






'''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

						A:   Any						=					True
except ImportError:
						A:   int						=					False


try:
						from torch.hub import _get_torch_home

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

A:   List[Any]						=					os.path.join(torch_cache_home, "transformers")

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






def 						_UpperCAmelCase     (	a		:						List[Any]=OBJECTS    ,      a		:						List[str]=ATTRIBUTES				)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : Any   	=      []
						with open(snake_case_				) as f:
												for object in f.readlines():
																		vg_classes.append(object.split(','				)[0].lower().strip()				)

						lowercase_   : List[str]   	=      []
						with open(snake_case_				) as f:
												for object in f.readlines():
																		vg_attrs.append(object.split(','				)[0].lower().strip()				)
						return vg_classes, vg_attrs






def 						_UpperCAmelCase     (	a		:						Optional[Any]				)      ->      Dict:





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






class 				__magic_name__  :







	"""simple docstring"""



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

							lowercase_   : Any   	=      d
	def __repr__(				self							)    ->	List[Any]:
							return str(list((self._pointer.keys())							)							)
	def __setattr__(				self	,   _lowercase	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Tuple   	=      val
							lowercase_   : Dict   	=      val
							lowercase_   : Optional[int]   	=      key.split('.'							)
							lowercase_   : List[str]   	=      len(__SCREAMING_SNAKE_CASE							) - 1
							lowercase_   : List[Any]   	=      self._pointer
							if len(__SCREAMING_SNAKE_CASE							) > 1:
													for i, l in enumerate(__SCREAMING_SNAKE_CASE							):
																			if hasattr(self	,   __SCREAMING_SNAKE_CASE							) and isinstance(getattr(self	,   __SCREAMING_SNAKE_CASE							)	,   __SCREAMING_SNAKE_CASE							):
																									setattr(getattr(self	,   __SCREAMING_SNAKE_CASE							)	,   '.'.join(levels[i:]							)	,   __SCREAMING_SNAKE_CASE							)
																			if l == last_level:
																									lowercase_   : int   	=      val
																			else:
																									lowercase_   : Tuple   	=      pointer[l]
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							return self._pointer
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase							)    ->	Dict:
							with open(f"{file_name}"	,   'w'							) as stream:
													dump(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase							)    ->	Tuple:
							with open(f"{file_name}"	,   'w'							) as stream:
													json.dump(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE							)
	@staticmethod
	def 							lowerCamelCase__		(				_lowercase							)    ->	int:
							with open(__SCREAMING_SNAKE_CASE							) as stream:
													lowercase_   : Dict   	=      load(__SCREAMING_SNAKE_CASE	,   Loader=__SCREAMING_SNAKE_CASE							)
							return data
	def __str__(				self							)    ->	Any:
							lowercase_   : str   	=      '    '
							if self._name != "root":
													lowercase_   : List[Any]   	=      f"{t * (self._level-1)}{self._name}:\n"
							else:
													lowercase_   : Tuple   	=      ''
							lowercase_   : Optional[Any]   	=      self._level
							for i, (k, v) in enumerate(self._pointer.items()							):
													if isinstance(__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE							):
																			r += f"{t * (self._level)}{v}\n"
																			self._level += 1
													else:
																			r += f"{t * (self._level)}{k}: {v} ({type(__SCREAMING_SNAKE_CASE							).__name__})\n"
													lowercase_   : int   	=      level
							return r[:-1]
	@classmethod
	def 							lowerCamelCase__		(				cls	,   _lowercase	,   **_lowercase							)    ->	List[Any]:
							lowercase_   : int   	=      cls.get_config_dict(__SCREAMING_SNAKE_CASE	,   **__SCREAMING_SNAKE_CASE							)
							return cls(__SCREAMING_SNAKE_CASE							)







	@classmethod
	def 							lowerCamelCase__		(				cls	,   _lowercase	,   **_lowercase							)    ->	int:
							lowercase_   : int   	=      kwargs.pop('cache_dir'	,   __SCREAMING_SNAKE_CASE							)
							lowercase_   : Optional[Any]   	=      kwargs.pop('force_download'	,   __SCREAMING_SNAKE_CASE							)
							lowercase_   : int   	=      kwargs.pop('resume_download'	,   __SCREAMING_SNAKE_CASE							)
							lowercase_   : Any   	=      kwargs.pop('proxies'	,   __SCREAMING_SNAKE_CASE							)
							lowercase_   : Any   	=      kwargs.pop('local_files_only'	,   __SCREAMING_SNAKE_CASE							)

