






"""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

			lowerCAmelCase__							= True
except ImportError:
			lowerCAmelCase__							= False


try:
			from torch.hub import _get_torch_home

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

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

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





def 							lowercase__		(				lowerCamelCase=OBJECTS,	lowerCamelCase=ATTRIBUTES					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						[]
				with open(__lowerCamelCase					) as f:
								for object in f.readlines():
												vg_classes.append(object.split(','					)[0].lower().strip()					)

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





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


class _lowerCAmelCase	:
						SCREAMING_SNAKE_CASE_:				Optional[Any]   				=							{}







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

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						d







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







						def __setattr__(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       int:
										_SCREAMING_SNAKE_CASE      :		Any							=						val
										_SCREAMING_SNAKE_CASE      :		Dict							=						val
										_SCREAMING_SNAKE_CASE      :		str							=						key.split('.'       )
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						len(_lowerCAmelCase       ) - 1
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						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      :		Tuple							=						val
																		else:
																						_SCREAMING_SNAKE_CASE      :		str							=						pointer[l]







						def  A			(						self       )		->       List[Any]:
										return self._pointer







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       Dict:
										with open(F"""{file_name}"""      ,							'w'       ) as stream:
														dump(_lowerCAmelCase      ,							_lowerCAmelCase       )







						def  A			(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_       )		->       str:
										with open(F"""{file_name}"""      ,							'w'       ) as stream:
														json.dump(_lowerCAmelCase      ,							_lowerCAmelCase       )







						@staticmethod
						def  A			(						lowerCAmelCase_       )		->       Union[str, Any]:
										with open(_lowerCAmelCase       ) as stream:
														_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						load(_lowerCAmelCase      ,							Loader=_lowerCAmelCase       )
										return data







						def __str__(						self       )		->       int:
										_SCREAMING_SNAKE_CASE      :		str							=						"""    """
										if self._name != "root":
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						F"""{t * (self._level-1)}{self._name}:\n"""
										else:
														_SCREAMING_SNAKE_CASE      :		str							=						""""""
										_SCREAMING_SNAKE_CASE      :		str							=						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      :		Union[str, Any]							=						level
										return r[:-1]







						@classmethod
						def  A			(						cls      ,							lowerCAmelCase_      ,							**lowerCAmelCase_       )		->       str:
										_SCREAMING_SNAKE_CASE      :		Tuple							=						cls.get_config_dict(_lowerCAmelCase      ,							**_lowerCAmelCase       )
										return cls(_lowerCAmelCase       )







						@classmethod
						def  A			(						cls      ,							lowerCAmelCase_      ,							**lowerCAmelCase_       )		->       Any:
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						kwargs.pop('cache_dir'      ,							_lowerCAmelCase       )
										_SCREAMING_SNAKE_CASE      :		Tuple							=						kwargs.pop('force_download'      ,							_lowerCAmelCase       )
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						kwargs.pop('resume_download'      ,							_lowerCAmelCase       )
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						kwargs.pop('proxies'      ,							_lowerCAmelCase       )
										_SCREAMING_SNAKE_CASE      :		Tuple							=						kwargs.pop('local_files_only'      ,							_lowerCAmelCase       )

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

										try:
														# Load from URL or cache if already cached
														_SCREAMING_SNAKE_CASE      :		List[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      :		Union[str, Any]							=						Config.load_yaml(_lowerCAmelCase       )

										except EnvironmentError:
														_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						"""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 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						torch.load('dump.pt',	map_location=in_tensor.device					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						in_tensor.numpy()
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						out_tensor.numpy()[0]
				print(na.shape,	na[0, 0, :5]					)
				print(na.shape,	na[0, 0, :5]					)
				assert np.allclose(__lowerCamelCase,	__lowerCamelCase,	rtol=0.01,	atol=0.1					), (
				    f"""{sum([1 for x in np.isclose(__lowerCamelCase,	__lowerCamelCase,	rtol=0.01,	atol=0.1					).flatten() if x is False]					)/len(na.flatten()					)*100:.4f} %"""
				    " element-wise mismatch"
				)
				raise Exception('tensors are all good'					)

				# Hugging face functions below





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Tuple							=						urlparse(__lowerCamelCase					)
				return parsed.scheme in ("http", "https")





