import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


_UpperCAmelCase            =		get_logger(__name__)


_UpperCAmelCase            =		Path(__file__).parent / """model_card_template.md"""
_UpperCAmelCase            =		uuida().hex
_UpperCAmelCase            =		os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
_UpperCAmelCase            =		os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
_UpperCAmelCase            =		HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""




def     UpperCamelCase						(	__lowercase						:			Union[Dict, str, None] = None     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
		if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
				return ua + "; telemetry/off"
		if is_torch_available():
				ua += f'''; torch/{_torch_version}'''
		if is_flax_available():
				ua += f'''; jax/{_jax_version}'''
				ua += f'''; flax/{_flax_version}'''
		if is_onnx_available():
				ua += f'''; onnxruntime/{_onnxruntime_version}'''
		# CI will set this value to True
		if os.environ.get('DIFFUSERS_IS_CI'						,''     ).upper() in ENV_VARS_TRUE_VALUES:
				ua += "; is_ci/true"
		if isinstance(__lowercase						,__lowercase     ):
				ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()     )
		elif isinstance(__lowercase						,__lowercase     ):
				ua += "; " + user_agent
		return ua




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Optional[str] = None						,__lowercase						:			Optional[str] = None     ):
		'''simple docstring'''




		if token is None:
				A_		:    int         =				HfFolder.get_token()
		if organization is None:
				A_		:    Union[str, Any]         =				whoami(__lowercase     )['name']
				return f'''{username}/{model_id}'''
		else:
				return f'''{organization}/{model_id}'''




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Any     ):
		'''simple docstring'''




		if not is_jinja_available():
				raise ValueError(
				    'Modelcard rendering is based on Jinja templates.'
				    ' Please make sure to have `jinja` installed before using `create_model_card`.'
				    ' To install it, please run `pip install Jinja2`.'     )

		if hasattr(__lowercase						,'local_rank'     ) and args.local_rank not in [-1, 0]:
				return

		A_		:    Optional[Any]         =				args.hub_token if hasattr(__lowercase						,'hub_token'     ) else None
		A_		:    Dict         =				get_full_repo_name(__lowercase						,token=__lowercase     )

		A_		:    List[Any]         =				ModelCard.from_template(
		    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
		        language='en'						,license='apache-2.0'						,library_name='diffusers'						,tags=[]						,datasets=args.dataset_name						,metrics=[]						,)						,template_path=__lowercase						,model_name=__lowercase						,repo_name=__lowercase						,dataset_name=args.dataset_name if hasattr(__lowercase						,'dataset_name'     ) else None						,learning_rate=args.learning_rate						,train_batch_size=args.train_batch_size						,eval_batch_size=args.eval_batch_size						,gradient_accumulation_steps=(
		        args.gradient_accumulation_steps if hasattr(__lowercase						,'gradient_accumulation_steps'     ) else None
		    )						,adam_betaa=args.adam_betaa if hasattr(__lowercase						,'adam_beta1'     ) else None						,adam_betaa=args.adam_betaa if hasattr(__lowercase						,'adam_beta2'     ) else None						,adam_weight_decay=args.adam_weight_decay if hasattr(__lowercase						,'adam_weight_decay'     ) else None						,adam_epsilon=args.adam_epsilon if hasattr(__lowercase						,'adam_epsilon'     ) else None						,lr_scheduler=args.lr_scheduler if hasattr(__lowercase						,'lr_scheduler'     ) else None						,lr_warmup_steps=args.lr_warmup_steps if hasattr(__lowercase						,'lr_warmup_steps'     ) else None						,ema_inv_gamma=args.ema_inv_gamma if hasattr(__lowercase						,'ema_inv_gamma'     ) else None						,ema_power=args.ema_power if hasattr(__lowercase						,'ema_power'     ) else None						,ema_max_decay=args.ema_max_decay if hasattr(__lowercase						,'ema_max_decay'     ) else None						,mixed_precision=args.mixed_precision						,)