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

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

													lowercase_   : Optional[int]   	=      Config.load_yaml(__SCREAMING_SNAKE_CASE							)

							except EnvironmentError:
													lowercase_   : int   	=      'Can\'t load config for'
													raise EnvironmentError(__SCREAMING_SNAKE_CASE							)

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

							return Config.load_yaml(__SCREAMING_SNAKE_CASE							), kwargs






def 						_UpperCAmelCase     (	a		:						Tuple				)      ->      Union[str, Any]:





						"""simple docstring"""
						lowercase_   : List[Any]   	=      torch.load('dump.pt'    ,      map_location=in_tensor.device				)
						lowercase_   : Union[str, Any]   	=      in_tensor.numpy()
						lowercase_   : Optional[Any]   	=      out_tensor.numpy()[0]
						print(na.shape    ,      na[0, 0, :5]				)
						print(na.shape    ,      na[0, 0, :5]				)
						assert np.allclose(snake_case_    ,      snake_case_    ,      rtol=0.01    ,      atol=0.1				), (
						    f"{sum([1 for x in np.isclose(snake_case_    ,      snake_case_    ,      rtol=0.01    ,      atol=0.1				).flatten() if x is False]				)/len(na.flatten()				)*1_0_0:.4f} %"
						    " element-wise mismatch"
						)
						raise Exception('tensors are all good'				)

						# Hugging face functions below






def 						_UpperCAmelCase     (	a		:						Any				)      ->      Optional[int]:





						"""simple docstring"""
						lowercase_   : List[str]   	=      urlparse(snake_case_				)
						return parsed.scheme in ("http", "https")






def 						_UpperCAmelCase     (	a		:						str    ,      a		:						str    ,      a		:						Union[str, Any]=True				)      ->      str:





						"""simple docstring"""
						lowercase_   : List[str]   	=      CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
						lowercase_   : Optional[Any]   	=      '/' not in model_id
						if legacy_format:
												return f"{endpoint}/{model_id}-{filename}"
						else:
												return f"{endpoint}/{model_id}/{filename}"






def 						_UpperCAmelCase     (	a		:						Tuple    ,      a		:						str    ,      a		:						List[str]=None    ,      a		:						int=0    ,      a		:						Union[str, Any]=None    ,      )      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      'python/{}'.format(sys.version.split()[0]				)
						if _torch_available:
												ua += "; torch/{}".format(torch.__version__				)
						if isinstance(snake_case_    ,      snake_case_				):
												ua += "; " + "; ".join('{}/{}'.format(snake_case_    ,      snake_case_				) for k, v in user_agent.items()				)
						elif isinstance(snake_case_    ,      snake_case_				):
												ua += "; " + user_agent
						lowercase_   : Optional[int]   	=      {'user-agent': ua}
						if resume_size > 0:
												lowercase_   : List[Any]   	=      'bytes=%d-' % (resume_size,)
						lowercase_   : List[Any]   	=      requests.get(snake_case_    ,      stream=snake_case_    ,      proxies=snake_case_    ,      headers=snake_case_				)
						if response.status_code == 4_1_6:  # Range not satisfiable
												return
						lowercase_   : Dict   	=      response.headers.get('Content-Length'				)
						lowercase_   : Optional[Any]   	=      resume_size + int(snake_case_				) if content_length is not None else None
						lowercase_   : Optional[Any]   	=      tqdm(
						    unit='B'    ,      unit_scale=snake_case_    ,      total=snake_case_    ,      initial=snake_case_    ,      desc='Downloading'    ,      )
						for chunk in response.iter_content(chunk_size=1_0_2_4				):
												if chunk:  # filter out keep-alive new chunks
																		progress.update(len(snake_case_				)				)
																		temp_file.write(snake_case_				)
						progress.close()






def 						_UpperCAmelCase     (	a		:						Dict    ,      a		:						List[Any]=None    ,      a		:						Optional[Any]=False    ,      a		:						List[str]=None    ,      a		:						Any=1_0    ,      a		:						Union[str, Any]=False    ,      a		:						Tuple=None    ,      a		:						str=False    ,      )      ->      str:





						"""simple docstring"""
						if cache_dir is None:
												lowercase_   : List[Any]   	=      TRANSFORMERS_CACHE
						if isinstance(snake_case_    ,      snake_case_				):
												lowercase_   : Optional[int]   	=      str(snake_case_				)

						os.makedirs(snake_case_    ,      exist_ok=snake_case_				)

						lowercase_   : List[Any]   	=      None
						if not local_files_only:
												try:
																		lowercase_   : Dict   	=      requests.head(snake_case_    ,      allow_redirects=snake_case_    ,      proxies=snake_case_    ,      timeout=snake_case_				)
																		if response.status_code == 2_0_0:
																								lowercase_   : Dict   	=      response.headers.get('ETag'				)
												except (EnvironmentError, requests.exceptions.Timeout):
																		# etag is already None
																		pass

						lowercase_   : List[Any]   	=      url_to_filename(snake_case_    ,      snake_case_				)

