

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_xlnet import XLNetTokenizer
else:
	A_			:					Optional[Any]    			=     None


A_			:					List[Any]    			=     logging.get_logger(__name__)

A_			:					Tuple    			=     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_			:					Union[str, Any]    			=     {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

A_			:					Tuple    			=     {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

A_			:					List[str]    			=     '▁'

# Segments (not really needed)
A_			:					Any    			=     0
A_			:					int    			=     1
A_			:					Optional[Any]    			=     2
A_			:					List[Any]    			=     3
A_			:					Union[str, Any]    			=     4



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Dict    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  int    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  Any    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  Union[str, Any]    				=			'''left'''
					UpperCAmelCase__:  Optional[Any]    				=			XLNetTokenizer






					def __init__(					self       ,			A__=None       ,			A__=None       ,			A__=False       ,			A__=True       ,			A__=False       ,			A__="<s>"       ,			A__="</s>"       ,			A__="<unk>"       ,			A__="<sep>"       ,			A__="<pad>"       ,			A__="<cls>"       ,			A__="<mask>"       ,			A__=["<eop>", "<eod>"]       ,			**A__       ,			):
						# Mask token behave like a normal word, i.e. include the space before it
						A__				:       Optional[int]	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else mask_token

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

						A__				:       Optional[Any]	  =						3
						A__				:       List[str]	  =						do_lower_case
						A__				:       Optional[int]	  =						remove_space
						A__				:       Optional[int]	  =						keep_accents
						A__				:       Union[str, Any]	  =						vocab_file
						A__				:       Union[str, Any]	  =						False if not self.vocab_file else True






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Union[str, Any]	  =						[self.sep_token_id]
						A__				:       Any	  =						[self.cls_token_id]
						if token_ids_a is None:
							return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Union[str, Any]	  =						[self.sep_token_id]
						A__				:       Any	  =						[2]

						if token_ids_a is None:
							return len(token_ids_a + sep     ) * [0] + cls_segment_id
						return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer."""     )

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

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

						return (out_vocab_file,)






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_			:					Any    			=     logging.get_logger(__name__)



class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			None



@experimental
def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					Any       , lowercase_:					str       , lowercase_:					str       , lowercase_:					List[str]       , lowercase_:					List[Any]       , lowercase_:					Union[str, Any]   )	-> Union[str, Any]:
	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	return _map_with_joblib(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[Any]       , lowercase_:					Tuple       , lowercase_:					Tuple       , lowercase_:					List[str]       , lowercase_:					Dict       , lowercase_:					Tuple   )	-> Tuple:
	A__				:       Union[str, Any]	  =						num_proc if num_proc <= len(lowercase_   ) else len(lowercase_   )
	A__				:       str	  =						[]  # We organize the splits ourselve (contiguous splits)
	for index in range(lowercase_   ):
		A__				:       Tuple	  =						len(lowercase_   ) // num_proc
		A__				:       Tuple	  =						len(lowercase_   ) % num_proc
		A__				:       Optional[Any]	  =						div * index + min(lowercase_       , lowercase_   )
		A__				:       Union[str, Any]	  =						start + div + (1 if index < mod else 0)
		split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

	if len(lowercase_   ) != sum(len(i[1]   ) for i in split_kwds   ):
		raise ValueError(
		    f"""Error dividing inputs iterable among processes. """
		    f"""Total number of objects {len(lowercase_   )}, """
		    f"""length: {sum(len(i[1]   ) for i in split_kwds   )}"""   )

	logger.info(
	    f"""Spawning {num_proc} processes for {len(lowercase_   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}"""   )
	A__  ,  A__				:       Optional[int]	  =						None, None
	if not disable_tqdm:
		A__  ,  A__				:       List[str]	  =						(RLock(),), tqdm.set_lock
	with Pool(lowercase_       , initargs=lowercase_       , initializer=lowercase_   ) as pool:
		A__				:       Optional[Any]	  =						pool.map(lowercase_       , lowercase_   )
	logger.info(f"""Finished {num_proc} processes"""   )
	A__				:       Tuple	  =						[obj for proc_res in mapped for obj in proc_res]
	logger.info(f"""Unpacked {len(lowercase_   )} objects"""   )

	return mapped

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Dict       , lowercase_:					str       , lowercase_:					Union[str, Any]       , lowercase_:					List[Any]       , lowercase_:					Dict       , lowercase_:					Optional[Any]   )	-> Union[str, Any]:
	# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
	# and it requires monkey-patching joblib internal classes which is subject to change
	import joblib

	with joblib.parallel_backend(ParallelBackendConfig.backend_name       , n_jobs=lowercase_   ):
		return joblib.Parallel()(
		    joblib.delayed(lowercase_   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 		UpperCamelCase   (lowercase_:					str   )	-> str:
	A__				:       int	  =						backend_name

	if backend_name == "spark":
		from joblibspark import register_spark

		register_spark()

		# TODO: call create_cache_and_write_probe if "download" in steps
		# TODO: raise NotImplementedError when Dataset.map etc is called

	try:
		yield
	finally:
		A__				:       Optional[Any]	  =						None



