


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	:     List[str]   =True
except ImportError:
						_UpperCAmelCase	:     Optional[Any]   =False


try:
						from torch.hub import _get_torch_home

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

_UpperCAmelCase	:     Union[str, Any]   =os.path.join(torch_cache_home, """transformers""")

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



def 	lowerCAmelCase       (						lowerCAmelCase_=OBJECTS       ,      lowerCAmelCase_=ATTRIBUTES       )->       str:
				lowerCAmelCase_       :					List[str]			 = []
				with open(lowerCAmelCase_       ) as f:
								for object in f.readlines():
												vg_classes.append(object.split(''','''       )[0].lower().strip()       )

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



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







class  snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     List[Any]    =							{}




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

										lowerCAmelCase_       :					int			 = d




						def __repr__( self							)    ->			List[Any]:
										return str(list((self._pointer.keys())							)							)




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




						def   lowercase_       ( self							)    ->			int:
										return self._pointer




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




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




						def   lowercase_       ( self      ,  __lowercase      ,  __lowercase							)    ->			int:




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




						@staticmethod
						def   lowercase_       ( __lowercase							)    ->			Optional[Any]:




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




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




						@classmethod
						def   lowercase_       ( cls      ,  __lowercase      ,  **__lowercase							)    ->			int:
										lowerCAmelCase_       :					Dict			 = cls.get_config_dict(lowerCAmelCase__      ,  **lowerCAmelCase__							)
										return cls(lowerCAmelCase__							)




						@classmethod
						def   lowercase_       ( cls      ,  __lowercase      ,  **__lowercase							)    ->			List[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = kwargs.pop('''cache_dir'''      ,  lowerCAmelCase__							)
										lowerCAmelCase_       :					List[str]			 = kwargs.pop('''force_download'''      ,  lowerCAmelCase__							)
										lowerCAmelCase_       :					int			 = kwargs.pop('''resume_download'''      ,  lowerCAmelCase__							)
										lowerCAmelCase_       :					Optional[Any]			 = kwargs.pop('''proxies'''      ,  lowerCAmelCase__							)
										lowerCAmelCase_       :					Any			 = kwargs.pop('''local_files_only'''      ,  lowerCAmelCase__							)

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

										try:
														# Load from URL or cache if already cached
														lowerCAmelCase_       :					Optional[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

														lowerCAmelCase_       :					Tuple			 = Config.load_yaml(lowerCAmelCase__							)

										except EnvironmentError:
														lowerCAmelCase_       :					Union[str, 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 	lowerCAmelCase       (						lowerCAmelCase_       )->       Union[str, Any]:
				lowerCAmelCase_       :					List[Any]			 = torch.load('''dump.pt'''       ,      map_location=in_tensor.device       )
				lowerCAmelCase_       :					Any			 = in_tensor.numpy()
				lowerCAmelCase_       :					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 	lowerCAmelCase       (						lowerCAmelCase_       )->       Optional[int]:
				lowerCAmelCase_       :					Dict			 = urlparse(lowerCAmelCase_       )
				return parsed.scheme in ("http", "https")



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_=True       )->       Union[str, Any]:
				lowerCAmelCase_       :					Optional[int]			 = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				lowerCAmelCase_       :					int			 = "/" not in model_id
				if legacy_format:
								return f"""{endpoint}/{model_id}-{filename}"""
				else:
								return f"""{endpoint}/{model_id}/{filename}"""



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



				if cache_dir is None:
								lowerCAmelCase_       :					int			 = TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase_       ,      lowerCAmelCase_       ):
								lowerCAmelCase_       :					Tuple			 = str(lowerCAmelCase_       )

				os.makedirs(lowerCAmelCase_       ,      exist_ok=lowerCAmelCase_       )

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

				lowerCAmelCase_       :					List[Any]			 = url_to_filename(lowerCAmelCase_       ,      lowerCAmelCase_       )

				# get cache path to put the file
				lowerCAmelCase_       :					str			 = 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:
												lowerCAmelCase_       :					Dict			 = [
												    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.
				lowerCAmelCase_       :					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:
												lowerCAmelCase_       :					Any			 = cache_path + ".incomplete"

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

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

								# Download to temporary file, then copy to cache dir once finished.
								# Otherwise you get corrupt cache entries if the download gets interrupted.
								with temp_file_manager() as temp_file:
												print(
												    '''%s not found in cache or force_download set to True, downloading to %s'''       ,      lowerCAmelCase_       ,      temp_file.name       ,      )

