



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__						:						List[str]          =      True
except ImportError:
						A__						:						Dict          =      False


try:
						from torch.hub import _get_torch_home

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

A__						:						str          =      os.path.join(torch_cache_home, '''transformers''')

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


def     UpperCamelCase(						__UpperCamelCase							:	int=OBJECTS  ,__UpperCamelCase							:	Optional[Any]=ATTRIBUTES				):
		lowerCAmelCase_    :  List[Any]             =				[]
		with open(__UpperCamelCase				) as f:
				for object in f.readlines():
						vg_classes.append(object.split(''','''				)[0].lower().strip()				)

		lowerCAmelCase_    :  Optional[int]             =				[]
		with open(__UpperCamelCase				) as f:
				for object in f.readlines():
						vg_attrs.append(object.split(''','''				)[0].lower().strip()				)
		return vg_classes, vg_attrs


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]				):
		lowerCAmelCase_    :  Optional[Any]             =				OrderedDict()
		with open(__UpperCamelCase  ,'''rb'''				) as f:
				lowerCAmelCase_    :  str             =				pkl.load(__UpperCamelCase				)['''model''']
		for k in copy.deepcopy(list(ckp.keys()				)				):
				lowerCAmelCase_    :  Tuple             =				ckp.pop(__UpperCamelCase				)
				if isinstance(__UpperCamelCase  ,np.ndarray				):
						lowerCAmelCase_    :  Any             =				torch.tensor(__UpperCamelCase				)
				else:
						assert isinstance(__UpperCamelCase  ,torch.tensor				), type(__UpperCamelCase				)
				lowerCAmelCase_    :  List[Any]             =				v
		return r



class        __snake_case		:
							_a       =				{}




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

									lowerCAmelCase_    :  Dict             =				d




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




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




							def      UpperCAmelCase__    ( self  : Dict):
									return self._pointer




							def      UpperCAmelCase__    ( self  : str       ,       A_  : Any       ,       A_  : Optional[Any]):
									with open(F"""{file_name}"""       ,       '''w''') as stream:
											dump(A_       ,       A_)




							def      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : str       ,       A_  : Optional[int]):
									with open(F"""{file_name}"""       ,       '''w''') as stream:
											json.dump(A_       ,       A_)




							@staticmethod
							def      UpperCAmelCase__    ( A_  : Any):
									with open(A_) as stream:
											lowerCAmelCase_    :  Union[str, Any]             =				load(A_       ,       Loader=A_)
									return data




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




							@classmethod
							def      UpperCAmelCase__    ( cls  : Optional[Any]       ,       A_  : str       ,       **A_  : Optional[int]):
									lowerCAmelCase_      ,						lowerCAmelCase_    :  int             =				cls.get_config_dict(A_       ,       **A_)
									return cls(A_)







							@classmethod
							def      UpperCAmelCase__    ( cls  : List[str]       ,       A_  : str       ,       **A_  : int):
									lowerCAmelCase_    :  Optional[int]             =				kwargs.pop('''cache_dir'''       ,       A_)
									lowerCAmelCase_    :  str             =				kwargs.pop('''force_download'''       ,       A_)
									lowerCAmelCase_    :  Dict             =				kwargs.pop('''resume_download'''       ,       A_)
									lowerCAmelCase_    :  int             =				kwargs.pop('''proxies'''       ,       A_)
									lowerCAmelCase_    :  Any             =				kwargs.pop('''local_files_only'''       ,       A_)

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

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

											lowerCAmelCase_    :  Dict             =				Config.load_yaml(A_)

									except EnvironmentError:
											lowerCAmelCase_    :  str             =				'''Can\'t load config for'''
											raise EnvironmentError(A_)

