


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase__			:      List[Any]								={
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Union[str, Any]								=[
				    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SwiftFormerForImageClassification',
				    'SwiftFormerModel',
				    'SwiftFormerPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_swiftformer import (
				    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwiftFormerConfig,
				    SwiftFormerOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swiftformer import (
								    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwiftFormerForImageClassification,
								    SwiftFormerModel,
								    SwiftFormerPreTrainedModel,
								)

else:
				import sys

				lowerCAmelCase__			:      List[str]								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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

				lowerCAmelCase__			:      Optional[Any]								=True
except ImportError:
				lowerCAmelCase__			:      List[Any]								=False


try:
				from torch.hub import _get_torch_home

				lowerCAmelCase__			:      Optional[int]								=_get_torch_home()
except ImportError:
				lowerCAmelCase__			:      Dict								=os.path.expanduser(
				    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
				)

lowerCAmelCase__			:      int								=os.path.join(torch_cache_home, 'transformers')

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






def 						a__			(				A__=OBJECTS,      A__=ATTRIBUTES				):
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[]
			with open(A__				) as f:
						for object in f.readlines():
									vg_classes.append(object.split(','				)[0].lower().strip()				)

			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[]
			with open(A__				) as f:
						for object in f.readlines():
									vg_attrs.append(object.split(','				)[0].lower().strip()				)
			return vg_classes, vg_attrs






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







class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						{}






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = "root"			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					name
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					level
										SCREAMING_SNAKE_CASE_		:     Any					=					{}
										for k, v in dictionary.items():
													if v is None:
																raise ValueError()
													SCREAMING_SNAKE_CASE_		:     str					=					copy.deepcopy(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					copy.deepcopy(lowerCAmelCase__       )
													if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ):
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					Config(lowerCAmelCase__			,						name=lowerCAmelCase__			,						level=level + 1       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					v
													setattr(self			,						lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					d






							def __repr__(      self       ):





										"""simple docstring"""




										return str(list((self._pointer.keys())       )       )






							def __setattr__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					val
										SCREAMING_SNAKE_CASE_		:     Tuple					=					val
										SCREAMING_SNAKE_CASE_		:     List[str]					=					key.split('.'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					len(lowerCAmelCase__       ) - 1
										SCREAMING_SNAKE_CASE_		:     str					=					self._pointer
										if len(lowerCAmelCase__       ) > 1:
													for i, l in enumerate(lowerCAmelCase__       ):
																if hasattr(self			,						lowerCAmelCase__       ) and isinstance(getattr(self			,						lowerCAmelCase__       )			,						lowerCAmelCase__       ):
																			setattr(getattr(self			,						lowerCAmelCase__       )			,						'.'.join(levels[i:]       )			,						lowerCAmelCase__       )
																if l == last_level:
																			SCREAMING_SNAKE_CASE_		:     Dict					=					val
																else:
																			SCREAMING_SNAKE_CASE_		:     int					=					pointer[l]






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self._pointer






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										with open(F'''{file_name}'''			,						'w'       ) as stream:
													dump(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										with open(F'''{file_name}'''			,						'w'       ) as stream:
													json.dump(lowerCAmelCase__			,						lowerCAmelCase__       )






							@staticmethod
							def 					UpperCamelCase__     (      lowerCAmelCase__       ):





										"""simple docstring"""




										with open(lowerCAmelCase__       ) as stream:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					load(lowerCAmelCase__			,						Loader=lowerCAmelCase__       )
										return data






							def __str__(      self       ):





										"""simple docstring"""




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






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Tuple					=					cls.get_config_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )
										return cls(lowerCAmelCase__       )






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					kwargs.pop('cache_dir'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					kwargs.pop('force_download'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					kwargs.pop('resume_download'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					kwargs.pop('proxies'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					kwargs.pop('local_files_only'			,						lowerCAmelCase__       )

										if os.path.isdir(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     List[str]					=					os.path.join(lowerCAmelCase__			,						lowerCAmelCase__       )
										elif os.path.isfile(lowerCAmelCase__       ) or is_remote_url(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     str					=					pretrained_model_name_or_path
										else:
													SCREAMING_SNAKE_CASE_		:     Any					=					hf_bucket_url(lowerCAmelCase__			,						filename=lowerCAmelCase__			,						use_cdn=lowerCAmelCase__       )

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

													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					Config.load_yaml(lowerCAmelCase__       )

										except EnvironmentError:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					'Can\'t load config for'
													raise EnvironmentError(lowerCAmelCase__       )

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

										return Config.load_yaml(lowerCAmelCase__       ), kwargs







def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     str					=					torch.load('dump.pt',      map_location=in_tensor.device				)
			SCREAMING_SNAKE_CASE_		:     int					=					in_tensor.numpy()
			SCREAMING_SNAKE_CASE_		:     Any					=					out_tensor.numpy()[0]
			print(na.shape,      na[0, 0, :5]				)
			print(na.shape,      na[0, 0, :5]				)
			assert np.allclose(A__,      A__,      rtol=0.01,      atol=0.1				), (
			    F'''{sum([1 for x in np.isclose(A__,      A__,      rtol=0.01,      atol=0.1				).flatten() if x is False]				)/len(na.flatten()				)*1_0_0:.4f} %'''
			    " element-wise mismatch"
			)
			raise Exception('tensors are all good'				)