		A_		:    List[Any]         =				os.path.join(args.output_dir						,'README.md'     )
		model_card.save(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			Optional[str]						,__lowercase						:			Optional[str] = None     ):
		'''simple docstring'''




		if resolved_file is None or commit_hash is not None:
				return commit_hash
		A_		:    Dict         =				str(Path(__lowercase     ).as_posix()     )
		A_		:    Dict         =				re.search(r'snapshots/([^/]+)/'						,__lowercase     )
		if search is None:
				return None
		A_		:    int         =				search.groups()[0]
		return commit_hash if REGEX_COMMIT_HASH.match(__lowercase     ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_UpperCAmelCase            =		os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
_UpperCAmelCase            =		os.path.join(hf_cache_home, """diffusers""")




def     UpperCamelCase						(	__lowercase						:			Optional[str] = None						,__lowercase						:			Optional[str] = None     ):
		'''simple docstring'''




		if new_cache_dir is None:
				A_		:    Optional[Any]         =				DIFFUSERS_CACHE
		if old_cache_dir is None:
				A_		:    Tuple         =				old_diffusers_cache

		A_		:    int         =				Path(__lowercase     ).expanduser()
		A_		:    Optional[Any]         =				Path(__lowercase     ).expanduser()
		for old_blob_path in old_cache_dir.glob('**/blobs/*'     ):
				if old_blob_path.is_file() and not old_blob_path.is_symlink():
						A_		:    Any         =				new_cache_dir / old_blob_path.relative_to(__lowercase     )
						new_blob_path.parent.mkdir(parents=__lowercase						,exist_ok=__lowercase     )
						os.replace(__lowercase						,__lowercase     )
						try:
								os.symlink(__lowercase						,__lowercase     )
						except OSError:
								logger.warning(
								    'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'     )
		# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


_UpperCAmelCase            =		os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
	_UpperCAmelCase            =		0
else:
	with open(cache_version_file) as f:
		try:
			_UpperCAmelCase            =		int(f.read())
		except ValueError:
			_UpperCAmelCase            =		0

if cache_version < 1:
	_UpperCAmelCase            =		os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
	if old_cache_is_not_empty:
		logger.warning(
		    """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
		    """existing cached models. This is a one-time operation, you can interrupt it or run it """
		    """later by calling `diffusers.utils.hub_utils.move_cache()`."""
		)
		try:
			move_cache()
		except Exception as e:
			_UpperCAmelCase            =		"""\n""".join(traceback.format_tb(e.__traceback__))
			logger.error(
			    F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
			    """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
			    """message and we will do our best to help."""
			)

if cache_version < 1:
	try:
		os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
		with open(cache_version_file, """w""") as f:
			f.write("""1""")
	except Exception:
		logger.warning(
		    F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
		    """the directory exists and can be written to."""
		)




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			Optional[str] = None     ):
		'''simple docstring'''




		if variant is not None:
				A_		:    Optional[int]         =				weights_name.split('.'     )
				A_		:    Optional[int]         =				splits[:-1] + [variant] + splits[-1:]
				A_		:    Any         =				'.'.join(__lowercase     )

		return weights_name




def     UpperCamelCase						(	__lowercase						:			Any						,*,
    __lowercase						:			List[str]						,__lowercase						:			Any						,__lowercase						:			Optional[int]						,__lowercase						:			Any						,__lowercase						:			List[Any]						,__lowercase						:			List[Any]						,__lowercase						:			str						,__lowercase						:			List[str]						,__lowercase						:			Dict						,__lowercase						:			Dict						,__lowercase						:			Union[str, Any]=None						,):
		'''simple docstring'''