						# get cache path to put the file
						lowercase_   : Tuple   	=      os.path.join(snake_case_    ,      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(snake_case_				):
																		return cache_path
												else:
																		lowercase_   : List[Any]   	=      [
																		    file
																		    for file in fnmatch.filter(os.listdir(snake_case_				)    ,      filename + '.*'				)
																		    if not file.endswith('.json'				) and not file.endswith('.lock'				)
																		]
																		if len(snake_case_				) > 0:
																								return os.path.join(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(snake_case_				) and not force_download:
												return cache_path

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

												if resume_download:
																		lowercase_   : Any   	=      cache_path + '.incomplete'

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

																		lowercase_   : Tuple   	=      _resumable_file_manager
																		if os.path.exists(snake_case_				):
																								lowercase_   : int   	=      os.stat(snake_case_				).st_size
																		else:
																								lowercase_   : List[Any]   	=      0
												else:
																		lowercase_   : List[Any]   	=      partial(tempfile.NamedTemporaryFile    ,      dir=snake_case_    ,      delete=snake_case_				)
																		lowercase_   : 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'    ,      snake_case_    ,      temp_file.name    ,      )

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

												os.replace(temp_file.name    ,      snake_case_				)

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

						return cache_path






def 						_UpperCAmelCase     (	a		:						Any    ,      a		:						List[str]=None				)      ->      Tuple:





						"""simple docstring"""
						lowercase_   : List[Any]   	=      url.encode('utf-8'				)
						lowercase_   : Optional[int]   	=      shaaaa(snake_case_				)
						lowercase_   : int   	=      url_hash.hexdigest()

						if etag:
												lowercase_   : Dict   	=      etag.encode('utf-8'				)
												lowercase_   : Any   	=      shaaaa(snake_case_				)
												filename += "." + etag_hash.hexdigest()

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

						return filename






def 						_UpperCAmelCase     (	a		:						Optional[int]    ,      a		:						List[Any]=None    ,      a		:						Optional[Any]=False    ,      a		:						Tuple=None    ,      a		:						str=False    ,      a		:						Tuple=None    ,      a		:						int=False    ,      a		:						Any=False    ,      a		:						Optional[int]=False    ,      )      ->      List[str]:





						"""simple docstring"""
						if cache_dir is None:
												lowercase_   : Tuple   	=      TRANSFORMERS_CACHE
						if isinstance(snake_case_    ,      snake_case_				):
												lowercase_   : Any   	=      str(snake_case_				)
						if isinstance(snake_case_    ,      snake_case_				):
												lowercase_   : str   	=      str(snake_case_				)

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

						if extract_compressed_file:
												if not is_zipfile(snake_case_				) and not tarfile.is_tarfile(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/"
												lowercase_   : Tuple   	=      os.path.split(snake_case_				)
												lowercase_   : Any   	=      output_file.replace('.'    ,      '-'				) + '-extracted'
												lowercase_   : List[str]   	=      os.path.join(snake_case_    ,      snake_case_				)

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

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

												return output_path_extracted

						return output_path






def 						_UpperCAmelCase     (	a		:						Union[str, Any]    ,      a		:						Union[str, Any]=","				)      ->      Optional[Any]:





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






def 						_UpperCAmelCase     (	a		:						Union[str, Any]				)      ->      Dict:





						"""simple docstring"""
						lowercase_   : Any   	=      requests.get(snake_case_				)
						lowercase_   : int   	=      np.array(Image.open(BytesIO(response.content				)				)				)
						return img






def 						_UpperCAmelCase     (	a		:						Union[str, Any]				)      ->      Optional[int]:





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






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						print(f"{os.path.abspath(os.path.join(snake_case_    ,      os.pardir				)				)}/demo.ipynb"				)






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						Union[str, Any]="RGB"				)      ->      Optional[int]:





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






def 						_UpperCAmelCase     (	a		:						List[Any]    ,      a		:						Optional[Any]=1				)      ->      Tuple:





						"""simple docstring"""
						return (images[i : i + batch] for i in range(0    ,      len(snake_case_				)    ,      snake_case_				))


'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


