



'''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

						_UpperCamelCase											=			True
except ImportError:
						_UpperCamelCase											=			False


try:
						from torch.hub import _get_torch_home

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

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

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


def   lowercase_	(   lowerCAmelCase__	:  Optional[int]=OBJECTS			,							lowerCAmelCase__	:  Optional[int]=ATTRIBUTES ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     []
			with open(__lowerCamelCase ) as f:
						for object in f.readlines():
									vg_classes.append(object.split(""",""" )[0].lower().strip() )

			__UpperCAmelCase   :      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__	:  Any ):

			"""simple docstring"""





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



class _A  :
		_SCREAMING_SNAKE_CASE					:    Tuple						      =			{}





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase = "root" ,       __UpperCAmelCase=0				)       ->     int:

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

					__UpperCAmelCase   :      List[Any]										=     d





		def __repr__(     self				)       ->     Tuple:

					'''simple docstring'''
					return str(list((self._pointer.keys())				)				)





		def __setattr__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     val
					__UpperCAmelCase   :      Dict										=     val
					__UpperCAmelCase   :      str										=     key.split("""."""				)
					__UpperCAmelCase   :      Optional[Any]										=     len(_lowerCAmelCase				) - 1
					__UpperCAmelCase   :      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:
														__UpperCAmelCase   :      Tuple										=     val
											else:
														__UpperCAmelCase   :      str										=     pointer[l]





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

					'''simple docstring'''
					return self._pointer





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     List[str]:

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





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Dict:

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





		@staticmethod
		def 							__A       (     __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					with open(_lowerCAmelCase				) as stream:
								__UpperCAmelCase   :      Union[str, Any]										=     load(_lowerCAmelCase ,       Loader=_lowerCAmelCase				)
					return data





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

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     """    """
					if self._name != "root":
								__UpperCAmelCase   :      List[Any]										=     f'{t * (self._level-1)}{self._name}:\n'
					else:
								__UpperCAmelCase   :      str										=     """"""
					__UpperCAmelCase   :      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'
								__UpperCAmelCase   :      Union[str, Any]										=     level
					return r[:-1]





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       **__UpperCAmelCase				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     cls.get_config_dict(_lowerCAmelCase ,       **_lowerCAmelCase				)
					return cls(_lowerCAmelCase				)





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       **__UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     kwargs.pop("""cache_dir""" ,       _lowerCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     kwargs.pop("""force_download""" ,       _lowerCAmelCase				)
					__UpperCAmelCase   :      List[Any]										=     kwargs.pop("""resume_download""" ,       _lowerCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     kwargs.pop("""proxies""" ,       _lowerCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     kwargs.pop("""local_files_only""" ,       _lowerCAmelCase				)

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

					try:
								# Load from URL or cache if already cached
								__UpperCAmelCase   :      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

								__UpperCAmelCase   :      Union[str, Any]										=     Config.load_yaml(_lowerCAmelCase				)

					except EnvironmentError:
								__UpperCAmelCase   :      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__	:  Optional[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[Any]										=     torch.load("""dump.pt"""			,							map_location=in_tensor.device )
			__UpperCAmelCase   :      Tuple										=     in_tensor.numpy()
			__UpperCAmelCase   :      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__	:  int ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     urlparse(__lowerCamelCase )
			return parsed.scheme in ("http", "https")


def   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Optional[int]=True ):

			"""simple docstring"""





			__UpperCAmelCase   :      Optional[int]										=     CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			__UpperCAmelCase   :      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__	:  List[str]			,							lowerCAmelCase__	:  Tuple			,							lowerCAmelCase__	:  Any=None			,							lowerCAmelCase__	:  List[Any]=0			,							lowerCAmelCase__	:  str=None			,							):

			"""simple docstring"""





			__UpperCAmelCase   :      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
			__UpperCAmelCase   :      Any										=     {"""user-agent""": ua}
			if resume_size > 0:
						__UpperCAmelCase   :      Union[str, Any]										=     """bytes=%d-""" % (resume_size,)
			__UpperCAmelCase   :      str										=     requests.get(__lowerCamelCase			,							stream=__lowerCamelCase			,							proxies=__lowerCamelCase			,							headers=__lowerCamelCase )
			if response.status_code == 416:  # Range not satisfiable
						return
			__UpperCAmelCase   :      int										=     response.headers.get("""Content-Length""" )
			__UpperCAmelCase   :      List[str]										=     resume_size + int(__lowerCamelCase ) if content_length is not None else None
			__UpperCAmelCase   :      Tuple										=     tqdm(
			    unit="""B"""			,							unit_scale=__lowerCamelCase			,							total=__lowerCamelCase			,							initial=__lowerCamelCase			,							desc="""Downloading"""			,							)
			for chunk in response.iter_content(chunk_size=1024 ):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(__lowerCamelCase ) )
									temp_file.write(__lowerCamelCase )
			progress.close()


def   lowercase_	(   lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  int=None			,							lowerCAmelCase__	:  Optional[int]=False			,							lowerCAmelCase__	:  Tuple=None			,							lowerCAmelCase__	:  List[Any]=10			,							lowerCAmelCase__	:  int=False			,							lowerCAmelCase__	:  Optional[int]=None			,							lowerCAmelCase__	:  Any=False			,							):

