






"""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 				snake_case      (   A__=OBJECTS	,A__=ATTRIBUTES						):
			UpperCAmelCase_     : Optional[int]			     =					[]
			with open(A__						) as f:
						for object in f.readlines():
									vg_classes.append(object.split(","						)[0].lower().strip()						)

			UpperCAmelCase_     : Dict			     =					[]
			with open(A__						) as f:
						for object in f.readlines():
									vg_attrs.append(object.split(","						)[0].lower().strip()						)
			return vg_classes, vg_attrs

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







class 			UpperCamelCase_       :
	__magic_name__	      =				{}





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

				UpperCAmelCase_     : str			     =					d





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





	def __setattr__(      self					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: List[Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : Optional[Any]			     =					val
				UpperCAmelCase_     : List[str]			     =					val
				UpperCAmelCase_     : Optional[int]			     =					key.split("."						)
				UpperCAmelCase_     : Union[str, Any]			     =					len(lowerCAmelCase_						) - 1
				UpperCAmelCase_     : 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:
													UpperCAmelCase_     : str			     =					val
										else:
													UpperCAmelCase_     : int			     =					pointer[l]





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Any:
				return self._pointer





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Dict						)						->			Dict:
				with open(f"""{file_name}"""	,	"w"						) as stream:
							dump(lowerCAmelCase_	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: List[str]						)						->			Any:
				with open(f"""{file_name}"""	,	"w"						) as stream:
							json.dump(lowerCAmelCase_	,	lowerCAmelCase_						)





	@staticmethod
	def      _SCREAMING_SNAKE_CASE      (      lowerCAmelCase_					: Any						)						->			Optional[Any]:
				with open(lowerCAmelCase_						) as stream:
							UpperCAmelCase_     : Any			     =					load(lowerCAmelCase_	,	Loader=lowerCAmelCase_						)
				return data





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





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: int	,	lowerCAmelCase_					: str	,	**lowerCAmelCase_					: str						)						->			int:
				UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					cls.get_config_dict(lowerCAmelCase_	,	**lowerCAmelCase_						)
				return cls(lowerCAmelCase_						)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: str	,	lowerCAmelCase_					: str	,	**lowerCAmelCase_					: List[str]						)						->			int:
				UpperCAmelCase_     : int			     =					kwargs.pop("cache_dir"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : List[str]			     =					kwargs.pop("force_download"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[int]			     =					kwargs.pop("resume_download"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					kwargs.pop("proxies"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Any			     =					kwargs.pop("local_files_only"	,	lowerCAmelCase_						)

				if os.path.isdir(lowerCAmelCase_						):
							UpperCAmelCase_     : Dict			     =					os.path.join(lowerCAmelCase_	,	lowerCAmelCase_						)
				elif os.path.isfile(lowerCAmelCase_						) or is_remote_url(lowerCAmelCase_						):
							UpperCAmelCase_     : Optional[Any]			     =					pretrained_model_name_or_path
				else:
							UpperCAmelCase_     : str			     =					hf_bucket_url(lowerCAmelCase_	,	filename=lowerCAmelCase_	,	use_cdn=lowerCAmelCase_						)

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

							UpperCAmelCase_     : Optional[Any]			     =					Config.load_yaml(lowerCAmelCase_						)

				except EnvironmentError:
							UpperCAmelCase_     : 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 				snake_case      (   A__						):
			UpperCAmelCase_     : Dict			     =					torch.load("dump.pt"	,map_location=in_tensor.device						)
			UpperCAmelCase_     : Tuple			     =					in_tensor.numpy()
			UpperCAmelCase_     : Any			     =					out_tensor.numpy()[0]
			print(na.shape	,na[0, 0, :5]						)
			print(na.shape	,na[0, 0, :5]						)
			assert np.allclose(A__	,A__	,rtol=0.01	,atol=0.1						), (
			    F"""{sum([1 for x in np.isclose(A__	,A__	,rtol=0.01	,atol=0.1						).flatten() if x is False]						)/len(na.flatten()						)*1_00:.4f} %"""
			    " element-wise mismatch"
			)
			raise Exception("tensors are all good"						)

