

'''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       a__    (  _SCREAMING_SNAKE_CASE					:    str=OBJECTS     ,							_SCREAMING_SNAKE_CASE					:    Any=ATTRIBUTES							)			-> Optional[int]:


	"""simple docstring"""





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

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


def       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[Any]							)			-> Optional[Any]:


	"""simple docstring"""





	UpperCAmelCase_			:	str      =   OrderedDict()
	with open(_SCREAMING_SNAKE_CASE     ,							"rb"							) as f:
		UpperCAmelCase_			:	Dict      =   pkl.load(_SCREAMING_SNAKE_CASE							)["model"]
	for k in copy.deepcopy(list(ckp.keys()							)							):
		UpperCAmelCase_			:	int      =   ckp.pop(_SCREAMING_SNAKE_CASE							)
		if isinstance(_SCREAMING_SNAKE_CASE     ,							np.ndarray							):
			UpperCAmelCase_			:	Optional[int]      =   torch.tensor(_SCREAMING_SNAKE_CASE							)
		else:
			assert isinstance(_SCREAMING_SNAKE_CASE     ,							torch.tensor							), type(_SCREAMING_SNAKE_CASE							)
		UpperCAmelCase_			:	str      =   v
	return r
class  _snake_case						:
			__A   :     Dict          ={}






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

				UpperCAmelCase_			:	Any      =   d






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






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






			def 						UpperCamelCase__				(   self			):
				return self._pointer






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case			):
				with open(f'''{file_name}'''       ,"w"			) as stream:
					dump(_snake_case       ,_snake_case			)






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case			):
				with open(f'''{file_name}'''       ,"w"			) as stream:
					json.dump(_snake_case       ,_snake_case			)






			@staticmethod
			def 						UpperCamelCase__				(   _snake_case			):
				with open(_snake_case			) as stream:
					UpperCAmelCase_			:	List[str]      =   load(_snake_case       ,Loader=_snake_case			)
				return data






			def __str__(   self			):
				UpperCAmelCase_			:	Optional[int]      =   "    "
				if self._name != "root":
					UpperCAmelCase_			:	Tuple      =   f'''{t * (self._level-1)}{self._name}:\n'''
				else:
					UpperCAmelCase_			:	List[Any]      =   ""
				UpperCAmelCase_			:	Optional[int]      =   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_			:	Tuple      =   level
				return r[:-1]






			@classmethod
			def 						UpperCamelCase__				(   cls       ,_snake_case       ,**_snake_case			):
				UpperCAmelCase_ , UpperCAmelCase_			:	int      =   cls.get_config_dict(_snake_case       ,**_snake_case			)
				return cls(_snake_case			)






			@classmethod
			def 						UpperCamelCase__				(   cls       ,_snake_case       ,**_snake_case			):
				UpperCAmelCase_			:	List[str]      =   kwargs.pop("cache_dir"       ,_snake_case			)
				UpperCAmelCase_			:	str      =   kwargs.pop("force_download"       ,_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   kwargs.pop("resume_download"       ,_snake_case			)
				UpperCAmelCase_			:	Dict      =   kwargs.pop("proxies"       ,_snake_case			)
				UpperCAmelCase_			:	Tuple      =   kwargs.pop("local_files_only"       ,_snake_case			)

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

				try:
					# Load from URL or cache if already cached
					UpperCAmelCase_			:	Optional[Any]      =   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_			:	Optional[int]      =   Config.load_yaml(_snake_case			)

				except EnvironmentError:
					UpperCAmelCase_			:	Union[str, Any]      =   "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       a__    (  _SCREAMING_SNAKE_CASE					:    Any							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Any      =   torch.load("dump.pt"     ,							map_location=in_tensor.device							)
	UpperCAmelCase_			:	Union[str, Any]      =   in_tensor.numpy()
	UpperCAmelCase_			:	Union[str, Any]      =   out_tensor.numpy()[0]
	print(na.shape     ,							na[0, 0, :5]							)
	print(na.shape     ,							na[0, 0, :5]							)
	assert np.allclose(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							rtol=0.01     ,							atol=0.1							), (
	    F'''{sum([1 for x in np.isclose(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							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       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[int]							)			-> int:


	"""simple docstring"""





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


def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=True							)			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Any      =   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       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    List[str]=None     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]=0     ,							_SCREAMING_SNAKE_CASE					:    Tuple=None     ,							)			-> Optional[Any]:


	"""simple docstring"""





