


'''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	:	Optional[int]     =     True
except ImportError:
							__A	:	Any     =     False


try:
							from torch.hub import _get_torch_home

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

__A	:	List[Any]     =     os.path.join(torch_cache_home, 'transformers')

__A	:	Optional[int]     =     'https://cdn.huggingface.co'
__A	:	Optional[int]     =     'https://s3.amazonaws.com/models.huggingface.co/bert'
__A	:	int     =     '/'.join(str(Path(__file__).resolve()).split('/')[:-1])
__A	:	Tuple     =     os.path.join(PATH, 'config.yaml')
__A	:	int     =     os.path.join(PATH, 'attributes.txt')
__A	:	List[str]     =     os.path.join(PATH, 'objects.txt')
__A	:	Optional[Any]     =     os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
__A	:	Optional[int]     =     os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
__A	:	Optional[Any]     =     os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
__A	:	List[str]     =     'pytorch_model.bin'
__A	:	Tuple     =     'config.yaml'





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple=OBJECTS				,      lowerCamelCase_  :Dict=ATTRIBUTES ):







		'''simple docstring'''
		snake_case_		:							Any			       =						[]
		with open(lowerCamelCase_ ) as f:
				for object in f.readlines():
						vg_classes.append(object.split(""",""" )[0].lower().strip() )

		snake_case_		:							Dict			       =						[]
		with open(lowerCamelCase_ ) as f:
				for object in f.readlines():
						vg_attrs.append(object.split(""",""" )[0].lower().strip() )
		return vg_classes, vg_attrs





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							Any			       =						OrderedDict()
		with open(lowerCamelCase_				,      """rb""" ) as f:
				snake_case_		:							int			       =						pkl.load(lowerCamelCase_ )["""model"""]
		for k in copy.deepcopy(list(ckp.keys() ) ):
				snake_case_		:							List[Any]			       =						ckp.pop(lowerCamelCase_ )
				if isinstance(lowerCamelCase_				,      np.ndarray ):
						snake_case_		:							List[Any]			       =						torch.tensor(lowerCamelCase_ )
				else:
						assert isinstance(lowerCamelCase_				,      torch.tensor ), type(lowerCamelCase_ )
				snake_case_		:							Optional[Any]			       =						v
		return r
class 						__UpperCamelCase	:
							lowercase			:		List[Any]   		=  {}





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

									snake_case_		:							Dict			       =						d





							def __repr__(  self		:List[str]			):
									return str(list((self._pointer.keys())			)			)





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





							def 		a__   (  self		:int			):
									return self._pointer





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Optional[Any]			):
									with open(F'''{file_name}'''  ,"""w"""			) as stream:
											dump(_UpperCamelCase  ,_UpperCamelCase			)





							def 		a__   (  self		:int  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Tuple			):
									with open(F'''{file_name}'''  ,"""w"""			) as stream:
											json.dump(_UpperCamelCase  ,_UpperCamelCase			)





							@staticmethod
							def 		a__   (  _UpperCamelCase		:List[str]			):
									with open(_UpperCamelCase			) as stream:
											snake_case_		:							List[Any]			       =						load(_UpperCamelCase  ,Loader=_UpperCamelCase			)
									return data





							def __str__(  self		:int			):
									snake_case_		:							Union[str, Any]			       =						"""    """
									if self._name != "root":
											snake_case_		:							Union[str, Any]			       =						F'''{t * (self._level-1)}{self._name}:\n'''
									else:
											snake_case_		:							Any			       =						""""""
									snake_case_		:							Optional[int]			       =						self._level
									for i, (k, v) in enumerate(self._pointer.items()			):
											if isinstance(_UpperCamelCase  ,_UpperCamelCase			):
													r += F'''{t * (self._level)}{v}\n'''
													self._level += 1
											else:
													r += F'''{t * (self._level)}{k}: {v} ({type(_UpperCamelCase			).__name__})\n'''
											snake_case_		:							List[Any]			       =						level
									return r[:-1]





							@classmethod
							def 		a__   (  cls		:Optional[int]  ,_UpperCamelCase		:str  ,**_UpperCamelCase		:Optional[int]			):
									snake_case_       ,      snake_case_		:							List[Any]			       =						cls.get_config_dict(_UpperCamelCase  ,**_UpperCamelCase			)
									return cls(_UpperCamelCase			)