			# Hugging face functions below

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Any			     =					urlparse(A__						)
			return parsed.scheme in ("http", "https")

def 				snake_case      (   A__	,A__	,A__=True						):
			UpperCAmelCase_     : Optional[int]			     =					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			UpperCAmelCase_     : Dict			     =					"/" not in model_id
			if legacy_format:
						return F"""{endpoint}/{model_id}-{filename}"""
			else:
						return F"""{endpoint}/{model_id}/{filename}"""

def 				snake_case      (   A__	,A__	,A__=None	,A__=0	,A__=None	,):
			UpperCAmelCase_     : Any			     =					"python/{}".format(sys.version.split()[0]						)
			if _torch_available:
						ua += "; torch/{}".format(torch.__version__						)
			if isinstance(A__	,A__						):
						ua += "; " + "; ".join("{}/{}".format(A__	,A__						) for k, v in user_agent.items()						)
			elif isinstance(A__	,A__						):
						ua += "; " + user_agent
			UpperCAmelCase_     : Any			     =					{"user-agent": ua}
			if resume_size > 0:
						UpperCAmelCase_     : Tuple			     =					"bytes=%d-" % (resume_size,)
			UpperCAmelCase_     : Optional[int]			     =					requests.get(A__	,stream=A__	,proxies=A__	,headers=A__						)
			if response.status_code == 4_16:  # Range not satisfiable
						return
			UpperCAmelCase_     : Union[str, Any]			     =					response.headers.get("Content-Length"						)
			UpperCAmelCase_     : List[Any]			     =					resume_size + int(A__						) if content_length is not None else None
			UpperCAmelCase_     : Tuple			     =					tqdm(
			    unit="B"	,unit_scale=A__	,total=A__	,initial=A__	,desc="Downloading"	,)
			for chunk in response.iter_content(chunk_size=10_24						):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(A__						)						)
									temp_file.write(A__						)
			progress.close()

def 				snake_case      (   A__	,A__=None	,A__=False	,A__=None	,A__=10	,A__=False	,A__=None	,A__=False	,):
			if cache_dir is None:
						UpperCAmelCase_     : Dict			     =					TRANSFORMERS_CACHE
			if isinstance(A__	,A__						):
						UpperCAmelCase_     : Any			     =					str(A__						)

			os.makedirs(A__	,exist_ok=A__						)

			UpperCAmelCase_     : Optional[Any]			     =					None
			if not local_files_only:
						try:
									UpperCAmelCase_     : Dict			     =					requests.head(A__	,allow_redirects=A__	,proxies=A__	,timeout=A__						)
									if response.status_code == 2_00:
												UpperCAmelCase_     : Optional[Any]			     =					response.headers.get("ETag"						)
						except (EnvironmentError, requests.exceptions.Timeout):
									# etag is already None
									pass

			UpperCAmelCase_     : Tuple			     =					url_to_filename(A__	,A__						)

			# get cache path to put the file
			UpperCAmelCase_     : List[str]			     =					os.path.join(A__	,A__						)

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

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

						if resume_download:
									UpperCAmelCase_     : Optional[Any]			     =					cache_path + ".incomplete"

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

									UpperCAmelCase_     : List[Any]			     =					_resumable_file_manager
									if os.path.exists(A__						):
												UpperCAmelCase_     : Dict			     =					os.stat(A__						).st_size
									else:
												UpperCAmelCase_     : Optional[int]			     =					0
						else:
									UpperCAmelCase_     : List[Any]			     =					partial(tempfile.NamedTemporaryFile	,dir=A__	,delete=A__						)
									UpperCAmelCase_     : Dict			     =					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"	,A__	,temp_file.name	,)

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

						os.replace(temp_file.name	,A__						)

						UpperCAmelCase_     : Any			     =					{"url": url, "etag": etag}
						UpperCAmelCase_     : int			     =					cache_path + ".json"
						with open(A__	,"w"						) as meta_file:
									json.dump(A__	,A__						)

			return cache_path

def 				snake_case      (   A__	,A__=None						):
			UpperCAmelCase_     : List[str]			     =					url.encode("utf-8"						)
			UpperCAmelCase_     : List[Any]			     =					shaaaa(A__						)
			UpperCAmelCase_     : Dict			     =					url_hash.hexdigest()

			if etag:
						UpperCAmelCase_     : Dict			     =					etag.encode("utf-8"						)
						UpperCAmelCase_     : Any			     =					shaaaa(A__						)
						filename += "." + etag_hash.hexdigest()

