




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case   :   Union[str, Any]     							=	logging.get_logger(__name__)

_snake_case   :   str     							=	{
    'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
    'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}

class     A					(	_a					):
							lowercase_           =       'markuplm'







							def __init__(						self :						List[str]							,    lowerCAmelCase_ :						str=3_05_22							,    lowerCAmelCase_ :						List[Any]=7_68							,    lowerCAmelCase_ :						List[str]=12							,    lowerCAmelCase_ :						Union[str, Any]=12							,    lowerCAmelCase_ :						List[str]=30_72							,    lowerCAmelCase_ :						Tuple="gelu"							,    lowerCAmelCase_ :						str=0.1							,    lowerCAmelCase_ :						Optional[Any]=0.1							,    lowerCAmelCase_ :						str=5_12							,    lowerCAmelCase_ :						Optional[Any]=2							,    lowerCAmelCase_ :						Dict=0.0_2							,    lowerCAmelCase_ :						Union[str, Any]=1e-12							,    lowerCAmelCase_ :						Dict=0							,    lowerCAmelCase_ :						Tuple=0							,    lowerCAmelCase_ :						Union[str, Any]=2							,    lowerCAmelCase_ :						Optional[Any]=2_56							,    lowerCAmelCase_ :						List[Any]=10_24							,    lowerCAmelCase_ :						int=2_16							,    lowerCAmelCase_ :						List[str]=10_01							,    lowerCAmelCase_ :						Optional[Any]=32							,    lowerCAmelCase_ :						str=50							,    lowerCAmelCase_ :						Optional[int]="absolute"							,    lowerCAmelCase_ :						Dict=True							,    lowerCAmelCase_ :						Any=None							,    **lowerCAmelCase_ :						List[str]							,    )    ->							List[Any]:


														"""simple docstring"""


														super().__init__(
														    pad_token_id=lowerCAmelCase_							,    bos_token_id=lowerCAmelCase_							,    eos_token_id=lowerCAmelCase_							,    **lowerCAmelCase_							,    )
														_a			  =       vocab_size
														_a			  =       hidden_size
														_a			  =       num_hidden_layers
														_a			  =       num_attention_heads
														_a			  =       hidden_act
														_a			  =       intermediate_size
														_a			  =       hidden_dropout_prob
														_a			  =       attention_probs_dropout_prob
														_a			  =       max_position_embeddings
														_a			  =       type_vocab_size
														_a			  =       initializer_range
														_a			  =       layer_norm_eps
														_a			  =       position_embedding_type
														_a			  =       use_cache
														_a			  =       classifier_dropout
														# additional properties
														_a			  =       max_depth
														_a			  =       max_xpath_tag_unit_embeddings
														_a			  =       max_xpath_subs_unit_embeddings
														_a			  =       tag_pad_id
														_a			  =       subs_pad_id
														_a			  =       xpath_unit_hidden_size







'''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

				_snake_case   :   Any     							=	True
except ImportError:
				_snake_case   :   List[str]     							=	False


try:
				from torch.hub import _get_torch_home

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

_snake_case   :   Tuple     							=	os.path.join(torch_cache_home, 'transformers')

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





							'''simple docstring'''


							_a			  =       []
							with open(UpperCamelCase   ) as f:
														for object in f.readlines():
																					vg_classes.append(object.split(''','''   )[0].lower().strip()   )

							_a			  =       []
							with open(UpperCamelCase   ) as f:
														for object in f.readlines():
																					vg_attrs.append(object.split(''','''   )[0].lower().strip()   )
							return vg_classes, vg_attrs
def 	snake_case_					(UpperCamelCase :						str   ):





							'''simple docstring'''


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

class     A					:
							lowercase_           =       {}







							def __init__(						self :						List[Any]							,    lowerCAmelCase_ :						dict							,    lowerCAmelCase_ :						str = "root"							,    lowerCAmelCase_ :						List[str]=0      )    ->							int:


														"""simple docstring"""


														_a			  =       name
														_a			  =       level
														_a			  =       {}
														for k, v in dictionary.items():
																					if v is None:
																												raise ValueError()
																					_a			  =       copy.deepcopy(lowerCAmelCase_      )
																					_a			  =       copy.deepcopy(lowerCAmelCase_      )
																					if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																												_a			  =       Config(lowerCAmelCase_							,    name=lowerCAmelCase_							,    level=level + 1      )
																					_a			  =       v
																					setattr(self							,    lowerCAmelCase_							,    lowerCAmelCase_      )

