




"""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

			lowercase__  		=		True
except ImportError:
			lowercase__  		=		False


try:
			from torch.hub import _get_torch_home

			lowercase__  		=		_get_torch_home()
except ImportError:
			lowercase__  		=		os.path.expanduser(
			    os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
			)

lowercase__  		=		os.path.join(torch_cache_home, """transformers""")

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







def   _snake_case    (       lowercase__=OBJECTS							,					lowercase__=ATTRIBUTES	):
				_lowerCamelCase  :    Tuple					=					[]
				with open(UpperCAmelCase__	) as f:
								for object in f.readlines():
												vg_classes.append(object.split(','	)[0].lower().strip()	)

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







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






class lowerCAmelCase__			:






			'''simple docstring'''



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

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






			@classmethod
			def       A_					(						cls						,   lowercase						,   **lowercase						):
							_lowerCamelCase  :    Union[str, Any]					=					kwargs.pop('cache_dir'						,   __UpperCamelCase						)
							_lowerCamelCase  :    Any					=					kwargs.pop('force_download'						,   __UpperCamelCase						)
							_lowerCamelCase  :    List[str]					=					kwargs.pop('resume_download'						,   __UpperCamelCase						)
							_lowerCamelCase  :    List[str]					=					kwargs.pop('proxies'						,   __UpperCamelCase						)
							_lowerCamelCase  :    int					=					kwargs.pop('local_files_only'						,   __UpperCamelCase						)

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

							try:
											# Load from URL or cache if already cached
											_lowerCamelCase  :    Optional[Any]					=					cached_path(
											    __UpperCamelCase						,   cache_dir=__UpperCamelCase						,   force_download=__UpperCamelCase						,   proxies=__UpperCamelCase						,   resume_download=__UpperCamelCase						,   local_files_only=__UpperCamelCase						,   )
											# Load config dict
											if resolved_config_file is None:
															raise EnvironmentError

											_lowerCamelCase  :    str					=					Config.load_yaml(__UpperCamelCase						)

							except EnvironmentError:
											_lowerCamelCase  :    List[str]					=					'Can\'t load config for'
											raise EnvironmentError(__UpperCamelCase						)

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

							return Config.load_yaml(__UpperCamelCase						), kwargs







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Tuple					=					torch.load('dump.pt'							,					map_location=in_tensor.device	)
				_lowerCamelCase  :    List[Any]					=					in_tensor.numpy()
				_lowerCamelCase  :    Union[str, Any]					=					out_tensor.numpy()[0]
				print(na.shape							,					na[0, 0, :5]	)
				print(na.shape							,					na[0, 0, :5]	)
				assert np.allclose(UpperCAmelCase__							,					UpperCAmelCase__							,					rtol=0.0_1							,					atol=0.1	), (
				    f'''{sum([1 for x in np.isclose(UpperCAmelCase__							,					UpperCAmelCase__							,					rtol=0.0_1							,					atol=0.1	).flatten() if x is False]	)/len(na.flatten()	)*100:.4f} %'''
				    " element-wise mismatch"
				)
				raise Exception('tensors are all good'	)

				# Hugging face functions below







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					urlparse(UpperCAmelCase__	)
				return parsed.scheme in ("http", "https")







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=True	):
				_lowerCamelCase  :    Optional[Any]					=					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				_lowerCamelCase  :    List[str]					=					'/' not in model_id
				if legacy_format:
								return f'''{endpoint}/{model_id}-{filename}'''
				else:
								return f'''{endpoint}/{model_id}/{filename}'''







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__=None							,					lowercase__=0							,					lowercase__=None							,					):
				_lowerCamelCase  :    Dict					=					'python/{}'.format(sys.version.split()[0]	)
				if _torch_available:
								ua += "; torch/{}".format(torch.__version__	)
				if isinstance(UpperCAmelCase__							,					UpperCAmelCase__	):
								ua += "; " + "; ".join('{}/{}'.format(UpperCAmelCase__							,					UpperCAmelCase__	) for k, v in user_agent.items()	)
				elif isinstance(UpperCAmelCase__							,					UpperCAmelCase__	):
								ua += "; " + user_agent
				_lowerCamelCase  :    List[str]					=					{'user-agent': ua}
				if resume_size > 0:
								_lowerCamelCase  :    Dict					=					'bytes=%d-' % (resume_size,)
				_lowerCamelCase  :    str					=					requests.get(UpperCAmelCase__							,					stream=UpperCAmelCase__							,					proxies=UpperCAmelCase__							,					headers=UpperCAmelCase__	)
				if response.status_code == 416:  # Range not satisfiable
								return
				_lowerCamelCase  :    Any					=					response.headers.get('Content-Length'	)
				_lowerCamelCase  :    Optional[Any]					=					resume_size + int(UpperCAmelCase__	) if content_length is not None else None
				_lowerCamelCase  :    Any					=					tqdm(
				    unit='B'							,					unit_scale=UpperCAmelCase__							,					total=UpperCAmelCase__							,					initial=UpperCAmelCase__							,					desc='Downloading'							,					)
				for chunk in response.iter_content(chunk_size=1024	):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(UpperCAmelCase__	)	)
												temp_file.write(UpperCAmelCase__	)
				progress.close()