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

									return Config.load_yaml(A_), kwargs





def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]				):
		lowerCAmelCase_    :  int             =				torch.load('''dump.pt'''  ,map_location=in_tensor.device				)
		lowerCAmelCase_    :  str             =				in_tensor.numpy()
		lowerCAmelCase_    :  Tuple             =				out_tensor.numpy()[0]
		print(na.shape  ,na[0, 0, :5]				)
		print(na.shape  ,na[0, 0, :5]				)
		assert np.allclose(__UpperCamelCase  ,__UpperCamelCase  ,rtol=0.0_1  ,atol=0.1				), (
		    f"""{sum([1 for x in np.isclose(__UpperCamelCase  ,__UpperCamelCase  ,rtol=0.0_1  ,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     UpperCamelCase(						__UpperCamelCase							:	int				):
		lowerCAmelCase_    :  List[str]             =				urlparse(__UpperCamelCase				)
		return parsed.scheme in ("http", "https")


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	str  ,__UpperCamelCase							:	List[str]=True				):
		lowerCAmelCase_    :  Union[str, Any]             =				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
		lowerCAmelCase_    :  Tuple             =				'''/''' not in model_id
		if legacy_format:
				return f"""{endpoint}/{model_id}-{filename}"""
		else:
				return f"""{endpoint}/{model_id}/{filename}"""


def     UpperCamelCase(						__UpperCamelCase							:	List[str]  ,__UpperCamelCase							:	int  ,__UpperCamelCase							:	Optional[Any]=None  ,__UpperCamelCase							:	Union[str, Any]=0  ,__UpperCamelCase							:	int=None  ,):
		lowerCAmelCase_    :  str             =				'''python/{}'''.format(sys.version.split()[0]				)
		if _torch_available:
				ua += "; torch/{}".format(torch.__version__				)
		if isinstance(__UpperCamelCase  ,__UpperCamelCase				):
				ua += "; " + "; ".join('''{}/{}'''.format(__UpperCamelCase  ,__UpperCamelCase				) for k, v in user_agent.items()				)
		elif isinstance(__UpperCamelCase  ,__UpperCamelCase				):
				ua += "; " + user_agent
		lowerCAmelCase_    :  Optional[Any]             =				{'''user-agent''': ua}
		if resume_size > 0:
				lowerCAmelCase_    :  Optional[Any]             =				'''bytes=%d-''' % (resume_size,)
		lowerCAmelCase_    :  Optional[int]             =				requests.get(__UpperCamelCase  ,stream=__UpperCamelCase  ,proxies=__UpperCamelCase  ,headers=__UpperCamelCase				)
		if response.status_code == 416:  # Range not satisfiable
				return
		lowerCAmelCase_    :  Any             =				response.headers.get('''Content-Length'''				)
		lowerCAmelCase_    :  Tuple             =				resume_size + int(__UpperCamelCase				) if content_length is not None else None
		lowerCAmelCase_    :  Tuple             =				tqdm(
		    unit='''B'''  ,unit_scale=__UpperCamelCase  ,total=__UpperCamelCase  ,initial=__UpperCamelCase  ,desc='''Downloading'''  ,)
		for chunk in response.iter_content(chunk_size=1024				):
				if chunk:  # filter out keep-alive new chunks
						progress.update(len(__UpperCamelCase				)				)
						temp_file.write(__UpperCamelCase				)
		progress.close()


def     UpperCamelCase(						__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Any=None  ,__UpperCamelCase							:	Optional[Any]=False  ,__UpperCamelCase							:	Any=None  ,__UpperCamelCase							:	Union[str, Any]=10  ,__UpperCamelCase							:	Optional[int]=False  ,__UpperCamelCase							:	Dict=None  ,__UpperCamelCase							:	List[Any]=False  ,):
		if cache_dir is None:
				lowerCAmelCase_    :  Optional[Any]             =				TRANSFORMERS_CACHE
		if isinstance(__UpperCamelCase  ,__UpperCamelCase				):
				lowerCAmelCase_    :  List[Any]             =				str(__UpperCamelCase				)

		os.makedirs(__UpperCamelCase  ,exist_ok=__UpperCamelCase				)

		lowerCAmelCase_    :  str             =				None
		if not local_files_only:
				try:
						lowerCAmelCase_    :  List[Any]             =				requests.head(__UpperCamelCase  ,allow_redirects=__UpperCamelCase  ,proxies=__UpperCamelCase  ,timeout=__UpperCamelCase				)
						if response.status_code == 200:
								lowerCAmelCase_    :  int             =				response.headers.get('''ETag'''				)
				except (EnvironmentError, requests.exceptions.Timeout):
						# etag is already None
						pass

		lowerCAmelCase_    :  str             =				url_to_filename(__UpperCamelCase  ,__UpperCamelCase				)