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

								os.replace(temp_file.name       ,      lowerCAmelCase_       )

								lowerCAmelCase_       :					str			 = {"url": url, "etag": etag}
								lowerCAmelCase_       :					List[Any]			 = cache_path + ".json"
								with open(lowerCAmelCase_       ,      '''w'''       ) as meta_file:
												json.dump(lowerCAmelCase_       ,      lowerCAmelCase_       )

				return cache_path



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_=None       )->       List[Any]:
				lowerCAmelCase_       :					Dict			 = url.encode('''utf-8'''       )
				lowerCAmelCase_       :					Dict			 = shaaaa(lowerCAmelCase_       )
				lowerCAmelCase_       :					int			 = url_hash.hexdigest()

				if etag:
								lowerCAmelCase_       :					Optional[int]			 = etag.encode('''utf-8'''       )
								lowerCAmelCase_       :					List[str]			 = shaaaa(lowerCAmelCase_       )
								filename += "." + etag_hash.hexdigest()

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

				return filename



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_=None       ,      lowerCAmelCase_=False       ,      lowerCAmelCase_=None       ,      lowerCAmelCase_=False       ,      lowerCAmelCase_=None       ,      lowerCAmelCase_=False       ,      lowerCAmelCase_=False       ,      lowerCAmelCase_=False       ,      )->       List[Any]:



				if cache_dir is None:
								lowerCAmelCase_       :					Union[str, Any]			 = TRANSFORMERS_CACHE
				if isinstance(lowerCAmelCase_       ,      lowerCAmelCase_       ):
								lowerCAmelCase_       :					int			 = str(lowerCAmelCase_       )
				if isinstance(lowerCAmelCase_       ,      lowerCAmelCase_       ):
								lowerCAmelCase_       :					Dict			 = str(lowerCAmelCase_       )

				if is_remote_url(lowerCAmelCase_       ):
								# URL, so get it from the cache (downloading if necessary)
								lowerCAmelCase_       :					List[Any]			 = 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.
								lowerCAmelCase_       :					Tuple			 = 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/"
								lowerCAmelCase_       :					List[Any]			 = os.path.split(lowerCAmelCase_       )
								lowerCAmelCase_       :					int			 = output_file.replace('''.'''       ,      '''-'''       ) + "-extracted"
								lowerCAmelCase_       :					str			 = 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
								lowerCAmelCase_       :					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_       ):
																lowerCAmelCase_       :					Optional[Any]			 = 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 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_=","       )->       Optional[Any]:
				assert isinstance(lowerCAmelCase_       ,      lowerCAmelCase_       )
				if os.path.isfile(lowerCAmelCase_       ):
								with open(lowerCAmelCase_       ) as f:
												lowerCAmelCase_       :					str			 = eval(f.read()       )
				else:
								lowerCAmelCase_       :					Tuple			 = requests.get(lowerCAmelCase_       )
								try:
												lowerCAmelCase_       :					List[Any]			 = requests.json()
								except Exception:
												lowerCAmelCase_       :					Optional[Any]			 = req.content.decode()
												assert data is not None, "could not connect"
												try:
																lowerCAmelCase_       :					List[Any]			 = eval(lowerCAmelCase_       )
												except Exception:
																lowerCAmelCase_       :					int			 = data.split('''\n'''       )
								req.close()
				return data



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       List[str]:
				lowerCAmelCase_       :					Optional[int]			 = requests.get(lowerCAmelCase_       )
				lowerCAmelCase_       :					Optional[int]			 = np.array(Image.open(BytesIO(response.content       )       )       )
				return img



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



def 	lowerCAmelCase       (						)->       Union[str, Any]:
				print(f"""{os.path.abspath(os.path.join(lowerCAmelCase_       ,      os.pardir       )       )}/demo.ipynb"""       )



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_="RGB"       )->       Dict:
				assert isinstance(lowerCAmelCase_       ,      lowerCAmelCase_       )
				if os.path.isfile(lowerCAmelCase_       ):
								lowerCAmelCase_       :					Optional[Any]			 = cva.imread(lowerCAmelCase_       )
				else:
								lowerCAmelCase_       :					Union[str, Any]			 = get_image_from_url(lowerCAmelCase_       )
								assert img is not None, f"""could not connect to: {im}"""
				lowerCAmelCase_       :					Optional[Any]			 = cva.cvtColor(lowerCAmelCase_       ,      cva.COLOR_BGR2RGB       )
				if input_format == "RGB":
								lowerCAmelCase_       :					str			 = img[:, :, ::-1]
				return img




def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_=1       )->       int:
				return (images[i : i + batch] for i in range(0       ,      len(lowerCAmelCase_       )       ,      lowerCAmelCase_       ))






def      _A       (		SCREAMING_SNAKE_CASE     :   int = 50						):





  """simple docstring"""



  a__	:					Any										=[1] * (length + 1)

  for row_length in range(3       ,       length + 1						):
    for block_length in range(3       ,       row_length + 1						):
      for block_start in range(row_length - block_length						):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
     print(F"""{solution() = }""")