		A_		:    List[str]         =				str(__lowercase     )
		if os.path.isfile(__lowercase     ):
				return pretrained_model_name_or_path
		elif os.path.isdir(__lowercase     ):
				if os.path.isfile(os.path.join(__lowercase						,__lowercase     )     ):
						# Load from a PyTorch checkpoint
						A_		:    Tuple         =				os.path.join(__lowercase						,__lowercase     )
						return model_file
				elif subfolder is not None and os.path.isfile(
				    os.path.join(__lowercase						,__lowercase						,__lowercase     )     ):
						A_		:    List[str]         =				os.path.join(__lowercase						,__lowercase						,__lowercase     )
						return model_file
				else:
						raise EnvironmentError(
						    f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''     )
		else:
				# 1. First check if deprecated way of loading from branches is used
				if (
				    revision in DEPRECATED_REVISION_ARGS
				    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
				    and version.parse(version.parse(__lowercase     ).base_version     ) >= version.parse('0.20.0'     )
				):
						try:
								A_		:    Tuple         =				hf_hub_download(
								    __lowercase						,filename=_add_variant(__lowercase						,__lowercase     )						,cache_dir=__lowercase						,force_download=__lowercase						,proxies=__lowercase						,resume_download=__lowercase						,local_files_only=__lowercase						,use_auth_token=__lowercase						,user_agent=__lowercase						,subfolder=__lowercase						,revision=revision or commit_hash						,)
								warnings.warn(
								    f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''						,__lowercase						,)
								return model_file
						except:  # noqa: E722
								warnings.warn(
								    f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__lowercase						,__lowercase     )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(__lowercase						,__lowercase     )}\' so that the correct variant file can be added.'''						,__lowercase						,)
				try:
						# 2. Load model file as usual
						A_		:    Any         =				hf_hub_download(
						    __lowercase						,filename=__lowercase						,cache_dir=__lowercase						,force_download=__lowercase						,proxies=__lowercase						,resume_download=__lowercase						,local_files_only=__lowercase						,use_auth_token=__lowercase						,user_agent=__lowercase						,subfolder=__lowercase						,revision=revision or commit_hash						,)
						return model_file

				except RepositoryNotFoundError:
						raise EnvironmentError(
						    f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
						    'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
						    'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
						    'login`.'     )
				except RevisionNotFoundError:
						raise EnvironmentError(
						    f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
						    'this model name. Check the model page at '
						    f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''     )
				except EntryNotFoundError:
						raise EnvironmentError(
						    f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''     )
				except HTTPError as err:
						raise EnvironmentError(
						    f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''     )
				except ValueError:
						raise EnvironmentError(
						    f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
						    f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
						    f''' directory containing a file named {weights_name} or'''
						    ' \nCheckout your internet connection or see how to run the library in'
						    ' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'     )
				except EnvironmentError:
						raise EnvironmentError(
						    f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
						    '\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
						    f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
						    f'''containing a file named {weights_name}'''     )







import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_UpperCAmelCase            =		logging.get_logger(__name__)


_UpperCAmelCase            =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

_UpperCAmelCase            =		{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[int]         =				(
		    list(range(ord('!'     )						,ord('~'     ) + 1     )     ) + list(range(ord('¡'     )						,ord('¬'     ) + 1     )     ) + list(range(ord('®'     )						,ord('ÿ'     ) + 1     )     )
		)
		A_		:    Any         =				bs[:]
		A_		:    Optional[Any]         =				0
		for b in range(2**8     ):
				if b not in bs:
						bs.append(__lowercase     )
						cs.append(2**8 + n     )
						n += 1
		A_		:    str         =				[chr(__lowercase     ) for n in cs]
		return dict(zip(__lowercase						,__lowercase     )     )




def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    List[str]         =				set()
		A_		:    List[Any]         =				word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)     )
				A_		:    Union[str, Any]         =				char
		return pairs





class UpperCAmelCase						( __A  ):


				'''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		,     lowercase="replace"		,     lowercase="<s>"		,     lowercase="</s>"		,     lowercase="</s>"		,     lowercase="<s>"		,     lowercase="<unk>"		,     lowercase="<pad>"		,     lowercase="<mask>"		,     lowercase=False		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else bos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else eos_token
						A_		:    str         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else sep_token
						A_		:    Tuple         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else cls_token
						A_		:    List[str]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else unk_token
						A_		:    Optional[int]         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						A_		:    int         =				AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase ) if isinstance(lowercase		,     lowercase ) else mask_token

