



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:
	UpperCAmelCase							 =					None

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

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

UpperCAmelCase							 =					{
    """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,
}

UpperCAmelCase							 =					"""▁"""

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Tuple         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Union[str, Any]         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[str]         =					AlbertTokenizer


							def __init__(							self  ,  a_=None  ,  a_=None  ,  a_=True  ,  a_=True  ,  a_=False  ,  a_="[CLS]"  ,  a_="[SEP]"  ,  a_="<unk>"  ,  a_="[SEP]"  ,  a_="<pad>"  ,  a_="[CLS]"  ,  a_="[MASK]"  ,  **a_  ,  ):
											# 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.
											a__				      =		(
											    AddedToken(a_  ,  lstrip=a_  ,  rstrip=a_  ,  normalized=a_		)
											    if isinstance(a_  ,  a_		)
											    else mask_token
											)

											super().__init__(
											    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_  ,  **a_  ,  )

											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 cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep


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






from __future__ import annotations

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