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


def       a__    (  _SCREAMING_SNAKE_CASE					:    Any     ,							_SCREAMING_SNAKE_CASE					:    Optional[int]=None     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=False     ,							_SCREAMING_SNAKE_CASE					:    Optional[int]=None     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=10     ,							_SCREAMING_SNAKE_CASE					:    List[str]=False     ,							_SCREAMING_SNAKE_CASE					:    List[str]=None     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]=False     ,							)			-> List[str]:


	"""simple docstring"""





	if cache_dir is None:
		UpperCAmelCase_			:	int      =   TRANSFORMERS_CACHE
	if isinstance(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
		UpperCAmelCase_			:	str      =   str(_SCREAMING_SNAKE_CASE							)

	os.makedirs(_SCREAMING_SNAKE_CASE     ,							exist_ok=_SCREAMING_SNAKE_CASE							)

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

	UpperCAmelCase_			:	Any      =   url_to_filename(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							)

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

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

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

		if resume_download:
			UpperCAmelCase_			:	List[str]      =   cache_path + ".incomplete"

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

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

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

		os.replace(temp_file.name     ,							_SCREAMING_SNAKE_CASE							)

		UpperCAmelCase_			:	Dict      =   {"url": url, "etag": etag}
		UpperCAmelCase_			:	Union[str, Any]      =   cache_path + ".json"
		with open(_SCREAMING_SNAKE_CASE     ,							"w"							) as meta_file:
			json.dump(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							)

	return cache_path


def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    int=None							)			-> Tuple:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   url.encode("utf-8"							)
	UpperCAmelCase_			:	str      =   shaaaa(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Dict      =   url_hash.hexdigest()

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

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

	return filename


def       a__    (  _SCREAMING_SNAKE_CASE					:    Union[str, Any]     ,							_SCREAMING_SNAKE_CASE					:    Any=None     ,							_SCREAMING_SNAKE_CASE					:    Tuple=False     ,							_SCREAMING_SNAKE_CASE					:    int=None     ,							_SCREAMING_SNAKE_CASE					:    int=False     ,							_SCREAMING_SNAKE_CASE					:    str=None     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=False     ,							_SCREAMING_SNAKE_CASE					:    List[Any]=False     ,							_SCREAMING_SNAKE_CASE					:    List[str]=False     ,							)			-> List[str]:


	"""simple docstring"""





	if cache_dir is None:
		UpperCAmelCase_			:	Tuple      =   TRANSFORMERS_CACHE
	if isinstance(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
		UpperCAmelCase_			:	str      =   str(_SCREAMING_SNAKE_CASE							)
	if isinstance(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
		UpperCAmelCase_			:	List[str]      =   str(_SCREAMING_SNAKE_CASE							)

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

	if extract_compressed_file:
		if not is_zipfile(_SCREAMING_SNAKE_CASE							) and not tarfile.is_tarfile(_SCREAMING_SNAKE_CASE							):
			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(_SCREAMING_SNAKE_CASE							)
		UpperCAmelCase_			:	List[Any]      =   output_file.replace("."     ,							"-"							) + "-extracted"
		UpperCAmelCase_			:	Union[str, Any]      =   os.path.join(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							)

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

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

		return output_path_extracted

	return output_path


def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    List[str]=","							)			-> Tuple:


	"""simple docstring"""





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


def       a__    (  _SCREAMING_SNAKE_CASE					:    Union[str, Any]							)			-> Optional[Any]:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   requests.get(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Optional[Any]      =   np.array(Image.open(BytesIO(response.content							)							)							)
	return img


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[Any]							)			-> List[Any]:


	"""simple docstring"""





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


def       a__    (  )			-> Optional[int]:


	"""simple docstring"""





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


def       a__    (  _SCREAMING_SNAKE_CASE					:    Optional[int]     ,							_SCREAMING_SNAKE_CASE					:    Any="RGB"							)			-> Any:


	"""simple docstring"""





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





def       a__    (  _SCREAMING_SNAKE_CASE					:    int     ,							_SCREAMING_SNAKE_CASE					:    List[Any]=1							)			-> List[str]:


	"""simple docstring"""





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








"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

						_A         =	vocab_file
						_A         =	False if not self.vocab_file else True







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = None						)				->  Tuple[str]:





						'''simple docstring'''







						if not self.can_save_slow_tokenizer:
										raise ValueError(
										    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
										    "tokenizer."						)

						if not os.path.isdir(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	os.path.join(
						    a__			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(a__						):
										copyfile(self.vocab_file			,      a__						)

						return (out_vocab_file,)