
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
			from .tokenization_albert import AlbertTokenizer
else:
			SCREAMING_SNAKE_CASE_					     =	None

SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_					     =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE_					     =	{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE_					     =	{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

SCREAMING_SNAKE_CASE_					     =	'''▁'''
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		VOCAB_FILES_NAMES
							_snake_case						      =		PRETRAINED_VOCAB_FILES_MAP
							_snake_case						      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case						      =		AlbertTokenizer




							def __init__( self    ,				snake_case_=None    ,				snake_case_=None    ,				snake_case_=True    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_="[CLS]"    ,				snake_case_="[SEP]"    ,				snake_case_="<unk>"    ,				snake_case_="[SEP]"    ,				snake_case_="<pad>"    ,				snake_case_="[CLS]"    ,				snake_case_="[MASK]"    ,				**snake_case_    ,				)       ->  Optional[int]:
													# Mask token behave like a normal word, i.e. include the space before it and
													# is included in the raw text, there should be a match in a non-normalized sentence.
													__lowerCAmelCase     =  (
													    AddedToken(snake_case_    ,				lstrip=snake_case_    ,				rstrip=snake_case_    ,				normalized=snake_case_				)
													    if isinstance(snake_case_    ,				snake_case_				)
													    else mask_token
													)

													super().__init__(
													    snake_case_    ,				tokenizer_file=snake_case_    ,				do_lower_case=snake_case_    ,				remove_space=snake_case_    ,				keep_accents=snake_case_    ,				bos_token=snake_case_    ,				eos_token=snake_case_    ,				unk_token=snake_case_    ,				sep_token=snake_case_    ,				pad_token=snake_case_    ,				cls_token=snake_case_    ,				mask_token=snake_case_    ,				**snake_case_    ,				)

													__lowerCAmelCase     =  do_lower_case
													__lowerCAmelCase     =  remove_space
													__lowerCAmelCase     =  keep_accents
													__lowerCAmelCase     =  vocab_file
													__lowerCAmelCase     =  False if not self.vocab_file else True




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  List[int]:
													__lowerCAmelCase     =  [self.sep_token_id]
													__lowerCAmelCase     =  [self.cls_token_id]
													if token_ids_a is None:
																			return cls + token_ids_a + sep
													return cls + token_ids_a + sep + token_ids_a + sep




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  List[int]:
													__lowerCAmelCase     =  [self.sep_token_id]
													__lowerCAmelCase     =  [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				) * [0] + len(token_ids_a + sep				) * [1]




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  Tuple[str]:
													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(snake_case_				):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																			return
													__lowerCAmelCase     =  os.path.join(
													    snake_case_    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

													return (out_vocab_file,)


"""simple docstring"""
import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
			from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm

def  lowercase     (_lowerCAmelCase						):
						if is_torch_version("""<""" ,    """2.0.0"""						) or not hasattr(_lowerCAmelCase ,    """_dynamo"""						):
												return False
						return isinstance(_lowerCAmelCase ,    torch._dynamo.eval_frame.OptimizedModule						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = True						):
						__lowerCAmelCase     =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

						__lowerCAmelCase     =  is_compiled_module(_lowerCAmelCase						)
						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  model._orig_mod

						if is_deepspeed_available():
												options += (DeepSpeedEngine,)

						while isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  model.module

						if not keep_fpaa_wrapper:
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """forward"""						)
												__lowerCAmelCase     =  model.__dict__.pop("""_original_forward""" ,    _lowerCAmelCase						)
												if original_forward is not None:
																		while hasattr(_lowerCAmelCase ,    """__wrapped__"""						):
																								__lowerCAmelCase     =  forward.__wrapped__
																								if forward == original_forward:
																														break
																		__lowerCAmelCase     =  forward
												if getattr(_lowerCAmelCase ,    """_converted_to_transformer_engine""" ,    _lowerCAmelCase						):
																		convert_model(_lowerCAmelCase ,    to_transformer_engine=_lowerCAmelCase						)

						if is_compiled:
												__lowerCAmelCase     =  model
												__lowerCAmelCase     =  compiled_model

						return model

def  lowercase     ():
						PartialState().wait_for_everyone()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						if PartialState().distributed_type == DistributedType.TPU:
												xm.save(_lowerCAmelCase ,    _lowerCAmelCase						)
						elif PartialState().local_process_index == 0:
												torch.save(_lowerCAmelCase ,    _lowerCAmelCase						)

@contextmanager
def  lowercase     (**_lowerCAmelCase						):
						for key, value in kwargs.items():
												__lowerCAmelCase     =  str(_lowerCAmelCase						)

						yield

						for key in kwargs:
												if key.upper() in os.environ:
																		del os.environ[key.upper()]

def  lowercase     (_lowerCAmelCase						):
						if not hasattr(_lowerCAmelCase ,    """__qualname__"""						) and not hasattr(_lowerCAmelCase ,    """__name__"""						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """__class__""" ,    _lowerCAmelCase						)
						if hasattr(_lowerCAmelCase ,    """__qualname__"""						):
												return obj.__qualname__
						if hasattr(_lowerCAmelCase ,    """__name__"""						):
												return obj.__name__
						return str(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						for key, value in source.items():
												if isinstance(_lowerCAmelCase ,    _lowerCAmelCase						):
																		__lowerCAmelCase     =  destination.setdefault(_lowerCAmelCase ,    {}						)
																		merge_dicts(_lowerCAmelCase ,    _lowerCAmelCase						)
												else:
																		__lowerCAmelCase     =  value

						return destination



def  lowercase     (_lowerCAmelCase = None						):
						if port is None:
												__lowerCAmelCase     =  2_9500
						with socket.socket(socket.AF_INET ,    socket.SOCK_STREAM						) as s:
												return s.connect_ex(("""localhost""", port)						) == 0
