






"""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							:   Tuple	  =       True
except ImportError:
						_snake_case							:   Optional[int]	  =       False


try:
						from torch.hub import _get_torch_home

						_snake_case							:   Dict	  =       _get_torch_home()
except ImportError:
						_snake_case							:   Tuple	  =       os.path.expanduser(
						    os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
						)

_snake_case							:   Any	  =       os.path.join(torch_cache_home, "transformers")

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




def     lowerCamelCase_					(	__A	:		Dict=OBJECTS      ,						__A	:		str=ATTRIBUTES      ):
					lowerCamelCase__					=[]
					with open(__SCREAMING_SNAKE_CASE      ) as f:
										for object in f.readlines():
															vg_classes.append(object.split(','      )[0].lower().strip()      )

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




def     lowerCamelCase_					(	__A	:		Union[str, Any]      ):
					lowerCamelCase__					=OrderedDict()
					with open(__SCREAMING_SNAKE_CASE      ,						'rb'      ) as f:
										lowerCamelCase__					=pkl.load(__SCREAMING_SNAKE_CASE      )["""model"""]
					for k in copy.deepcopy(list(ckp.keys()      )      ):
										lowerCamelCase__					=ckp.pop(__SCREAMING_SNAKE_CASE      )
										if isinstance(__SCREAMING_SNAKE_CASE      ,						np.ndarray      ):
															lowerCamelCase__					=torch.tensor(__SCREAMING_SNAKE_CASE      )
										else:
															assert isinstance(__SCREAMING_SNAKE_CASE      ,						torch.tensor      ), type(__SCREAMING_SNAKE_CASE      )
										lowerCamelCase__					=v
					return r




class 							__lowercase:
						lowerCAmelCase_		:				List[str]=							{}






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = "root"     ,    _SCREAMING_SNAKE_CASE=0)      ->	Union[str, Any]:




											"""simple docstring"""





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

											lowerCamelCase__					=d






						def __repr__(       self)      ->	Optional[int]:




											"""simple docstring"""





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






						def __setattr__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Dict:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											return self._pointer






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	List[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Optional[int]:




											"""simple docstring"""





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






						@staticmethod
						def 		lowerCAmelCase (       _SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





											with open(_lowerCAmelCase) as stream:
																lowerCamelCase__					=load(_lowerCAmelCase     ,    Loader=_lowerCAmelCase)
											return data






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




											"""simple docstring"""





											lowerCamelCase__					="""    """
											if self._name != "root":
																lowerCamelCase__					=F"{t * (self._level-1)}{self._name}:\n"
											else:
																lowerCamelCase__					=""""""
											lowerCamelCase__					=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"
																lowerCamelCase__					=level
											return r[:-1]






						@classmethod
						def 		lowerCAmelCase (       cls     ,    _SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=cls.get_config_dict(_lowerCAmelCase     ,    **_lowerCAmelCase)
											return cls(_lowerCAmelCase)






						@classmethod
						def 		lowerCAmelCase (       cls     ,    _SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	int:




											"""simple docstring"""





											lowerCamelCase__					=kwargs.pop('cache_dir'     ,    _lowerCAmelCase)
											lowerCamelCase__					=kwargs.pop('force_download'     ,    _lowerCAmelCase)
											lowerCamelCase__					=kwargs.pop('resume_download'     ,    _lowerCAmelCase)
											lowerCamelCase__					=kwargs.pop('proxies'     ,    _lowerCAmelCase)
											lowerCamelCase__					=kwargs.pop('local_files_only'     ,    _lowerCAmelCase)

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

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

																lowerCamelCase__					=Config.load_yaml(_lowerCAmelCase)

											except EnvironmentError:
																lowerCamelCase__					="""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     lowerCamelCase_					(	__A	:		str      ):
					lowerCamelCase__					=torch.load('dump.pt'      ,						map_location=in_tensor.device      )
					lowerCamelCase__					=in_tensor.numpy()
					lowerCamelCase__					=out_tensor.numpy()[0]
					print(na.shape      ,						na[0, 0, :5]      )
					print(na.shape      ,						na[0, 0, :5]      )
					assert np.allclose(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      ,						rtol=0.0_1      ,						atol=0.1      ), (
					    f"{sum([1 for x in np.isclose(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      ,						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     lowerCamelCase_					(	__A	:		List[Any]      ):
					lowerCamelCase__					=urlparse(__SCREAMING_SNAKE_CASE      )
					return parsed.scheme in ("http", "https")




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		str      ,						__A	:		str=True      ):
					lowerCamelCase__					=CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
					lowerCamelCase__					="""/""" not in model_id
					if legacy_format:
										return f"{endpoint}/{model_id}-{filename}"
					else:
										return f"{endpoint}/{model_id}/{filename}"




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




def     lowerCamelCase_					(	__A	:		List[Any]      ,						__A	:		Optional[Any]=None      ,						__A	:		Union[str, Any]=False      ,						__A	:		Optional[int]=None      ,						__A	:		Optional[int]=10      ,						__A	:		int=False      ,						__A	:		str=None      ,						__A	:		Dict=False      ,						):
					if cache_dir is None:
										lowerCamelCase__					=TRANSFORMERS_CACHE
					if isinstance(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      ):
										lowerCamelCase__					=str(__SCREAMING_SNAKE_CASE      )

					os.makedirs(__SCREAMING_SNAKE_CASE      ,						exist_ok=__SCREAMING_SNAKE_CASE      )

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

					lowerCamelCase__					=url_to_filename(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      )

					# get cache path to put the file
					lowerCamelCase__					=os.path.join(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      )

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

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

										if resume_download:
															lowerCamelCase__					=cache_path + """.incomplete"""

