

"""simple docstring"""




import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
	import torch

	__A	:       str        =						True
except ImportError:
	__A	:       List[str]        =						False


try:
	from torch.hub import _get_torch_home

	__A	:       List[Any]        =						_get_torch_home()
except ImportError:
	__A	:       List[str]        =						os.path.expanduser(
	    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
	)

__A	:       Dict        =						os.path.join(torch_cache_home, 'transformers')

__A	:       Any        =						'https://cdn.huggingface.co'
__A	:       List[str]        =						'https://s3.amazonaws.com/models.huggingface.co/bert'
__A	:       Any        =						'/'.join(str(Path(__file__).resolve()).split('/')[:-1])
__A	:       List[str]        =						os.path.join(PATH, 'config.yaml')
__A	:       Union[str, Any]        =						os.path.join(PATH, 'attributes.txt')
__A	:       List[Any]        =						os.path.join(PATH, 'objects.txt')
__A	:       List[str]        =						os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
__A	:       Any        =						os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
__A	:       Optional[int]        =						os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
__A	:       Union[str, Any]        =						'pytorch_model.bin'
__A	:       Optional[int]        =						'config.yaml'
def   __SCREAMING_SNAKE_CASE		(		lowercase__=OBJECTS     ,				lowercase__=ATTRIBUTES ):
					"""simple docstring"""
					A     =   []
					with open(lowercase__ ) as f:
										for object in f.readlines():
															vg_classes.append(object.split("," )[0].lower().strip() )

					A     =   []
					with open(lowercase__ ) as f:
										for object in f.readlines():
															vg_attrs.append(object.split("," )[0].lower().strip() )
					return vg_classes, vg_attrs
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   OrderedDict()
					with open(lowercase__     ,				"rb" ) as f:
										A     =   pkl.load(lowercase__ )["model"]
					for k in copy.deepcopy(list(ckp.keys() ) ):
										A     =   ckp.pop(lowercase__ )
										if isinstance(lowercase__     ,				np.ndarray ):
															A     =   torch.tensor(lowercase__ )
										else:
															assert isinstance(lowercase__     ,				torch.tensor ), type(lowercase__ )
										A     =   v
					return r





class      __UpperCamelCase					:
		SCREAMING_SNAKE_CASE														=       {}


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						dict	,	__SCREAMING_SNAKE_CASE					:						str = "root"	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=0):
							A     =   name
							A     =   level
							A     =   {}
							for k, v in dictionary.items():
												if v is None:
																	raise ValueError()
												A     =   copy.deepcopy(__SCREAMING_SNAKE_CASE)
												A     =   copy.deepcopy(__SCREAMING_SNAKE_CASE)
												if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE):
																	A     =   Config(__SCREAMING_SNAKE_CASE	,	name=__SCREAMING_SNAKE_CASE	,	level=level + 1)
												A     =   v
												setattr(self	,	__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)

							A     =   d


		def __repr__(self					:						int):
							return str(list((self._pointer.keys())))


		def __setattr__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Any):
							A     =   val
							A     =   val
							A     =   key.split(".")
							A     =   len(__SCREAMING_SNAKE_CASE) - 1
							A     =   self._pointer
							if len(__SCREAMING_SNAKE_CASE) > 1:
												for i, l in enumerate(__SCREAMING_SNAKE_CASE):
																	if hasattr(self	,	__SCREAMING_SNAKE_CASE) and isinstance(getattr(self	,	__SCREAMING_SNAKE_CASE)	,	__SCREAMING_SNAKE_CASE):
																						setattr(getattr(self	,	__SCREAMING_SNAKE_CASE)	,	".".join(levels[i:])	,	__SCREAMING_SNAKE_CASE)
																	if l == last_level:
																						A     =   val
																	else:
																						A     =   pointer[l]


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							return self._pointer


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						Dict):
							with open(F"""{file_name}"""	,	"w") as stream:
												dump(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						int	,	__SCREAMING_SNAKE_CASE					:						int):
							with open(F"""{file_name}"""	,	"w") as stream:
												json.dump(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		@staticmethod
		def    SCREAMING_SNAKE_CASE__  (__SCREAMING_SNAKE_CASE					:						str):
							with open(__SCREAMING_SNAKE_CASE) as stream:
												A     =   load(__SCREAMING_SNAKE_CASE	,	Loader=__SCREAMING_SNAKE_CASE)
							return data


		def __str__(self					:						Union[str, Any]):
							A     =   "    "
							if self._name != "root":
												A     =   F"""{t * (self._level-1)}{self._name}:\n"""
							else:
												A     =   ""
							A     =   self._level
							for i, (k, v) in enumerate(self._pointer.items()):
												if isinstance(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE):
																	r += F"""{t * (self._level)}{v}\n"""
																	self._level += 1
												else:
																	r += F"""{t * (self._level)}{k}: {v} ({type(__SCREAMING_SNAKE_CASE).__name__})\n"""
												A     =   level
							return r[:-1]


