUM_TOKENS:
		return None
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MinHash(num_perm=_a)
	for token in set(_a):
		min_hash.update(token.encode())
	return min_hash

def  lowerCamelCase__ (					_a):
	return {t for t in NON_ALPHA.split(_a) if len(t.strip()) > 0}






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      *,
			    a							: float = 0.85      ,      )							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       duplication_jaccard_threshold
				SCREAMING_SNAKE_CASE   :      Any				     =       NUM_PERM
				SCREAMING_SNAKE_CASE   :      int				     =       MinHashLSH(threshold=self._duplication_jaccard_threshold      ,      num_perm=self._num_perm	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       defaultdict(a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Tuple      ,      a							: MinHash	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       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 							__UpperCamelCase (			self							: Optional[int]	)							->       List[List[Dict]]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
				for base, duplicates in self._duplicate_clusters.items():
					SCREAMING_SNAKE_CASE   :      int				     =       [base] + list(a	)
					# reformat the cluster to be a list of dict
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
					duplicate_clusters.append(a	)
				return duplicate_clusters






			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: List[str]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self.get_duplicate_clusters()
				with open(a      ,      "w"	) as f:
					json.dump(a      ,      a	)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       element
	SCREAMING_SNAKE_CASE   :      Any				     =       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__ (					_a):
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash							,			ThreadedIterator(_a							,			max_queue_size=10000)							,			chunksize=100							,			):
			if data is not None:
				yield data

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       DuplicationIndex(duplication_jaccard_threshold=_a)

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

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

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       get_tokens(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       get_tokens(_a)
	return len(tokensa & tokensa) / len(tokensa | tokensa)


a_						     =    None

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
	for elementa in cluster:
		SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
		for elementa in extremes:
			SCREAMING_SNAKE_CASE   :      int				     =       _shared_dataset[elementa["base_index"]]["content"]
			if jaccard_similarity(_a							,			_a) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       1
			extremes.append(_a)
	return extremes

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	global _shared_dataset
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       dataset
	SCREAMING_SNAKE_CASE   :      str				     =       []
	SCREAMING_SNAKE_CASE   :      Dict				     =       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  lowerCamelCase__ (					_a							,			_a = 0.85):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       make_duplicate_clusters(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {}
	SCREAMING_SNAKE_CASE   :      List[str]				     =       find_extremes(_a							,			_a							,			_a)
	for extremes in extremes_clusters:
		for element in extremes:
			SCREAMING_SNAKE_CASE   :      str				     =       element
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       duplicate_indices - set(extreme_dict.keys())
	SCREAMING_SNAKE_CASE   :      str				     =       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:
			SCREAMING_SNAKE_CASE   :      Dict				     =       element["base_index"] in extreme_dict
			if element["is_extreme"]:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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