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

		# 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(__UpperCamelCase				):
						return cache_path
				else:
						lowerCAmelCase_    :  int             =				[
						    file
						    for file in fnmatch.filter(os.listdir(__UpperCamelCase				)  ,filename + '''.*'''				)
						    if not file.endswith('''.json'''				) and not file.endswith('''.lock'''				)
						]
						if len(__UpperCamelCase				) > 0:
								return os.path.join(__UpperCamelCase  ,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(__UpperCamelCase				) and not force_download:
				return cache_path

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

				if resume_download:
						lowerCAmelCase_    :  Optional[int]             =				cache_path + '''.incomplete'''

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

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

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

				os.replace(temp_file.name  ,__UpperCamelCase				)

				lowerCAmelCase_    :  str             =				{'''url''': url, '''etag''': etag}
				lowerCAmelCase_    :  Optional[int]             =				cache_path + '''.json'''
				with open(__UpperCamelCase  ,'''w'''				) as meta_file:
						json.dump(__UpperCamelCase  ,__UpperCamelCase				)

		return cache_path


def     UpperCamelCase(						__UpperCamelCase							:	Any  ,__UpperCamelCase							:	Tuple=None				):
		lowerCAmelCase_    :  str             =				url.encode('''utf-8'''				)
		lowerCAmelCase_    :  Tuple             =				shaaaa(__UpperCamelCase				)
		lowerCAmelCase_    :  Optional[int]             =				url_hash.hexdigest()

		if etag:
				lowerCAmelCase_    :  int             =				etag.encode('''utf-8'''				)
				lowerCAmelCase_    :  Tuple             =				shaaaa(__UpperCamelCase				)
				filename += "." + etag_hash.hexdigest()

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

		return filename


def     UpperCamelCase(						__UpperCamelCase							:	int  ,__UpperCamelCase							:	Optional[Any]=None  ,__UpperCamelCase							:	Optional[Any]=False  ,__UpperCamelCase							:	Optional[Any]=None  ,__UpperCamelCase							:	Union[str, Any]=False  ,__UpperCamelCase							:	Any=None  ,__UpperCamelCase							:	Dict=False  ,__UpperCamelCase							:	Optional[Any]=False  ,__UpperCamelCase							:	List[Any]=False  ,):
		if cache_dir is None:
				lowerCAmelCase_    :  int             =				TRANSFORMERS_CACHE
		if isinstance(__UpperCamelCase  ,__UpperCamelCase				):
				lowerCAmelCase_    :  Union[str, Any]             =				str(__UpperCamelCase				)
		if isinstance(__UpperCamelCase  ,__UpperCamelCase				):
				lowerCAmelCase_    :  List[str]             =				str(__UpperCamelCase				)

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

		if extract_compressed_file:
				if not is_zipfile(__UpperCamelCase				) and not tarfile.is_tarfile(__UpperCamelCase				):
						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/"
				lowerCAmelCase_      ,						lowerCAmelCase_    :  Optional[Any]             =				os.path.split(__UpperCamelCase				)
				lowerCAmelCase_    :  Any             =				output_file.replace('''.'''  ,'''-'''				) + '''-extracted'''
				lowerCAmelCase_    :  Dict             =				os.path.join(__UpperCamelCase  ,__UpperCamelCase				)

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

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

				return output_path_extracted

		return output_path


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


def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]				):
		lowerCAmelCase_    :  List[str]             =				requests.get(__UpperCamelCase				)
		lowerCAmelCase_    :  Any             =				np.array(Image.open(BytesIO(response.content				)				)				)
		return img