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase=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 							lowercase__		(				lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=0,	lowerCamelCase=None,	):
				_SCREAMING_SNAKE_CASE      :		Dict							=						"""python/{}""".format(sys.version.split()[0]					)
				if _torch_available:
								ua += "; torch/{}".format(torch.__version__					)
				if isinstance(__lowerCamelCase,	__lowerCamelCase					):
								ua += "; " + "; ".join('{}/{}'.format(__lowerCamelCase,	__lowerCamelCase					) for k, v in user_agent.items()					)
				elif isinstance(__lowerCamelCase,	__lowerCamelCase					):
								ua += "; " + user_agent
				_SCREAMING_SNAKE_CASE      :		Any							=						{"""user-agent""": ua}
				if resume_size > 0:
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						"""bytes=%d-""" % (resume_size,)
				_SCREAMING_SNAKE_CASE      :		str							=						requests.get(__lowerCamelCase,	stream=__lowerCamelCase,	proxies=__lowerCamelCase,	headers=__lowerCamelCase					)
				if response.status_code == 416:  # Range not satisfiable
								return
				_SCREAMING_SNAKE_CASE      :		int							=						response.headers.get('Content-Length'					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						resume_size + int(__lowerCamelCase					) if content_length is not None else None
				_SCREAMING_SNAKE_CASE      :		Tuple							=						tqdm(
				    unit='B',	unit_scale=__lowerCamelCase,	total=__lowerCamelCase,	initial=__lowerCamelCase,	desc='Downloading',	)
				for chunk in response.iter_content(chunk_size=1_024					):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(__lowerCamelCase					)					)
												temp_file.write(__lowerCamelCase					)
				progress.close()





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=False,	lowerCamelCase=None,	lowerCamelCase=10,	lowerCamelCase=False,	lowerCamelCase=None,	lowerCamelCase=False,	):
				if cache_dir is None:
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						TRANSFORMERS_CACHE
				if isinstance(__lowerCamelCase,	__lowerCamelCase					):
								_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						str(__lowerCamelCase					)

				os.makedirs(__lowerCamelCase,	exist_ok=__lowerCamelCase					)

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

				_SCREAMING_SNAKE_CASE      :		str							=						url_to_filename(__lowerCamelCase,	__lowerCamelCase					)

				# get cache path to put the file
				_SCREAMING_SNAKE_CASE      :		Tuple							=						os.path.join(__lowerCamelCase,	__lowerCamelCase					)

				# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
				# try to get the last downloaded one
				if etag is None:
								if os.path.exists(__lowerCamelCase					):
												return cache_path
								else:
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						[
												    file
												    for file in fnmatch.filter(os.listdir(__lowerCamelCase					),	filename + '.*'					)
												    if not file.endswith('.json'					) and not file.endswith('.lock'					)
												]
												if len(__lowerCamelCase					) > 0:
																return os.path.join(__lowerCamelCase,	matching_files[-1]					)
												else:
																# If files cannot be found and local_files_only=True,
																# the models might've been found if local_files_only=False
																# Notify the user about that
																if local_files_only:
																				raise ValueError(
																				    'Cannot find the requested files in the cached path and outgoing traffic has been'
																				    ' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
																				    ' to False.'					)
																return None

    # From now on, etag is not None.
				if os.path.exists(__lowerCamelCase					) and not force_download:
								return cache_path

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

								if resume_download:
												_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						cache_path + """.incomplete"""

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

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

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

								os.replace(temp_file.name,	__lowerCamelCase					)

								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						{"""url""": url, """etag""": etag}
								_SCREAMING_SNAKE_CASE      :		Any							=						cache_path + """.json"""
								with open(__lowerCamelCase,	'w'					) as meta_file:
												json.dump(__lowerCamelCase,	__lowerCamelCase					)

				return cache_path





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase=None					):
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						url.encode('utf-8'					)
				_SCREAMING_SNAKE_CASE      :		List[str]							=						shaaaa(__lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						url_hash.hexdigest()

				if etag:
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						etag.encode('utf-8'					)
								_SCREAMING_SNAKE_CASE      :		Dict							=						shaaaa(__lowerCamelCase					)
								filename += "." + etag_hash.hexdigest()

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

				return filename





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase=None,	lowerCamelCase=False,	lowerCamelCase=None,	lowerCamelCase=False,	lowerCamelCase=None,	lowerCamelCase=False,	lowerCamelCase=False,	lowerCamelCase=False,	):
				if cache_dir is None:
								_SCREAMING_SNAKE_CASE      :		List[str]							=						TRANSFORMERS_CACHE
				if isinstance(__lowerCamelCase,	__lowerCamelCase					):
								_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						str(__lowerCamelCase					)
				if isinstance(__lowerCamelCase,	__lowerCamelCase					):
								_SCREAMING_SNAKE_CASE      :		int							=						str(__lowerCamelCase					)