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

			return filename

def 				snake_case      (   A__	,A__=None	,A__=False	,A__=None	,A__=False	,A__=None	,A__=False	,A__=False	,A__=False	,):
			if cache_dir is None:
						UpperCAmelCase_     : Dict			     =					TRANSFORMERS_CACHE
			if isinstance(A__	,A__						):
						UpperCAmelCase_     : Dict			     =					str(A__						)
			if isinstance(A__	,A__						):
						UpperCAmelCase_     : Dict			     =					str(A__						)

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

			if extract_compressed_file:
						if not is_zipfile(A__						) and not tarfile.is_tarfile(A__						):
									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_     ,	UpperCAmelCase_     : Any			     =					os.path.split(A__						)
						UpperCAmelCase_     : Tuple			     =					output_file.replace("."	,"-"						) + "-extracted"
						UpperCAmelCase_     : List[Any]			     =					os.path.join(A__	,A__						)

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

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

						return output_path_extracted

			return output_path

def 				snake_case      (   A__	,A__=","						):
			assert isinstance(A__	,A__						)
			if os.path.isfile(A__						):
						with open(A__						) as f:
									UpperCAmelCase_     : str			     =					eval(f.read()						)
			else:
						UpperCAmelCase_     : int			     =					requests.get(A__						)
						try:
									UpperCAmelCase_     : int			     =					requests.json()
						except Exception:
									UpperCAmelCase_     : Dict			     =					req.content.decode()
									assert data is not None, "could not connect"
									try:
												UpperCAmelCase_     : Tuple			     =					eval(A__						)
									except Exception:
												UpperCAmelCase_     : Tuple			     =					data.split("\n"						)
						req.close()
			return data

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Tuple			     =					requests.get(A__						)
			UpperCAmelCase_     : List[Any]			     =					np.array(Image.open(BytesIO(response.content						)						)						)
			return img

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

def 				snake_case      (   ):
			print(F"""{os.path.abspath(os.path.join(A__	,os.pardir						)						)}/demo.ipynb"""						)

def 				snake_case      (   A__	,A__="RGB"						):
			assert isinstance(A__	,A__						)
			if os.path.isfile(A__						):
						UpperCAmelCase_     : Tuple			     =					cva.imread(A__						)
			else:
						UpperCAmelCase_     : Tuple			     =					get_image_from_url(A__						)
						assert img is not None, F"""could not connect to: {im}"""
			UpperCAmelCase_     : Any			     =					cva.cvtColor(A__	,cva.COLOR_BGR2RGB						)
			if input_format == "RGB":
						UpperCAmelCase_     : Tuple			     =					img[:, :, ::-1]
			return img




def 				snake_case      (   A__	,A__=1						):
			return (images[i : i + batch] for i in range(0	,len(A__						)	,A__						))











"""simple docstring"""
def 				snake_case      (   A__	,A__						):
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			UpperCAmelCase_     : Dict			     =					(boundary[1] - boundary[0]) / steps
			UpperCAmelCase_     : Optional[int]			     =					boundary[0]
			UpperCAmelCase_     : str			     =					boundary[1]
			UpperCAmelCase_     : Tuple			     =					make_points(A__	,A__	,A__						)
			UpperCAmelCase_     : List[str]			     =					0.0
			y += (h / 2.0) * f(A__						)
			for i in x_i:
						# print(i)
						y += h * f(A__						)
			y += (h / 2.0) * f(A__						)
			return y

def 				snake_case      (   A__	,A__	,A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					a + h
			while x < (b - h):
						yield x
						UpperCAmelCase_     : Optional[Any]			     =					x + h

def 				snake_case      (   A__						):  # enter your function here
			UpperCAmelCase_     : Dict			     =					(x - 0) * (x - 0)
			return y

def 				snake_case      (   ):
			UpperCAmelCase_     : Dict			     =					0.0  # Lower bound of integration
			UpperCAmelCase_     : Optional[int]			     =					1.0  # Upper bound of integration
			UpperCAmelCase_     : Dict			     =					10.0  # define number of steps or resolution
			UpperCAmelCase_     : List[Any]			     =					[a, b]  # define boundary of integration
			UpperCAmelCase_     : Union[str, Any]			     =					method_a(A__	,A__						)
			print(F"""y = {y}"""						)


if __name__ == "__main__":
			main()