		@classmethod
		def    SCREAMING_SNAKE_CASE__  (cls					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						str	,	**__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							A     ,       A     =   cls.get_config_dict(__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)
							return cls(__SCREAMING_SNAKE_CASE)


		@classmethod
		def    SCREAMING_SNAKE_CASE__  (cls					:						int	,	__SCREAMING_SNAKE_CASE					:						str	,	**__SCREAMING_SNAKE_CASE					:						List[str]):
							A     =   kwargs.pop("cache_dir"	,	__SCREAMING_SNAKE_CASE)
							A     =   kwargs.pop("force_download"	,	__SCREAMING_SNAKE_CASE)
							A     =   kwargs.pop("resume_download"	,	__SCREAMING_SNAKE_CASE)
							A     =   kwargs.pop("proxies"	,	__SCREAMING_SNAKE_CASE)
							A     =   kwargs.pop("local_files_only"	,	__SCREAMING_SNAKE_CASE)

							if os.path.isdir(__SCREAMING_SNAKE_CASE):
												A     =   os.path.join(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							elif os.path.isfile(__SCREAMING_SNAKE_CASE) or is_remote_url(__SCREAMING_SNAKE_CASE):
												A     =   pretrained_model_name_or_path
							else:
												A     =   hf_bucket_url(__SCREAMING_SNAKE_CASE	,	filename=__SCREAMING_SNAKE_CASE	,	use_cdn=__SCREAMING_SNAKE_CASE)

							try:
												# Load from URL or cache if already cached
												A     =   cached_path(
												    __SCREAMING_SNAKE_CASE	,	cache_dir=__SCREAMING_SNAKE_CASE	,	force_download=__SCREAMING_SNAKE_CASE	,	proxies=__SCREAMING_SNAKE_CASE	,	resume_download=__SCREAMING_SNAKE_CASE	,	local_files_only=__SCREAMING_SNAKE_CASE	,	)
												# Load config dict
												if resolved_config_file is None:
																	raise EnvironmentError

												A     =   Config.load_yaml(__SCREAMING_SNAKE_CASE)

							except EnvironmentError:
												A     =   "Can't load config for"
												raise EnvironmentError(__SCREAMING_SNAKE_CASE)

							if resolved_config_file == config_file:
												print("loading configuration file from path")
							else:
												print("loading configuration file cache")

							return Config.load_yaml(__SCREAMING_SNAKE_CASE), kwargs

def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   torch.load("dump.pt"     ,				map_location=in_tensor.device )
					A     =   in_tensor.numpy()
					A     =   out_tensor.numpy()[0]
					print(na.shape     ,				na[0, 0, :5] )
					print(na.shape     ,				na[0, 0, :5] )
					assert np.allclose(lowercase__     ,				lowercase__     ,				rtol=0.01     ,				atol=0.1 ), (
					    F"""{sum([1 for x in np.isclose(lowercase__     ,				lowercase__     ,				rtol=0.01     ,				atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %"""
					    " element-wise mismatch"
					)
					raise Exception("tensors are all good" )

					# Hugging face functions below
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   urlparse(lowercase__ )
					return parsed.scheme in ("http", "https")
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__=True ):
					"""simple docstring"""
					A     =   CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
					A     =   "/" not in model_id
					if legacy_format:
										return F"""{endpoint}/{model_id}-{filename}"""
					else:
										return F"""{endpoint}/{model_id}/{filename}"""
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=0     ,				lowercase__=None     ,				):
					"""simple docstring"""
					A     =   "python/{}".format(sys.version.split()[0] )
					if _torch_available:
										ua += "; torch/{}".format(torch.__version__ )
					if isinstance(lowercase__     ,				lowercase__ ):
										ua += "; " + "; ".join("{}/{}".format(lowercase__     ,				lowercase__ ) for k, v in user_agent.items() )
					elif isinstance(lowercase__     ,				lowercase__ ):
										ua += "; " + user_agent
					A     =   {"user-agent": ua}
					if resume_size > 0:
										A     =   "bytes=%d-" % (resume_size,)
					A     =   requests.get(lowercase__     ,				stream=lowercase__     ,				proxies=lowercase__     ,				headers=lowercase__ )
					if response.status_code == 416:  # Range not satisfiable
										return
					A     =   response.headers.get("Content-Length" )
					A     =   resume_size + int(lowercase__ ) if content_length is not None else None
					A     =   tqdm(
					    unit="B"     ,				unit_scale=lowercase__     ,				total=lowercase__     ,				initial=lowercase__     ,				desc="Downloading"     ,				)
					for chunk in response.iter_content(chunk_size=1_024 ):
										if chunk:  # filter out keep-alive new chunks
															progress.update(len(lowercase__ ) )
															temp_file.write(lowercase__ )
					progress.close()
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=None     ,				lowercase__=False     ,				lowercase__=None     ,				lowercase__=10     ,				lowercase__=False     ,				lowercase__=None     ,				lowercase__=False     ,				):
					"""simple docstring"""
					if cache_dir is None:
										A     =   TRANSFORMERS_CACHE
					if isinstance(lowercase__     ,				lowercase__ ):
										A     =   str(lowercase__ )

