



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				:							Optional[Any]								=True
except ImportError:
	UpperCAmelCase				:							str								=False


try:
	from torch.hub import _get_torch_home

	UpperCAmelCase				:							List[Any]								=_get_torch_home()
except ImportError:
	UpperCAmelCase				:							Union[str, Any]								=os.path.expanduser(
	    os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
	)

UpperCAmelCase				:							Dict								=os.path.join(torch_cache_home, """transformers""")

UpperCAmelCase				:							List[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				:							int								=os.path.join(PATH, """config.yaml""")
UpperCAmelCase				:							List[Any]								=os.path.join(PATH, """attributes.txt""")
UpperCAmelCase				:							Union[str, Any]								=os.path.join(PATH, """objects.txt""")
UpperCAmelCase				:							int								=os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
UpperCAmelCase				:							List[Any]								=os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
UpperCAmelCase				:							int								=os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
UpperCAmelCase				:							Union[str, Any]								="""pytorch_model.bin"""
UpperCAmelCase				:							Union[str, Any]								="""config.yaml"""



def 				_lowerCAmelCase      (_lowerCAmelCase=OBJECTS				,		_lowerCAmelCase=ATTRIBUTES):
					UpperCamelCase_											=				[]
					with open(_lowerCAmelCase) as f:
										for object in f.readlines():
															vg_classes.append(object.split(",")[0].lower().strip())

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


class  _lowercase				:
				'''simple docstring'''







				lowercase__				  =  {}

				def __init__(	self       ,	snake_case__       ,	snake_case__ = "root"       ,	snake_case__=0      ):

									'''simple docstring'''


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

									UpperCamelCase_											=				d

				def __repr__(	self      ):

									'''simple docstring'''


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

				def __setattr__(	self       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				val
									UpperCamelCase_											=				val
									UpperCamelCase_											=				key.split("."      )
									UpperCamelCase_											=				len(snake_case__      ) - 1
									UpperCamelCase_											=				self._pointer
									if len(snake_case__      ) > 1:
														for i, l in enumerate(snake_case__      ):
																			if hasattr(self       ,	snake_case__      ) and isinstance(getattr(self       ,	snake_case__      )       ,	snake_case__      ):
																								setattr(getattr(self       ,	snake_case__      )       ,	".".join(levels[i:]      )       ,	snake_case__      )
																			if l == last_level:
																								UpperCamelCase_											=				val
																			else:
																								UpperCamelCase_											=				pointer[l]

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									return self._pointer

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


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

				@staticmethod
				def  _lowerCamelCase   (	snake_case__      ):

									'''simple docstring'''


									with open(snake_case__      ) as stream:
														UpperCamelCase_											=				load(snake_case__       ,	Loader=snake_case__      )
									return data

				def __str__(	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				"    "
									if self._name != "root":
														UpperCamelCase_											=				F"""{t * (self._level-1)}{self._name}:\n"""
									else:
														UpperCamelCase_											=				""
									UpperCamelCase_											=				self._level
									for i, (k, v) in enumerate(self._pointer.items()      ):
														if isinstance(snake_case__       ,	snake_case__      ):
																			r += F"""{t * (self._level)}{v}\n"""
																			self._level += 1
														else:
																			r += F"""{t * (self._level)}{k}: {v} ({type(snake_case__      ).__name__})\n"""
														UpperCamelCase_											=				level
									return r[:-1]

				@classmethod
				def  _lowerCamelCase   (	cls       ,	snake_case__       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_											=				cls.get_config_dict(snake_case__       ,	**snake_case__      )
									return cls(snake_case__      )

				@classmethod
				def  _lowerCamelCase   (	cls       ,	snake_case__       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				kwargs.pop("cache_dir"       ,	snake_case__      )
									UpperCamelCase_											=				kwargs.pop("force_download"       ,	snake_case__      )
									UpperCamelCase_											=				kwargs.pop("resume_download"       ,	snake_case__      )
									UpperCamelCase_											=				kwargs.pop("proxies"       ,	snake_case__      )
									UpperCamelCase_											=				kwargs.pop("local_files_only"       ,	snake_case__      )

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

									try:
														# Load from URL or cache if already cached
														UpperCamelCase_											=				cached_path(
														    snake_case__       ,	cache_dir=snake_case__       ,	force_download=snake_case__       ,	proxies=snake_case__       ,	resume_download=snake_case__       ,	local_files_only=snake_case__       ,	)
														# Load config dict
														if resolved_config_file is None:
																			raise EnvironmentError

														UpperCamelCase_											=				Config.load_yaml(snake_case__      )

									except EnvironmentError:
														UpperCamelCase_											=				"Can't load config for"
														raise EnvironmentError(snake_case__      )

									if resolved_config_file == config_file:
														print("loading configuration file from path"      )
									else:
														print("loading configuration file cache"      )

									return Config.load_yaml(snake_case__      ), kwargs




def 				_lowerCAmelCase      (_lowerCAmelCase):
					UpperCamelCase_											=				torch.load("dump.pt"				,		map_location=in_tensor.device)
					UpperCamelCase_											=				in_tensor.numpy()
					UpperCamelCase_											=				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())*1_00:.4f} %"""
					    " element-wise mismatch"
					)
					raise Exception("tensors are all good")

					# Hugging face functions below



def 				_lowerCAmelCase      (_lowerCAmelCase):
					UpperCamelCase_											=				urlparse(_lowerCAmelCase)
					return parsed.scheme in ("http", "https")