def   _snake_case    (       lowercase__							,					lowercase__=None							,					lowercase__=False							,					lowercase__=None							,					lowercase__=10							,					lowercase__=False							,					lowercase__=None							,					lowercase__=False							,					):
				if cache_dir is None:
								_lowerCamelCase  :    Optional[int]					=					TRANSFORMERS_CACHE
				if isinstance(UpperCAmelCase__							,					UpperCAmelCase__	):
								_lowerCamelCase  :    Any					=					str(UpperCAmelCase__	)

				os.makedirs(UpperCAmelCase__							,					exist_ok=UpperCAmelCase__	)

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

				_lowerCamelCase  :    Optional[int]					=					url_to_filename(UpperCAmelCase__							,					UpperCAmelCase__	)

				# get cache path to put the file
				_lowerCamelCase  :    Optional[int]					=					os.path.join(UpperCAmelCase__							,					UpperCAmelCase__	)

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

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

								if resume_download:
												_lowerCamelCase  :    List[str]					=					cache_path + '.incomplete'

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

												_lowerCamelCase  :    Union[str, Any]					=					_resumable_file_manager
												if os.path.exists(UpperCAmelCase__	):
																_lowerCamelCase  :    str					=					os.stat(UpperCAmelCase__	).st_size
												else:
																_lowerCamelCase  :    Any					=					0
								else:
												_lowerCamelCase  :    Union[str, Any]					=					partial(tempfile.NamedTemporaryFile							,					dir=UpperCAmelCase__							,					delete=UpperCAmelCase__	)
												_lowerCamelCase  :    List[str]					=					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'							,					UpperCAmelCase__							,					temp_file.name							,					)

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

								os.replace(temp_file.name							,					UpperCAmelCase__	)

								_lowerCamelCase  :    Tuple					=					{'url': url, 'etag': etag}
								_lowerCamelCase  :    Optional[int]					=					cache_path + '.json'
								with open(UpperCAmelCase__							,					'w'	) as meta_file:
												json.dump(UpperCAmelCase__							,					UpperCAmelCase__	)

				return cache_path







def   _snake_case    (       lowercase__							,					lowercase__=None	):
				_lowerCamelCase  :    Tuple					=					url.encode('utf-8'	)
				_lowerCamelCase  :    List[str]					=					shaaaa(UpperCAmelCase__	)
				_lowerCamelCase  :    Union[str, Any]					=					url_hash.hexdigest()

				if etag:
								_lowerCamelCase  :    Tuple					=					etag.encode('utf-8'	)
								_lowerCamelCase  :    Dict					=					shaaaa(UpperCAmelCase__	)
								filename += "." + etag_hash.hexdigest()

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

				return filename







def   _snake_case    (       lowercase__							,					lowercase__=None							,					lowercase__=False							,					lowercase__=None							,					lowercase__=False							,					lowercase__=None							,					lowercase__=False							,					lowercase__=False							,					lowercase__=False							,					):
				if cache_dir is None:
								_lowerCamelCase  :    Optional[Any]					=					TRANSFORMERS_CACHE
				if isinstance(UpperCAmelCase__							,					UpperCAmelCase__	):
								_lowerCamelCase  :    Union[str, Any]					=					str(UpperCAmelCase__	)
				if isinstance(UpperCAmelCase__							,					UpperCAmelCase__	):
								_lowerCamelCase  :    Any					=					str(UpperCAmelCase__	)

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

				if extract_compressed_file:
								if not is_zipfile(UpperCAmelCase__	) and not tarfile.is_tarfile(UpperCAmelCase__	):
												return output_path

								# Path where we extract compressed archives
								# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
								_lowerCamelCase,				_lowerCamelCase  :    Any					=					os.path.split(UpperCAmelCase__	)
								_lowerCamelCase  :    int					=					output_file.replace('.'							,					'-'	) + '-extracted'
								_lowerCamelCase  :    List[Any]					=					os.path.join(UpperCAmelCase__							,					UpperCAmelCase__	)