					os.makedirs(lowercase__     ,				exist_ok=lowercase__ )

					A     =   None
					if not local_files_only:
										try:
															A     =   requests.head(lowercase__     ,				allow_redirects=lowercase__     ,				proxies=lowercase__     ,				timeout=lowercase__ )
															if response.status_code == 200:
																				A     =   response.headers.get("ETag" )
										except (EnvironmentError, requests.exceptions.Timeout):
															# etag is already None
															pass

					A     =   url_to_filename(lowercase__     ,				lowercase__ )

					# get cache path to put the file
					A     =   os.path.join(lowercase__     ,				lowercase__ )

					# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
					# try to get the last downloaded one
					if etag is None:
										if os.path.exists(lowercase__ ):
															return cache_path
										else:
															A     =   [
															    file
															    for file in fnmatch.filter(os.listdir(lowercase__ )     ,				filename + ".*" )
															    if not file.endswith(".json" ) and not file.endswith(".lock" )
															]
															if len(lowercase__ ) > 0:
																				return os.path.join(lowercase__     ,				matching_files[-1] )
															else:
																				# If files cannot be found and local_files_only=True,
																				# the models might've been found if local_files_only=False
																				# Notify the user about that
																				if local_files_only:
																									raise ValueError(
																									    "Cannot find the requested files in the cached path and outgoing traffic has been"
																									    " disabled. To enable model look-ups and downloads online, set 'local_files_only'"
																									    " to False." )
																				return None

    # From now on, etag is not None.
					if os.path.exists(lowercase__ ) and not force_download:
										return cache_path

					# Prevent parallel downloads of the same file with a lock.
					A     =   cache_path + ".lock"
					with FileLock(lowercase__ ):
										# If the download just completed while the lock was activated.
										if os.path.exists(lowercase__ ) and not force_download:
															# Even if returning early like here, the lock will be released.
															return cache_path

										if resume_download:
															A     =   cache_path + ".incomplete"

															@contextmanager
															def _resumable_file_manager():
																				with open(lowercase__     ,				"a+b" ) as f:
																									yield f

															A     =   _resumable_file_manager
															if os.path.exists(lowercase__ ):
																				A     =   os.stat(lowercase__ ).st_size
															else:
																				A     =   0
										else:
															A     =   partial(tempfile.NamedTemporaryFile     ,				dir=lowercase__     ,				delete=lowercase__ )
															A     =   0

										# Download to temporary file, then copy to cache dir once finished.
										# Otherwise you get corrupt cache entries if the download gets interrupted.
										with temp_file_manager() as temp_file:
															print(
															    "%s not found in cache or force_download set to True, downloading to %s"     ,				lowercase__     ,				temp_file.name     ,				)

															http_get(
															    lowercase__     ,				lowercase__     ,				proxies=lowercase__     ,				resume_size=lowercase__     ,				user_agent=lowercase__     ,				)

										os.replace(temp_file.name     ,				lowercase__ )