							@classmethod
							def 		a__   (  cls		:Dict  ,_UpperCamelCase		:str  ,**_UpperCamelCase		:Dict			):
									snake_case_		:							int			       =						kwargs.pop("""cache_dir"""  ,_UpperCamelCase			)
									snake_case_		:							Any			       =						kwargs.pop("""force_download"""  ,_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						kwargs.pop("""resume_download"""  ,_UpperCamelCase			)
									snake_case_		:							Tuple			       =						kwargs.pop("""proxies"""  ,_UpperCamelCase			)
									snake_case_		:							Tuple			       =						kwargs.pop("""local_files_only"""  ,_UpperCamelCase			)

									if os.path.isdir(_UpperCamelCase			):
											snake_case_		:							Optional[int]			       =						os.path.join(_UpperCamelCase  ,_UpperCamelCase			)
									elif os.path.isfile(_UpperCamelCase			) or is_remote_url(_UpperCamelCase			):
											snake_case_		:							int			       =						pretrained_model_name_or_path
									else:
											snake_case_		:							Optional[Any]			       =						hf_bucket_url(_UpperCamelCase  ,filename=_UpperCamelCase  ,use_cdn=_UpperCamelCase			)

									try:
											# Load from URL or cache if already cached
											snake_case_		:							Optional[int]			       =						cached_path(
											    _UpperCamelCase  ,cache_dir=_UpperCamelCase  ,force_download=_UpperCamelCase  ,proxies=_UpperCamelCase  ,resume_download=_UpperCamelCase  ,local_files_only=_UpperCamelCase  ,)
											# Load config dict
											if resolved_config_file is None:
													raise EnvironmentError

											snake_case_		:							int			       =						Config.load_yaml(_UpperCamelCase			)

									except EnvironmentError:
											snake_case_		:							Union[str, Any]			       =						"""Can't load config for"""
											raise EnvironmentError(_UpperCamelCase			)

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

									return Config.load_yaml(_UpperCamelCase			), kwargs





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						torch.load("""dump.pt"""				,      map_location=in_tensor.device )
		snake_case_		:							List[str]			       =						in_tensor.numpy()
		snake_case_		:							Union[str, Any]			       =						out_tensor.numpy()[0]
		print(na.shape				,      na[0, 0, :5] )
		print(na.shape				,      na[0, 0, :5] )
		assert np.allclose(lowerCamelCase_				,      lowerCamelCase_				,      rtol=0.01				,      atol=0.1 ), (
		    F'''{sum([1 for x in np.isclose(lowerCamelCase_				,      lowerCamelCase_				,      rtol=0.01				,      atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*1_00:.4f} %'''
		    " element-wise mismatch"
		)
		raise Exception("""tensors are all good""" )

		# Hugging face functions below





def      UpperCAmelCase      (		lowerCamelCase_  :Any ):







		'''simple docstring'''
		snake_case_		:							int			       =						urlparse(lowerCamelCase_ )
		return parsed.scheme in ("http", "https")





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :str				,      lowerCamelCase_  :Dict=True ):







		'''simple docstring'''
		snake_case_		:							Any			       =						CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		snake_case_		:							Optional[Any]			       =						"""/""" not in model_id
		if legacy_format:
				return F'''{endpoint}/{model_id}-{filename}'''
		else:
				return F'''{endpoint}/{model_id}/{filename}'''





def      UpperCAmelCase      (		lowerCamelCase_  :Any				,      lowerCamelCase_  :str				,      lowerCamelCase_  :List[str]=None				,      lowerCamelCase_  :Dict=0				,      lowerCamelCase_  :Optional[Any]=None				,      ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						"""python/{}""".format(sys.version.split()[0] )
		if _torch_available:
				ua += "; torch/{}".format(torch.__version__ )
		if isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				ua += "; " + "; ".join("""{}/{}""".format(lowerCamelCase_				,      lowerCamelCase_ ) for k, v in user_agent.items() )
		elif isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				ua += "; " + user_agent
		snake_case_		:							List[str]			       =						{"""user-agent""": ua}
		if resume_size > 0:
				snake_case_		:							List[str]			       =						"""bytes=%d-""" % (resume_size,)
		snake_case_		:							Any			       =						requests.get(lowerCamelCase_				,      stream=lowerCamelCase_				,      proxies=lowerCamelCase_				,      headers=lowerCamelCase_ )
		if response.status_code == 4_16:  # Range not satisfiable
				return
		snake_case_		:							Dict			       =						response.headers.get("""Content-Length""" )
		snake_case_		:							Dict			       =						resume_size + int(lowerCamelCase_ ) if content_length is not None else None
		snake_case_		:							List[Any]			       =						tqdm(
		    unit="""B"""				,      unit_scale=lowerCamelCase_				,      total=lowerCamelCase_				,      initial=lowerCamelCase_				,      desc="""Downloading"""				,      )
		for chunk in response.iter_content(chunk_size=10_24 ):
				if chunk:  # filter out keep-alive new chunks
						progress.update(len(lowerCamelCase_ ) )
						temp_file.write(lowerCamelCase_ )
		progress.close()