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=True):
					UpperCamelCase_											=				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
					UpperCamelCase_											=				"/" 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				,		):
					UpperCamelCase_											=				"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_											=				{"user-agent": ua}
					if resume_size > 0:
										UpperCamelCase_											=				"bytes=%d-" % (resume_size,)
					UpperCamelCase_											=				requests.get(_lowerCAmelCase				,		stream=_lowerCAmelCase				,		proxies=_lowerCAmelCase				,		headers=_lowerCAmelCase)
					if response.status_code == 4_16:  # Range not satisfiable
										return
					UpperCamelCase_											=				response.headers.get("Content-Length")
					UpperCamelCase_											=				resume_size + int(_lowerCAmelCase) if content_length is not None else None
					UpperCamelCase_											=				tqdm(
					    unit="B"				,		unit_scale=_lowerCAmelCase				,		total=_lowerCAmelCase				,		initial=_lowerCAmelCase				,		desc="Downloading"				,		)
					for chunk in response.iter_content(chunk_size=10_24):
										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				,		):
					if cache_dir is None:
										UpperCamelCase_											=				TRANSFORMERS_CACHE
					if isinstance(_lowerCAmelCase				,		_lowerCAmelCase):
										UpperCamelCase_											=				str(_lowerCAmelCase)

					os.makedirs(_lowerCAmelCase				,		exist_ok=_lowerCAmelCase)

					UpperCamelCase_											=				None
					if not local_files_only:
										try:
															UpperCamelCase_											=				requests.head(_lowerCAmelCase				,		allow_redirects=_lowerCAmelCase				,		proxies=_lowerCAmelCase				,		timeout=_lowerCAmelCase)
															if response.status_code == 2_00:
																				UpperCamelCase_											=				response.headers.get("ETag")
										except (EnvironmentError, requests.exceptions.Timeout):
															# etag is already None
															pass

					UpperCamelCase_											=				url_to_filename(_lowerCAmelCase				,		_lowerCAmelCase)

					# get cache path to put the file
					UpperCamelCase_											=				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_											=				[
															    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_											=				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_											=				cache_path + ".incomplete"

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

															UpperCamelCase_											=				_resumable_file_manager
															if os.path.exists(_lowerCAmelCase):
																				UpperCamelCase_											=				os.stat(_lowerCAmelCase).st_size
															else:
																				UpperCamelCase_											=				0
										else:
															UpperCamelCase_											=				partial(tempfile.NamedTemporaryFile				,		dir=_lowerCAmelCase				,		delete=_lowerCAmelCase)
															UpperCamelCase_											=				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_											=				{"url": url, "etag": etag}
										UpperCamelCase_											=				cache_path + ".json"
										with open(_lowerCAmelCase				,		"w") as meta_file:
															json.dump(_lowerCAmelCase				,		_lowerCAmelCase)

					return cache_path



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase=None):
					UpperCamelCase_											=				url.encode("utf-8")
					UpperCamelCase_											=				shaaaa(_lowerCAmelCase)
					UpperCamelCase_											=				url_hash.hexdigest()

					if etag:
										UpperCamelCase_											=				etag.encode("utf-8")
										UpperCamelCase_											=				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				,		):
					if cache_dir is None:
										UpperCamelCase_											=				TRANSFORMERS_CACHE
					if isinstance(_lowerCAmelCase				,		_lowerCAmelCase):
										UpperCamelCase_											=				str(_lowerCAmelCase)
					if isinstance(_lowerCAmelCase				,		_lowerCAmelCase):
										UpperCamelCase_											=				str(_lowerCAmelCase)

					if is_remote_url(_lowerCAmelCase):
										# URL, so get it from the cache (downloading if necessary)
										UpperCamelCase_											=				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_											=				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_					,      UpperCamelCase_											=				os.path.split(_lowerCAmelCase)
										UpperCamelCase_											=				output_file.replace("."				,		"-") + "-extracted"
										UpperCamelCase_											=				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_											=				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_											=				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=","):
					assert isinstance(_lowerCAmelCase				,		_lowerCAmelCase)
					if os.path.isfile(_lowerCAmelCase):
										with open(_lowerCAmelCase) as f:
															UpperCamelCase_											=				eval(f.read())
					else:
										UpperCamelCase_											=				requests.get(_lowerCAmelCase)
										try:
															UpperCamelCase_											=				requests.json()
										except Exception:
															UpperCamelCase_											=				req.content.decode()
															assert data is not None, "could not connect"
															try:
																				UpperCamelCase_											=				eval(_lowerCAmelCase)
															except Exception:
																				UpperCamelCase_											=				data.split("\n")
										req.close()
					return data



def 				_lowerCAmelCase      (_lowerCAmelCase):
					UpperCamelCase_											=				requests.get(_lowerCAmelCase)
					UpperCamelCase_											=				np.array(Image.open(BytesIO(response.content)))
					return img



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



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



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






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





from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				_lowerCAmelCase      ():
					UpperCamelCase_											=				HfArgumentParser(_lowerCAmelCase)
					try:
										UpperCamelCase_											=				parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										UpperCamelCase_											=				"Arg --no_{0} is no longer used, please use --no-{0} instead."
										UpperCamelCase_											=				" ".join(str(_lowerCAmelCase).split(" ")[:-1])
										UpperCamelCase_											=				""
										UpperCamelCase_											=				eval(str(_lowerCAmelCase).split(" ")[-1])
										UpperCamelCase_											=				[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:])
															else:
																				wrong_args.append(_lowerCAmelCase)
										if len(_lowerCAmelCase) > 0:
															UpperCamelCase_											=				full_error_msg + begin_error_msg + str(_lowerCAmelCase)
										raise ValueError(_lowerCAmelCase)

					UpperCamelCase_											=				PyTorchBenchmark(args=_lowerCAmelCase)
					benchmark.run()


if __name__ == "__main__":
	main()