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

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

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

										os.replace(temp_file.name      ,						__SCREAMING_SNAKE_CASE      )

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

					return cache_path




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		List[Any]=None      ):
					lowerCamelCase__					=url.encode('utf-8'      )
					lowerCamelCase__					=shaaaa(__SCREAMING_SNAKE_CASE      )
					lowerCamelCase__					=url_hash.hexdigest()

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

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

					return filename




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		Optional[int]=None      ,						__A	:		int=False      ,						__A	:		Any=None      ,						__A	:		str=False      ,						__A	:		Optional[Any]=None      ,						__A	:		Tuple=False      ,						__A	:		int=False      ,						__A	:		Tuple=False      ,						):
					if cache_dir is None:
										lowerCamelCase__					=TRANSFORMERS_CACHE
					if isinstance(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      ):
										lowerCamelCase__					=str(__SCREAMING_SNAKE_CASE      )
					if isinstance(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      ):
										lowerCamelCase__					=str(__SCREAMING_SNAKE_CASE      )

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

					if extract_compressed_file:
										if not is_zipfile(__SCREAMING_SNAKE_CASE      ) and not tarfile.is_tarfile(__SCREAMING_SNAKE_CASE      ):
															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__					=os.path.split(__SCREAMING_SNAKE_CASE      )
										lowerCamelCase__					=output_file.replace('.'      ,						'-'      ) + """-extracted"""
										lowerCamelCase__					=os.path.join(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      )

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

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

										return output_path_extracted

					return output_path




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




def     lowerCamelCase_					(	__A	:		Optional[Any]      ):
					lowerCamelCase__					=requests.get(__SCREAMING_SNAKE_CASE      )
					lowerCamelCase__					=np.array(Image.open(BytesIO(response.content      )      )      )
					return img




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




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




def     lowerCamelCase_					(	__A	:		Any      ,						__A	:		Optional[int]="RGB"      ):
					assert isinstance(__SCREAMING_SNAKE_CASE      ,						__SCREAMING_SNAKE_CASE      )
					if os.path.isfile(__SCREAMING_SNAKE_CASE      ):
										lowerCamelCase__					=cva.imread(__SCREAMING_SNAKE_CASE      )
					else:
										lowerCamelCase__					=get_image_from_url(__SCREAMING_SNAKE_CASE      )
										assert img is not None, f"could not connect to: {im}"
					lowerCamelCase__					=cva.cvtColor(__SCREAMING_SNAKE_CASE      ,						cva.COLOR_BGR2RGB      )
					if input_format == "RGB":
										lowerCamelCase__					=img[:, :, ::-1]
					return img




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		List[Any]=1      ):
					return (images[i : i + batch] for i in range(0      ,						len(__SCREAMING_SNAKE_CASE      )      ,						__SCREAMING_SNAKE_CASE      ))
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[Any]          =	MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       A							:	int          =	"mobilebert"

       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Union[str, Any]=3_05_22			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Dict=24			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Optional[Any]=5_12			,			_lowerCAmelCase					:     Union[str, Any]="relu"			,			_lowerCAmelCase					:     List[str]=0.0			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Tuple=5_12			,			_lowerCAmelCase					:     Optional[Any]=2			,			_lowerCAmelCase					:     Optional[int]=0.02			,			_lowerCAmelCase					:     List[str]=1e-12			,			_lowerCAmelCase					:     int=0			,			_lowerCAmelCase					:     Union[str, Any]=1_28			,			_lowerCAmelCase					:     Optional[Any]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Any=1_28			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=True			,			_lowerCAmelCase					:     Tuple=4			,			_lowerCAmelCase					:     List[str]="no_norm"			,			_lowerCAmelCase					:     Union[str, Any]=True			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     int			,			):
           super().__init__(pad_token_id=_lowerCAmelCase			,			**_lowerCAmelCase			)

           __snake_case       :				Any 			=    vocab_size
           __snake_case       :				Tuple 			=    hidden_size
           __snake_case       :				int 			=    num_hidden_layers
           __snake_case       :				Dict 			=    num_attention_heads
           __snake_case       :				List[Any] 			=    hidden_act
           __snake_case       :				List[Any] 			=    intermediate_size
           __snake_case       :				Union[str, Any] 			=    hidden_dropout_prob
           __snake_case       :				Any 			=    attention_probs_dropout_prob
           __snake_case       :				Any 			=    max_position_embeddings
           __snake_case       :				List[Any] 			=    type_vocab_size
           __snake_case       :				List[Any] 			=    initializer_range
           __snake_case       :				Dict 			=    layer_norm_eps
           __snake_case       :				Any 			=    embedding_size
           __snake_case       :				List[str] 			=    trigram_input
           __snake_case       :				Union[str, Any] 			=    use_bottleneck
           __snake_case       :				List[Any] 			=    intra_bottleneck_size
           __snake_case       :				Tuple 			=    use_bottleneck_attention
           __snake_case       :				Optional[Any] 			=    key_query_shared_bottleneck
           __snake_case       :				Tuple 			=    num_feedforward_networks
           __snake_case       :				int 			=    normalization_type
           __snake_case       :				List[Any] 			=    classifier_activation

           if self.use_bottleneck:
               __snake_case       :				List[str] 			=    intra_bottleneck_size
           else:
               __snake_case       :				int 			=    hidden_size

           __snake_case       :				Optional[Any] 			=    classifier_dropout







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @property
       def 			snake_case__    (							self					:     Optional[Any]			):
           if self.task == "multiple-choice":
               __snake_case       :				Any 			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
               __snake_case       :				Optional[Any] 			=    {0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]			)