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

						with open(lowercase		,     encoding='utf-8' ) as vocab_handle:
								A_		:    int         =				json.load(lowercase )
						A_		:    Union[str, Any]         =				{v: k for k, v in self.encoder.items()}
						A_		:    Union[str, Any]         =				errors  # how to handle errors in decoding
						A_		:    Tuple         =				bytes_to_unicode()
						A_		:    str         =				{v: k for k, v in self.byte_encoder.items()}
						with open(lowercase		,     encoding='utf-8' ) as merges_handle:
								A_		:    str         =				merges_handle.read().split('\n' )[1:-1]
						A_		:    Optional[Any]         =				[tuple(merge.split() ) for merge in bpe_merges]
						A_		:    Any         =				dict(zip(lowercase		,     range(len(lowercase ) ) ) )
						A_		:    str         =				{}
						A_		:    List[Any]         =				add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						A_		:    Optional[Any]         =				re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return len(self.encoder )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return dict(self.encoder		,     **self.added_tokens_encoder )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if token in self.cache:
								return self.cache[token]
						A_		:    List[Any]         =				tuple(lowercase )
						A_		:    List[Any]         =				get_pairs(lowercase )

						if not pairs:
								return token

						while True:
								A_		:    List[str]         =				min(lowercase		,     key=lambda lowercase : self.bpe_ranks.get(lowercase		,     float('inf' ) ) )
								if bigram not in self.bpe_ranks:
										break
								A_       ,      A_		:    Optional[Any]         =				bigram
								A_		:    List[Any]         =				[]
								A_		:    Tuple         =				0
								while i < len(lowercase ):
										try:
												A_		:    Optional[Any]         =				word.index(lowercase		,     lowercase )
										except ValueError:
												new_word.extend(word[i:] )
												break
										else:
												new_word.extend(word[i:j] )
												A_		:    int         =				j

										if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
												new_word.append(first + second )
												i += 2
										else:
												new_word.append(word[i] )
												i += 1
								A_		:    Union[str, Any]         =				tuple(lowercase )
								A_		:    Tuple         =				new_word
								if len(lowercase ) == 1:
										break
								else:
										A_		:    Optional[int]         =				get_pairs(lowercase )
						A_		:    Optional[Any]         =				' '.join(lowercase )
						A_		:    str         =				word
						return word
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[]
						for token in re.findall(self.pat		,     lowercase ):
								A_		:    Optional[Any]         =				''.join(
								    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
								bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase ).split(' ' ) )
						return bpe_tokens
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.encoder.get(lowercase		,     self.encoder.get(self.unk_token ) )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return self.decoder.get(lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				''.join(lowercase )
						A_		:    List[Any]         =				bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'		,     errors=self.errors )
						return text
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						if not os.path.isdir(lowercase ):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
								return
						A_		:    Optional[int]         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						A_		:    Dict         =				os.path.join(
						    lowercase		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

						with open(lowercase		,     'w'		,     encoding='utf-8' ) as f:
								f.write(json.dumps(self.encoder		,     indent=2		,     sort_keys=lowercase		,     ensure_ascii=lowercase ) + '\n' )

						A_		:    Dict         =				0
						with open(lowercase		,     'w'		,     encoding='utf-8' ) as writer:
								writer.write('#version: 0.2\n' )
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda lowercase : kv[1] ):
										if index != token_index:
												logger.warning(
												    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
												    ' Please check that the tokenizer is not corrupted!' )
												A_		:    int         =				token_index
										writer.write(' '.join(lowercase ) + '\n' )
										index += 1

						return vocab_file, merge_file
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





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



						"""simple docstring"""





						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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[self.sep_token_id]
						A_		:    List[Any]         =				[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]







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=False		,     **lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				kwargs.pop('add_prefix_space'		,     self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(lowercase ) > 0 and not text[0].isspace()):
								A_		:    int         =				' ' + text
						return (text, kwargs)