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Optional[int]=None				,      lowerCamelCase_  :Union[str, Any]=False				,      lowerCamelCase_  :List[str]=None				,      lowerCamelCase_  :Optional[int]=10				,      lowerCamelCase_  :List[Any]=False				,      lowerCamelCase_  :str=None				,      lowerCamelCase_  :List[Any]=False				,      ):







		'''simple docstring'''
		if cache_dir is None:
				snake_case_		:							Union[str, Any]			       =						TRANSFORMERS_CACHE
		if isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							Dict			       =						str(lowerCamelCase_ )

		os.makedirs(lowerCamelCase_				,      exist_ok=lowerCamelCase_ )

		snake_case_		:							List[str]			       =						None
		if not local_files_only:
				try:
						snake_case_		:							Optional[Any]			       =						requests.head(lowerCamelCase_				,      allow_redirects=lowerCamelCase_				,      proxies=lowerCamelCase_				,      timeout=lowerCamelCase_ )
						if response.status_code == 2_00:
								snake_case_		:							Any			       =						response.headers.get("""ETag""" )
				except (EnvironmentError, requests.exceptions.Timeout):
						# etag is already None
						pass

		snake_case_		:							Tuple			       =						url_to_filename(lowerCamelCase_				,      lowerCamelCase_ )

		# get cache path to put the file
		snake_case_		:							List[Any]			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )

		# 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(lowerCamelCase_ ):
						return cache_path
				else:
						snake_case_		:							List[str]			       =						[
						    file
						    for file in fnmatch.filter(os.listdir(lowerCamelCase_ )				,      filename + """.*""" )
						    if not file.endswith(""".json""" ) and not file.endswith(""".lock""" )
						]
						if len(lowerCamelCase_ ) > 0:
								return os.path.join(lowerCamelCase_				,      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(lowerCamelCase_ ) and not force_download:
				return cache_path

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

				if resume_download:
						snake_case_		:							List[Any]			       =						cache_path + """.incomplete"""

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

						snake_case_		:							List[str]			       =						_resumable_file_manager
						if os.path.exists(lowerCamelCase_ ):
								snake_case_		:							List[Any]			       =						os.stat(lowerCamelCase_ ).st_size
						else:
								snake_case_		:							int			       =						0
				else:
						snake_case_		:							int			       =						partial(tempfile.NamedTemporaryFile				,      dir=lowerCamelCase_				,      delete=lowerCamelCase_ )
						snake_case_		:							Optional[Any]			       =						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"""				,      lowerCamelCase_				,      temp_file.name				,      )

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

				os.replace(temp_file.name				,      lowerCamelCase_ )

				snake_case_		:							int			       =						{"""url""": url, """etag""": etag}
				snake_case_		:							Tuple			       =						cache_path + """.json"""
				with open(lowerCamelCase_				,      """w""" ) as meta_file:
						json.dump(lowerCamelCase_				,      lowerCamelCase_ )

		return cache_path





def      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :str=None ):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						url.encode("""utf-8""" )
		snake_case_		:							List[str]			       =						shaaaa(lowerCamelCase_ )
		snake_case_		:							Optional[int]			       =						url_hash.hexdigest()

		if etag:
				snake_case_		:							List[Any]			       =						etag.encode("""utf-8""" )
				snake_case_		:							str			       =						shaaaa(lowerCamelCase_ )
				filename += "." + etag_hash.hexdigest()

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

		return filename





def      UpperCAmelCase      (		lowerCamelCase_  :int				,      lowerCamelCase_  :int=None				,      lowerCamelCase_  :Dict=False				,      lowerCamelCase_  :List[str]=None				,      lowerCamelCase_  :Optional[int]=False				,      lowerCamelCase_  :Any=None				,      lowerCamelCase_  :List[Any]=False				,      lowerCamelCase_  :List[str]=False				,      lowerCamelCase_  :int=False				,      ):







