


"""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:
				lowercase_			     =  None

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

lowercase_			     =  {
    "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",
    },
}

lowercase_			     =  {
    "albert-base-v1": 5_1_2,
    "albert-large-v1": 5_1_2,
    "albert-xlarge-v1": 5_1_2,
    "albert-xxlarge-v1": 5_1_2,
    "albert-base-v2": 5_1_2,
    "albert-large-v2": 5_1_2,
    "albert-xlarge-v2": 5_1_2,
    "albert-xxlarge-v2": 5_1_2,
}

lowercase_			     =  "▁"
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				VOCAB_FILES_NAMES
		__UpperCAmelCase		:			List[Any]										=				PRETRAINED_VOCAB_FILES_MAP
		__UpperCAmelCase		:			str										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCAmelCase		:			int										=				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 			__UpperCAmelCase       (  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 			__UpperCAmelCase       (  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 			__UpperCAmelCase       (  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 collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def A_	(		A__				)  ->		Tuple:

       # A local function to see if a dot lands in the circle.
       def is_in_circle(A__		,      A__				) -> bool:
              a__						:	List[str] =				sqrt((x**2) + (y**2)				)
              # Our circle has a radius of 1, so a distance
              # greater than 1 would land outside the circle.
              return distance_from_centre <= 1

       # The proportion of guesses that landed in the circle
       a__						:	List[str] =				mean(
           int(is_in_circle(uniform(-1.0		,      1.0				)		,      uniform(-1.0		,      1.0				)				)				)
           for _ in range(A__				)				)
       # The ratio of the area for circle to square is pi/4.
       a__						:	Optional[Any] =				proportion * 4
       print(F'The estimated value of pi is {pi_estimate}'				)
       print(F'The numpy value of pi is {pi}'				)
       print(F'The total error is {abs(pi - pi_estimate				)}'				)



def A_	(		A__		,      A__		,      A__ = 0.0		,      A__ = 1.0		,      )  ->		float:
       return mean(
           function_to_integrate(uniform(A__		,      A__				)				) for _ in range(A__				)				) * (max_value - min_value)



def A_	(		A__		,      A__ = 0.0		,      A__ = 1.0				)  ->		None:

       def identity_function(A__				) -> float:
              return x

       a__						:	List[Any] =				area_under_curve_estimator(
           A__		,      A__		,      A__		,      A__				)
       a__						:	Union[str, Any] =				(max_value * max_value - min_value * min_value) / 2

       print('******************'				)
       print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {expected_value}'				)
       print(F'Total error is {abs(estimated_value - expected_value				)}'				)
       print('******************'				)



def A_	(		A__				)  ->		None:

       def function_to_integrate(A__				) -> float:
              return sqrt(4.0 - x * x				)

       a__						:	Dict =				area_under_curve_estimator(
           A__		,      A__		,      0.0		,      2.0				)

       print('******************'				)
       print('Estimating pi using area_under_curve_estimator'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {pi}'				)
       print(F'Total error is {abs(estimated_value - pi				)}'				)
       print('******************'				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