										A     =   {"url": url, "etag": etag}
										A     =   cache_path + ".json"
										with open(lowercase__     ,				"w" ) as meta_file:
															json.dump(lowercase__     ,				lowercase__ )

					return cache_path
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=None ):
					"""simple docstring"""
					A     =   url.encode("utf-8" )
					A     =   shaaaa(lowercase__ )
					A     =   url_hash.hexdigest()

					if etag:
										A     =   etag.encode("utf-8" )
										A     =   shaaaa(lowercase__ )
										filename += "." + etag_hash.hexdigest()

					if url.endswith(".h5" ):
										filename += ".h5"

					return filename
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=None     ,				lowercase__=False     ,				lowercase__=None     ,				lowercase__=False     ,				lowercase__=None     ,				lowercase__=False     ,				lowercase__=False     ,				lowercase__=False     ,				):
					"""simple docstring"""
					if cache_dir is None:
										A     =   TRANSFORMERS_CACHE
					if isinstance(lowercase__     ,				lowercase__ ):
										A     =   str(lowercase__ )
					if isinstance(lowercase__     ,				lowercase__ ):
										A     =   str(lowercase__ )

					if is_remote_url(lowercase__ ):
										# URL, so get it from the cache (downloading if necessary)
										A     =   get_from_cache(
										    lowercase__     ,				cache_dir=lowercase__     ,				force_download=lowercase__     ,				proxies=lowercase__     ,				resume_download=lowercase__     ,				user_agent=lowercase__     ,				local_files_only=lowercase__     ,				)
					elif os.path.exists(lowercase__ ):
										# File, and it exists.
										A     =   url_or_filename
					elif urlparse(lowercase__ ).scheme == "":
										# File, but it doesn't exist.
										raise EnvironmentError("file {} not found".format(lowercase__ ) )
					else:
										# Something unknown
										raise ValueError("unable to parse {} as a URL or as a local path".format(lowercase__ ) )

					if extract_compressed_file:
										if not is_zipfile(lowercase__ ) and not tarfile.is_tarfile(lowercase__ ):
															return output_path

										# Path where we extract compressed archives
										# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
										A     ,       A     =   os.path.split(lowercase__ )
										A     =   output_file.replace("."     ,				"-" ) + "-extracted"
										A     =   os.path.join(lowercase__     ,				lowercase__ )

										if os.path.isdir(lowercase__ ) and os.listdir(lowercase__ ) and not force_extract:
															return output_path_extracted

										# Prevent parallel extractions
										A     =   output_path + ".lock"
										with FileLock(lowercase__ ):
															shutil.rmtree(lowercase__     ,				ignore_errors=lowercase__ )
															os.makedirs(lowercase__ )
															if is_zipfile(lowercase__ ):
																				with ZipFile(lowercase__     ,				"r" ) as zip_file:
																									zip_file.extractall(lowercase__ )
																									zip_file.close()
															elif tarfile.is_tarfile(lowercase__ ):
																				A     =   tarfile.open(lowercase__ )
																				tar_file.extractall(lowercase__ )
																				tar_file.close()
															else:
																				raise EnvironmentError("Archive format of {} could not be identified".format(lowercase__ ) )

										return output_path_extracted

					return output_path
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__="," ):
					"""simple docstring"""
					assert isinstance(lowercase__     ,				lowercase__ )
					if os.path.isfile(lowercase__ ):
										with open(lowercase__ ) as f:
															A     =   eval(f.read() )
					else:
										A     =   requests.get(lowercase__ )
										try:
															A     =   requests.json()
										except Exception:
															A     =   req.content.decode()
															assert data is not None, "could not connect"
															try:
																				A     =   eval(lowercase__ )
															except Exception:
																				A     =   data.split("\n" )
										req.close()
					return data
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   requests.get(lowercase__ )
					A     =   np.array(Image.open(BytesIO(response.content ) ) )
					return img
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   url.split("/" )[-1]
					if fn not in os.listdir(os.getcwd() ):
										wget.download(lowercase__ )
					with open(lowercase__     ,				"rb" ) as stream:
										A     =   pkl.load(lowercase__ )
					A     =   weights.pop("model" )
					A     =   {}
					for k, v in model.items():
										A     =   torch.from_numpy(lowercase__ )
										if "running_var" in k:
															A     =   torch.tensor([0] )
															A     =   k.replace("running_var"     ,				"num_batches_tracked" )
															A     =   zero
					return new
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					print(F"""{os.path.abspath(os.path.join(lowercase__     ,				os.pardir ) )}/demo.ipynb""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__="RGB" ):
					"""simple docstring"""
					assert isinstance(lowercase__     ,				lowercase__ )
					if os.path.isfile(lowercase__ ):
										A     =   cva.imread(lowercase__ )
					else:
										A     =   get_image_from_url(lowercase__ )
										assert img is not None, F"""could not connect to: {im}"""
					A     =   cva.cvtColor(lowercase__     ,				cva.COLOR_BGR2RGB )
					if input_format == "RGB":
										A     =   img[:, :, ::-1]
					return img
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=1 ):
					"""simple docstring"""
					return (images[i : i + batch] for i in range(0     ,				len(lowercase__ )     ,				lowercase__ ))













import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