		'''simple docstring'''
		if cache_dir is None:
				snake_case_		:							Dict			       =						TRANSFORMERS_CACHE
		if isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							int			       =						str(lowerCamelCase_ )
		if isinstance(lowerCamelCase_				,      lowerCamelCase_ ):
				snake_case_		:							Tuple			       =						str(lowerCamelCase_ )

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

		if extract_compressed_file:
				if not is_zipfile(lowerCamelCase_ ) and not tarfile.is_tarfile(lowerCamelCase_ ):
						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/"
				snake_case_       ,      snake_case_		:							Union[str, Any]			       =						os.path.split(lowerCamelCase_ )
				snake_case_		:							Any			       =						output_file.replace("""."""				,      """-""" ) + """-extracted"""
				snake_case_		:							Any			       =						os.path.join(lowerCamelCase_				,      lowerCamelCase_ )

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

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

				return output_path_extracted

		return output_path





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :str="," ):







		'''simple docstring'''
		assert isinstance(lowerCamelCase_				,      lowerCamelCase_ )
		if os.path.isfile(lowerCamelCase_ ):
				with open(lowerCamelCase_ ) as f:
						snake_case_		:							List[str]			       =						eval(f.read() )
		else:
				snake_case_		:							Optional[int]			       =						requests.get(lowerCamelCase_ )
				try:
						snake_case_		:							Any			       =						requests.json()
				except Exception:
						snake_case_		:							Tuple			       =						req.content.decode()
						assert data is not None, "could not connect"
						try:
								snake_case_		:							Dict			       =						eval(lowerCamelCase_ )
						except Exception:
								snake_case_		:							Dict			       =						data.split("""\n""" )
				req.close()
		return data





def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any] ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						requests.get(lowerCamelCase_ )
		snake_case_		:							Any			       =						np.array(Image.open(BytesIO(response.content ) ) )
		return img





def      UpperCAmelCase      (		lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		snake_case_		:							Any			       =						url.split("""/""" )[-1]
		if fn not in os.listdir(os.getcwd() ):
				wget.download(lowerCamelCase_ )
		with open(lowerCamelCase_				,      """rb""" ) as stream:
				snake_case_		:							Optional[int]			       =						pkl.load(lowerCamelCase_ )
		snake_case_		:							int			       =						weights.pop("""model""" )
		snake_case_		:							Any			       =						{}
		for k, v in model.items():
				snake_case_		:							Dict			       =						torch.from_numpy(lowerCamelCase_ )
				if "running_var" in k:
						snake_case_		:							Tuple			       =						torch.tensor([0] )
						snake_case_		:							Tuple			       =						k.replace("""running_var"""				,      """num_batches_tracked""" )
						snake_case_		:							Union[str, Any]			       =						zero
		return new





def      UpperCAmelCase      (		):







		'''simple docstring'''
		print(F'''{os.path.abspath(os.path.join(lowerCamelCase_				,      os.pardir ) )}/demo.ipynb''' )





def      UpperCAmelCase      (		lowerCamelCase_  :int				,      lowerCamelCase_  :Any="RGB" ):







		'''simple docstring'''
		assert isinstance(lowerCamelCase_				,      lowerCamelCase_ )
		if os.path.isfile(lowerCamelCase_ ):
				snake_case_		:							str			       =						cva.imread(lowerCamelCase_ )
		else:
				snake_case_		:							Any			       =						get_image_from_url(lowerCamelCase_ )
				assert img is not None, F'''could not connect to: {im}'''
		snake_case_		:							int			       =						cva.cvtColor(lowerCamelCase_				,      cva.COLOR_BGR2RGB )
		if input_format == "RGB":
				snake_case_		:							Dict			       =						img[:, :, ::-1]
		return img






def      UpperCAmelCase      (		lowerCamelCase_  :Any				,      lowerCamelCase_  :List[Any]=1 ):







		'''simple docstring'''
		return (images[i : i + batch] for i in range(0				,      len(lowerCamelCase_ )				,      lowerCamelCase_ ))



'''simple docstring'''






import random
from typing import Any





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		for _ in range(len(lowerCamelCase_ ) ):
				snake_case_		:							Union[str, Any]			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_		:							Any			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_       ,      snake_case_		:							List[str]			       =						data[b], data[a]
		return data


if __name__ == "__main__":
							__A	:	Optional[int]     =     [0, 1, 2, 3, 4, 5, 6, 7]
							__A	:	Optional[int]     =     ['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))