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

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

								return output_path_extracted

				return output_path







def   _snake_case    (       lowercase__							,					lowercase__=","	):
				assert isinstance(UpperCAmelCase__							,					UpperCAmelCase__	)
				if os.path.isfile(UpperCAmelCase__	):
								with open(UpperCAmelCase__	) as f:
												_lowerCamelCase  :    Union[str, Any]					=					eval(f.read()	)
				else:
								_lowerCamelCase  :    Optional[int]					=					requests.get(UpperCAmelCase__	)
								try:
												_lowerCamelCase  :    str					=					requests.json()
								except Exception:
												_lowerCamelCase  :    Optional[int]					=					req.content.decode()
												assert data is not None, "could not connect"
												try:
																_lowerCamelCase  :    int					=					eval(UpperCAmelCase__	)
												except Exception:
																_lowerCamelCase  :    Tuple					=					data.split('\n'	)
								req.close()
				return data







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Tuple					=					requests.get(UpperCAmelCase__	)
				_lowerCamelCase  :    str					=					np.array(Image.open(BytesIO(response.content	)	)	)
				return img







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







def   _snake_case    (       ):
				print(f'''{os.path.abspath(os.path.join(UpperCAmelCase__							,					os.pardir	)	)}/demo.ipynb'''	)







def   _snake_case    (       lowercase__							,					lowercase__="RGB"	):
				assert isinstance(UpperCAmelCase__							,					UpperCAmelCase__	)
				if os.path.isfile(UpperCAmelCase__	):
								_lowerCamelCase  :    List[Any]					=					cva.imread(UpperCAmelCase__	)
				else:
								_lowerCamelCase  :    Any					=					get_image_from_url(UpperCAmelCase__	)
								assert img is not None, f'''could not connect to: {im}'''
				_lowerCamelCase  :    List[str]					=					cva.cvtColor(UpperCAmelCase__							,					cva.COLOR_BGR2RGB	)
				if input_format == "RGB":
								_lowerCamelCase  :    Union[str, Any]					=					img[:, :, ::-1]
				return img







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








"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		"""▁"""

lowercase__  		=		{"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase__  		=		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase__  		=		{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase="<s>"						,   lowercase="</s>"						,   lowercase="</s>"						,   lowercase="<s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase="<mask>"						,   lowercase = None						,   **lowercase						,   ):
							# Mask token behave like a normal word, i.e. include the space before it
							_lowerCamelCase  :    str					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else mask_token

							_lowerCamelCase  :    Union[str, Any]					=					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=lowercase						,   eos_token=lowercase						,   unk_token=lowercase						,   sep_token=lowercase						,   cls_token=lowercase						,   pad_token=lowercase						,   mask_token=lowercase						,   sp_model_kwargs=self.sp_model_kwargs						,   **lowercase						,   )

							_lowerCamelCase  :    Any					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(str(lowercase						)						)
							_lowerCamelCase  :    str					=					vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_lowerCamelCase  :    List[str]					=					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_lowerCamelCase  :    List[Any]					=					1

							_lowerCamelCase  :    List[Any]					=					len(self.sp_model						) + self.fairseq_offset
							_lowerCamelCase  :    int					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
			def __getstate__(						self						):
							_lowerCamelCase  :    Tuple					=					self.__dict__.copy()
							_lowerCamelCase  :    Tuple					=					None
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.serialized_model_proto()
							return state
			def __setstate__(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					d

							# for backward compatibility
							if not hasattr(self						,   'sp_model_kwargs'						):
											_lowerCamelCase  :    str					=					{}

							_lowerCamelCase  :    List[str]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)
			def       A_					(						self						,   lowercase						,   lowercase = None						):

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowerCamelCase  :    Any					=					[self.cls_token_id]
							_lowerCamelCase  :    List[Any]					=					[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep
			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = False						):

							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase						,   token_ids_a=lowercase						,   already_has_special_tokens=lowercase						)

							if token_ids_a is None:
											return [1] + ([0] * len(lowercase						)) + [1]
							return [1] + ([0] * len(lowercase						)) + [1, 1] + ([0] * len(lowercase						)) + [1]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    Tuple					=					[self.sep_token_id]
							_lowerCamelCase  :    Optional[int]					=					[self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]
			@property
			def       A_					(						self						):
							return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					{self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab
			def       A_					(						self						,   lowercase						):
							return self.sp_model.encode(lowercase						,   out_type=lowercase						)
			def       A_					(						self						,   lowercase						):
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.PieceToId(lowercase						)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
			def       A_					(						self						,   lowercase						):
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					''.join(lowercase						).replace(lowercase						,   ' '						).strip()
							return out_string






			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if not os.path.isdir(lowercase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
							_lowerCamelCase  :    int					=					os.path.join(
							    lowercase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase						) and os.path.isfile(self.vocab_file						):
											copyfile(self.vocab_file						,   lowercase						)
							elif not os.path.isfile(self.vocab_file						):
											with open(lowercase						,   'wb'						) as fi:
															_lowerCamelCase  :    str					=					self.sp_model.serialized_model_proto()
															fi.write(lowercase						)

							return (out_vocab_file,)