			# Hugging face functions below






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     int					=					urlparse(A__				)
			return parsed.scheme in ("http", "https")






def 						a__			(				A__,      A__,      A__=True				):
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'/' not in model_id
			if legacy_format:
						return F'''{endpoint}/{model_id}-{filename}'''
			else:
						return F'''{endpoint}/{model_id}/{filename}'''






def 						a__			(				A__,      A__,      A__=None,      A__=0,      A__=None,      ):
			SCREAMING_SNAKE_CASE_		:     Any					=					'python/{}'.format(sys.version.split()[0]				)
			if _torch_available:
						ua += "; torch/{}".format(torch.__version__				)
			if isinstance(A__,      A__				):
						ua += "; " + "; ".join('{}/{}'.format(A__,      A__				) for k, v in user_agent.items()				)
			elif isinstance(A__,      A__				):
						ua += "; " + user_agent
			SCREAMING_SNAKE_CASE_		:     str					=					{'user-agent': ua}
			if resume_size > 0:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					'bytes=%d-' % (resume_size,)
			SCREAMING_SNAKE_CASE_		:     Dict					=					requests.get(A__,      stream=A__,      proxies=A__,      headers=A__				)
			if response.status_code == 4_1_6:  # Range not satisfiable
						return
			SCREAMING_SNAKE_CASE_		:     int					=					response.headers.get('Content-Length'				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					resume_size + int(A__				) if content_length is not None else None
			SCREAMING_SNAKE_CASE_		:     List[str]					=					tqdm(
			    unit='B',      unit_scale=A__,      total=A__,      initial=A__,      desc='Downloading',      )
			for chunk in response.iter_content(chunk_size=1_0_2_4				):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(A__				)				)
									temp_file.write(A__				)
			progress.close()






def 						a__			(				A__,      A__=None,      A__=False,      A__=None,      A__=1_0,      A__=False,      A__=None,      A__=False,      ):
			if cache_dir is None:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					TRANSFORMERS_CACHE
			if isinstance(A__,      A__				):
						SCREAMING_SNAKE_CASE_		:     int					=					str(A__				)

			os.makedirs(A__,      exist_ok=A__				)

			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None
			if not local_files_only:
						try:
									SCREAMING_SNAKE_CASE_		:     Any					=					requests.head(A__,      allow_redirects=A__,      proxies=A__,      timeout=A__				)
									if response.status_code == 2_0_0:
												SCREAMING_SNAKE_CASE_		:     Any					=					response.headers.get('ETag'				)
						except (EnvironmentError, requests.exceptions.Timeout):
									# etag is already None
									pass

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					url_to_filename(A__,      A__				)

			# get cache path to put the file
			SCREAMING_SNAKE_CASE_		:     int					=					os.path.join(A__,      A__				)

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

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

						if resume_download:
									SCREAMING_SNAKE_CASE_		:     List[str]					=					cache_path + '.incomplete'

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

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

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

						os.replace(temp_file.name,      A__				)

						SCREAMING_SNAKE_CASE_		:     Tuple					=					{'url': url, 'etag': etag}
						SCREAMING_SNAKE_CASE_		:     int					=					cache_path + '.json'
						with open(A__,      'w'				) as meta_file:
									json.dump(A__,      A__				)

			return cache_path






def 						a__			(				A__,      A__=None				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					url.encode('utf-8'				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					shaaaa(A__				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					url_hash.hexdigest()

			if etag:
						SCREAMING_SNAKE_CASE_		:     int					=					etag.encode('utf-8'				)
						SCREAMING_SNAKE_CASE_		:     Any					=					shaaaa(A__				)
						filename += "." + etag_hash.hexdigest()

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

			return filename






def 						a__			(				A__,      A__=None,      A__=False,      A__=None,      A__=False,      A__=None,      A__=False,      A__=False,      A__=False,      ):
			if cache_dir is None:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					TRANSFORMERS_CACHE
			if isinstance(A__,      A__				):
						SCREAMING_SNAKE_CASE_		:     Tuple					=					str(A__				)
			if isinstance(A__,      A__				):
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					str(A__				)

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

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

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

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

						return output_path_extracted

			return output_path






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






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					requests.get(A__				)
			SCREAMING_SNAKE_CASE_		:     str					=					np.array(Image.open(BytesIO(response.content				)				)				)
			return img






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






def 						a__			(				):
			print(F'''{os.path.abspath(os.path.join(A__,      os.pardir				)				)}/demo.ipynb'''				)






def 						a__			(				A__,      A__="RGB"				):
			assert isinstance(A__,      A__				)
			if os.path.isfile(A__				):
						SCREAMING_SNAKE_CASE_		:     Tuple					=					cva.imread(A__				)
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					get_image_from_url(A__				)
						assert img is not None, F'''could not connect to: {im}'''
			SCREAMING_SNAKE_CASE_		:     Tuple					=					cva.cvtColor(A__,      cva.COLOR_BGR2RGB				)
			if input_format == "RGB":
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					img[:, :, ::-1]
			return img






def 						a__			(				A__,      A__=1				):
			return (images[i : i + batch] for i in range(0,      len(A__				),      A__				))