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


def     UpperCamelCase(						):
		print(f"""{os.path.abspath(os.path.join(__UpperCamelCase  ,os.pardir				)				)}/demo.ipynb"""				)


def     UpperCamelCase(						__UpperCamelCase							:	str  ,__UpperCamelCase							:	List[str]="RGB"				):
		assert isinstance(__UpperCamelCase  ,__UpperCamelCase				)
		if os.path.isfile(__UpperCamelCase				):
				lowerCAmelCase_    :  Optional[Any]             =				cva.imread(__UpperCamelCase				)
		else:
				lowerCAmelCase_    :  List[Any]             =				get_image_from_url(__UpperCamelCase				)
				assert img is not None, f"""could not connect to: {im}"""
		lowerCAmelCase_    :  Union[str, Any]             =				cva.cvtColor(__UpperCamelCase  ,cva.COLOR_BGR2RGB				)
		if input_format == "RGB":
				lowerCAmelCase_    :  Union[str, Any]             =				img[:, :, ::-1]
		return img





def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]  ,__UpperCamelCase							:	List[str]=1				):
		return (images[i : i + batch] for i in range(0  ,len(__UpperCamelCase				)  ,__UpperCamelCase				))










from __future__ import annotations

from typing import Generic, TypeVar

A__						:						Dict          =      TypeVar('''T''')



class        __snake_case		(  Generic[T]		):




							def __init__( self  : List[Any]       ,       A_  : T):
									lowerCAmelCase_    :  Optional[Any]             =				data  # Assign data
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize next as null
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize prev as null





class        __snake_case		(  Generic[T]		):




							def __init__( self  : Union[str, Any]):
									lowerCAmelCase_    :  Node[T] | None             =				None




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : T):
									if self.head is None:
											lowerCAmelCase_    :  List[Any]             =				Node(A_)
									else:
											lowerCAmelCase_    :  Tuple             =				Node(A_)
											lowerCAmelCase_    :  Optional[int]             =				new_node
											lowerCAmelCase_    :  Optional[int]             =				self.head
											lowerCAmelCase_    :  Optional[Any]             =				None
											lowerCAmelCase_    :  Dict             =				new_node




							def      UpperCAmelCase__    ( self  : List[str]):
									if self.head is None:
											return None
									else:
											assert self.head is not None
											lowerCAmelCase_    :  Tuple             =				self.head.data
											lowerCAmelCase_    :  Any             =				self.head.next
											if self.head is not None:
													lowerCAmelCase_    :  Optional[Any]             =				None
											return temp




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									return self.head.data if self.head is not None else None




							def __len__( self  : Dict):
									lowerCAmelCase_    :  List[str]             =				self.head
									lowerCAmelCase_    :  str             =				0
									while temp is not None:
											count += 1
											lowerCAmelCase_    :  Union[str, Any]             =				temp.next
									return count




							def      UpperCAmelCase__    ( self  : Dict):
									return self.head is None







							def      UpperCAmelCase__    ( self  : Dict):
									print('''stack elements are:''')
									lowerCAmelCase_    :  Optional[Any]             =				self.head
									while temp is not None:
											print(temp.data       ,       end='''->''')
											lowerCAmelCase_    :  List[str]             =				temp.next


# Code execution starts here
if __name__ == "__main__":
						# Start with the empty stack
						A__						:						Stack[int]          =      Stack()

						# Insert 4 at the beginning. So stack becomes 4->None
						print('''Stack operations using Doubly LinkedList''')
						stack.push(4)

						# Insert 5 at the beginning. So stack becomes 4->5->None
						stack.push(5)

						# Insert 6 at the beginning. So stack becomes 4->5->6->None
						stack.push(6)

						# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
						stack.push(7)

						# Print the stack
						stack.print_stack()

						# Print the top element
						print('''\nTop element is ''', stack.top())

						# Print the stack size
						print('''Size of the stack is ''', len(stack))

						# pop the top element
						stack.pop()

						# pop the top element
						stack.pop()

						# two elements have now been popped off
						stack.print_stack()

						# Print True if the stack is empty else False
						print('''\nstack is empty:''', stack.is_empty())