			"""simple docstring"""





			if cache_dir is None:
						__UpperCAmelCase   :      Optional[Any]										=     TRANSFORMERS_CACHE
			if isinstance(__lowerCamelCase			,							__lowerCamelCase ):
						__UpperCAmelCase   :      Union[str, Any]										=     str(__lowerCamelCase )

			os.makedirs(__lowerCamelCase			,							exist_ok=__lowerCamelCase )

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

			__UpperCAmelCase   :      str										=     url_to_filename(__lowerCamelCase			,							__lowerCamelCase )

			# get cache path to put the file
			__UpperCAmelCase   :      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:
									__UpperCAmelCase   :      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.
			__UpperCAmelCase   :      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:
									__UpperCAmelCase   :      Union[str, Any]										=     cache_path + """.incomplete"""

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

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

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

			return cache_path


def   lowercase_	(   lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  Optional[Any]=None ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[Any]										=     url.encode("""utf-8""" )
			__UpperCAmelCase   :      List[str]										=     shaaaa(__lowerCamelCase )
			__UpperCAmelCase   :      Optional[Any]										=     url_hash.hexdigest()

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

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

			return filename


def   lowercase_	(   lowerCAmelCase__	:  Union[str, Any]			,							lowerCAmelCase__	:  int=None			,							lowerCAmelCase__	:  str=False			,							lowerCAmelCase__	:  int=None			,							lowerCAmelCase__	:  Any=False			,							lowerCAmelCase__	:  Any=None			,							lowerCAmelCase__	:  Optional[int]=False			,							lowerCAmelCase__	:  Any=False			,							lowerCAmelCase__	:  Any=False			,							):

			"""simple docstring"""





			if cache_dir is None:
						__UpperCAmelCase   :      List[str]										=     TRANSFORMERS_CACHE
			if isinstance(__lowerCamelCase			,							__lowerCamelCase ):
						__UpperCAmelCase   :      Optional[Any]										=     str(__lowerCamelCase )
			if isinstance(__lowerCamelCase			,							__lowerCamelCase ):
						__UpperCAmelCase   :      int										=     str(__lowerCamelCase )

			if is_remote_url(__lowerCamelCase ):
						# URL, so get it from the cache (downloading if necessary)
						__UpperCAmelCase   :      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.
						__UpperCAmelCase   :      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/"
						__UpperCAmelCase   :      Optional[int]										=     os.path.split(__lowerCamelCase )
						__UpperCAmelCase   :      Any										=     output_file.replace("""."""			,							"""-""" ) + """-extracted"""
						__UpperCAmelCase   :      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
						__UpperCAmelCase   :      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 ):
												__UpperCAmelCase   :      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__	:  Dict			,							lowerCAmelCase__	:  Optional[Any]="," ):

			"""simple docstring"""





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


def   lowercase_	(   lowerCAmelCase__	:  Dict ):

			"""simple docstring"""





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


def   lowercase_	(   lowerCAmelCase__	:  Optional[Any] ):

			"""simple docstring"""





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


def   lowercase_	(   ):

			"""simple docstring"""





			print(f'{os.path.abspath(os.path.join(__lowerCamelCase			,							os.pardir ) )}/demo.ipynb' )


def   lowercase_	(   lowerCAmelCase__	:  Optional[int]			,							lowerCAmelCase__	:  Union[str, Any]="RGB" ):

			"""simple docstring"""





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






def   lowercase_	(   lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  str=1 ):

			"""simple docstring"""





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









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





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

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

					if not self.min_threshold < threshold < self.max_threshold:
								__UpperCAmelCase   :      str										=     f'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     input_img
					__UpperCAmelCase   :      Optional[Any]										=     threshold
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					__UpperCAmelCase   :      Optional[Any]										=     [
					    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
					]
					__UpperCAmelCase   :      Any										=     np.ones((self.width, self.height, 3) ,       np.uinta				) * 255





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error				)
											self.error_table[y][x + 2] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x] += int(8 / 32 * current_error				)
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					_UpperCamelCase											=			[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 126, 130, 140)
					]

					for burkes in burkes_instances:
										burkes.process()

					for burkes in burkes_instances:
										imshow(
										    F'Original image with dithering threshold: {burkes.threshold}',
										    burkes.output_img,
										)

					waitKey(0)
					destroyAllWindows()