														_a			  =       d







							def __repr__(						self :						Union[str, Any]      )    ->							str:


														"""simple docstring"""


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







							def __setattr__(						self :						Dict							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Any      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       val
														_a			  =       val
														_a			  =       key.split('''.'''      )
														_a			  =       len(lowerCAmelCase_      ) - 1
														_a			  =       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:
																																			_a			  =       val
																												else:
																																			_a			  =       pointer[l]







							def      __lowerCAmelCase    (						self :						str      )    ->							Dict:


														"""simple docstring"""


														return self._pointer







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						Dict      )    ->							int:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Tuple      )    ->							Optional[Any]:


														"""simple docstring"""


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







							@staticmethod
							def      __lowerCAmelCase    (						lowerCAmelCase_ :						List[str]      )    ->							List[Any]:


														"""simple docstring"""


														with open(lowerCAmelCase_      ) as stream:
																					_a			  =       load(lowerCAmelCase_							,    Loader=lowerCAmelCase_      )
														return data







							def __str__(						self :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														_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(lowerCAmelCase_							,    lowerCAmelCase_      ):
																												r += F'{t * (self._level)}{v}\n'
																												self._level += 1
																					else:
																												r += F'{t * (self._level)}{k}: {v} ({type(lowerCAmelCase_      ).__name__})\n'
																					_a			  =       level
														return r[:-1]







							@classmethod
							def      __lowerCAmelCase    (						cls :						Dict							,    lowerCAmelCase_ :						str							,    **lowerCAmelCase_ :						Dict      )    ->							str:


														"""simple docstring"""


														_a  ,							_a			  =       cls.get_config_dict(lowerCAmelCase_							,    **lowerCAmelCase_      )
														return cls(lowerCAmelCase_      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						Tuple							,    lowerCAmelCase_ :						str							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														_a			  =       kwargs.pop('''cache_dir'''							,    lowerCAmelCase_      )
														_a			  =       kwargs.pop('''force_download'''							,    lowerCAmelCase_      )
														_a			  =       kwargs.pop('''resume_download'''							,    lowerCAmelCase_      )
														_a			  =       kwargs.pop('''proxies'''							,    lowerCAmelCase_      )
														_a			  =       kwargs.pop('''local_files_only'''							,    lowerCAmelCase_      )

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

														try:
																					# Load from URL or cache if already cached
																					_a			  =       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

																					_a			  =       Config.load_yaml(lowerCAmelCase_      )

														except EnvironmentError:
																					_a			  =       '''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 	snake_case_					(UpperCamelCase :						List[Any]   ):





							'''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(UpperCamelCase				,		UpperCamelCase				,		rtol=0.01				,		atol=0.1   ), (
							    f'{sum([1 for x in np.isclose(UpperCamelCase				,		UpperCamelCase				,		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 	snake_case_					(UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       urlparse(UpperCamelCase   )
							return parsed.scheme in ("http", "https")
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						str				,		UpperCamelCase :						int=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 	snake_case_					(UpperCamelCase :						Dict				,		UpperCamelCase :						int				,		UpperCamelCase :						List[str]=None				,		UpperCamelCase :						str=0				,		UpperCamelCase :						List[Any]=None				,		):





							'''simple docstring'''


							_a			  =       '''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
							_a			  =       {'''user-agent''': ua}
							if resume_size > 0:
														_a			  =       '''bytes=%d-''' % (resume_size,)
							_a			  =       requests.get(UpperCamelCase				,		stream=UpperCamelCase				,		proxies=UpperCamelCase				,		headers=UpperCamelCase   )
							if response.status_code == 416:  # Range not satisfiable
														return
							_a			  =       response.headers.get('''Content-Length'''   )
							_a			  =       resume_size + int(UpperCamelCase   ) if content_length is not None else None
							_a			  =       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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						Optional[Any]=None				,		UpperCamelCase :						Optional[int]=False				,		UpperCamelCase :						Optional[int]=None				,		UpperCamelCase :						Any=10				,		UpperCamelCase :						int=False				,		UpperCamelCase :						int=None				,		UpperCamelCase :						Any=False				,		):