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

				if extract_compressed_file:
								if not is_zipfile(__lowerCamelCase					) and not tarfile.is_tarfile(__lowerCamelCase					):
												return output_path

								# Path where we extract compressed archives
								# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
								_SCREAMING_SNAKE_CASE      :		Optional[int]							=						os.path.split(__lowerCamelCase					)
								_SCREAMING_SNAKE_CASE      :		Any							=						output_file.replace('.',	'-'					) + """-extracted"""
								_SCREAMING_SNAKE_CASE      :		Dict							=						os.path.join(__lowerCamelCase,	__lowerCamelCase					)

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

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

								return output_path_extracted

				return output_path





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





def 							lowercase__		(				lowerCamelCase					):
				_SCREAMING_SNAKE_CASE      :		Optional[int]							=						requests.get(__lowerCamelCase					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						np.array(Image.open(BytesIO(response.content					)					)					)
				return img





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





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





def 							lowercase__		(				lowerCamelCase,	lowerCamelCase="RGB"					):
				assert isinstance(__lowerCamelCase,	__lowerCamelCase					)
				if os.path.isfile(__lowerCamelCase					):
								_SCREAMING_SNAKE_CASE      :		Any							=						cva.imread(__lowerCamelCase					)
				else:
								_SCREAMING_SNAKE_CASE      :		Dict							=						get_image_from_url(__lowerCamelCase					)
								assert img is not None, f"""could not connect to: {im}"""
				_SCREAMING_SNAKE_CASE      :		Dict							=						cva.cvtColor(__lowerCamelCase,	cva.COLOR_BGR2RGB					)
				if input_format == "RGB":
								_SCREAMING_SNAKE_CASE      :		Tuple							=						img[:, :, ::-1]
				return img






def 							lowercase__		(				lowerCamelCase,	lowerCamelCase=1					):
				return (images[i : i + batch] for i in range(0,	len(__lowerCamelCase					),	__lowerCamelCase					))









"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCAmelCase__							= logging.getLogger(__name__)


@dataclass
class _lowerCAmelCase	:
						SCREAMING_SNAKE_CASE_:				str   				=							field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
						SCREAMING_SNAKE_CASE_:				bool   				=							field(default=__UpperCAmelCase      ,     metadata={'help': 'Set this flag to use fast tokenization.'}					)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						SCREAMING_SNAKE_CASE_:				Optional[str]   				=							field(
						    default=__UpperCAmelCase      ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,     )







def 							lowercase__		(				):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_SCREAMING_SNAKE_CASE      :		List[Any]							=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)

				if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
				else:
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE      :		List[Any]							=						parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir					)
				    and os.listdir(training_args.output_dir					)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ' --overwrite_output_dir to overcome.'					)

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',	datefmt='%m/%d/%Y %H:%M:%S',	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,	)
				logger.warning(
				    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s',	training_args.local_rank,	training_args.device,	training_args.n_gpu,	bool(training_args.local_rank != -1					),	training_args.fpaa,	)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank					):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('Training/evaluation parameters %s',	lowerCamelCase					)

				# Prepare Question-Answering task
				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	)
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,	cache_dir=model_args.cache_dir,	use_fast=lowerCamelCase,	)
				_SCREAMING_SNAKE_CASE      :		Dict							=						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path,	from_tf=bool('.ckpt' in model_args.model_name_or_path					),	config=lowerCamelCase,	cache_dir=model_args.cache_dir,	)

				# Get datasets
				_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						hasattr(model.config,	'lang2id'					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						(
				    SquadDataset(
				        lowerCamelCase,	tokenizer=lowerCamelCase,	is_language_sensitive=lowerCamelCase,	cache_dir=model_args.cache_dir					)
				    if training_args.do_train
				    else None
				)
				_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						(
				    SquadDataset(
				        lowerCamelCase,	tokenizer=lowerCamelCase,	mode='dev',	is_language_sensitive=lowerCamelCase,	cache_dir=model_args.cache_dir,	)
				    if training_args.do_eval
				    else None
				)

				# Data collator
				_SCREAMING_SNAKE_CASE      :		int							=						DataCollatorWithPadding(lowerCamelCase,	pad_to_multiple_of=8					) if training_args.fpaa else None

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						Trainer(
				    model=lowerCamelCase,	args=lowerCamelCase,	train_dataset=lowerCamelCase,	eval_dataset=lowerCamelCase,	data_collator=lowerCamelCase,	)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir					)





def 							lowercase__		(				lowerCamelCase					):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
			main()

