



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:
	UpperCAmelCase							 =					None


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase							 =					{
    """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""",
    },
}

UpperCAmelCase							 =					{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

UpperCAmelCase							 =					"""▁"""

# Segments (not really needed)
UpperCAmelCase							 =					0
UpperCAmelCase							 =					1
UpperCAmelCase							 =					2
UpperCAmelCase							 =					3
UpperCAmelCase							 =					4

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      int         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      Optional[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      Tuple         =					"""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__				      =		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__				      =		3
											a__				      =		do_lower_case
											a__				      =		remove_space
											a__				      =		keep_accents
											a__				      =		vocab_file
											a__				      =		False if not self.vocab_file else True


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.sep_token_id]
											a__				      =		[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__				      =		[self.sep_token_id]
											a__				      =		[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__				      =		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,)






def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		set()

				# Replace all the whitespace in our sentence
				a__				      =		input_str.replace(""" """ ,		""""""		)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()		)
				return len(__a		) == 26

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		[False] * 26
				for char in input_str:
								if char.islower():
												a__				      =		True
								elif char.isupper():
												a__				      =		True
				return all(__a		)

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def 			A_      (	):

				"""simple docstring"""
				from timeit import timeit

				a__				      =		"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
				print(timeit("""is_pangram()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_faster()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_fastest()""" ,		setup=__a		)		)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()