							'''simple docstring'''


							if cache_dir is None:
														_a			  =       TRANSFORMERS_CACHE
							if isinstance(UpperCamelCase				,		UpperCamelCase   ):
														_a			  =       str(UpperCamelCase   )

							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )

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

							_a			  =       url_to_filename(UpperCamelCase				,		UpperCamelCase   )

							# get cache path to put the file
							_a			  =       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:
																					_a			  =       [
																					    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.
							_a			  =       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:
																					_a			  =       cache_path + '''.incomplete'''

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

																					_a			  =       _resumable_file_manager
																					if os.path.exists(UpperCamelCase   ):
																												_a			  =       os.stat(UpperCamelCase   ).st_size
																					else:
																												_a			  =       0
														else:
																					_a			  =       partial(tempfile.NamedTemporaryFile				,		dir=UpperCamelCase				,		delete=UpperCamelCase   )
																					_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'''				,		UpperCamelCase				,		temp_file.name				,		)

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

														os.replace(temp_file.name				,		UpperCamelCase   )

														_a			  =       {'''url''': url, '''etag''': etag}
														_a			  =       cache_path + '''.json'''
														with open(UpperCamelCase				,		'''w'''   ) as meta_file:
																					json.dump(UpperCamelCase				,		UpperCamelCase   )

							return cache_path
def 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						List[str]=None   ):





							'''simple docstring'''


							_a			  =       url.encode('''utf-8'''   )
							_a			  =       shaaaa(UpperCamelCase   )
							_a			  =       url_hash.hexdigest()

							if etag:
														_a			  =       etag.encode('''utf-8'''   )
														_a			  =       shaaaa(UpperCamelCase   )
														filename += "." + etag_hash.hexdigest()

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

							return filename
def 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						Dict=None				,		UpperCamelCase :						Tuple=False				,		UpperCamelCase :						List[Any]=None				,		UpperCamelCase :						Union[str, Any]=False				,		UpperCamelCase :						List[Any]=None				,		UpperCamelCase :						Dict=False				,		UpperCamelCase :						List[str]=False				,		UpperCamelCase :						Tuple=False				,		):





							'''simple docstring'''


							if cache_dir is None:
														_a			  =       TRANSFORMERS_CACHE
							if isinstance(UpperCamelCase				,		UpperCamelCase   ):
														_a			  =       str(UpperCamelCase   )
							if isinstance(UpperCamelCase				,		UpperCamelCase   ):
														_a			  =       str(UpperCamelCase   )

							if is_remote_url(UpperCamelCase   ):
														# URL, so get it from the cache (downloading if necessary)
														_a			  =       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.
														_a			  =       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/"
														_a  ,							_a			  =       os.path.split(UpperCamelCase   )
														_a			  =       output_file.replace('''.'''				,		'''-'''   ) + '''-extracted'''
														_a			  =       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
														_a			  =       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   ):
																												_a			  =       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 	snake_case_					(UpperCamelCase :						int				,		UpperCamelCase :						List[Any]=","   ):





							'''simple docstring'''


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





							'''simple docstring'''


							_a			  =       requests.get(UpperCamelCase   )
							_a			  =       np.array(Image.open(BytesIO(response.content   )   )   )
							return img
def 	snake_case_					(UpperCamelCase :						int   ):





							'''simple docstring'''


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





							'''simple docstring'''


							print(f'{os.path.abspath(os.path.join(UpperCamelCase				,		os.pardir   )   )}/demo.ipynb'   )
def 	snake_case_					(UpperCamelCase :						Any				,		UpperCamelCase :						Optional[Any]="RGB"   ):





							'''simple docstring'''


							assert isinstance(UpperCamelCase				,		UpperCamelCase   )
							if os.path.isfile(UpperCamelCase   ):
														_a			  =       cva.imread(UpperCamelCase   )
							else:
														_a			  =       get_image_from_url(UpperCamelCase   )
														assert img is not None, f'could not connect to: {im}'
							_a			  =       cva.cvtColor(UpperCamelCase				,		cva.COLOR_BGR2RGB   )
							if input_format == "RGB":
														_a			  =       img[:, :, ::-1]
							return img

def 	snake_case_					(UpperCamelCase :						List[Any]				,		UpperCamelCase :						Any=1   ):





							'''simple docstring'''


							return (images[i : i + batch] for i in range(0				,		len(UpperCamelCase   )				,		UpperCamelCase   ))

