
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__							:							str									=			logging.get_logger(__name__)

lowercase__							:							Union[str, Any]									=			"▁"

lowercase__							:							Dict									=			{"vocab_file": "sentencepiece.bpe.model"}

lowercase__							:							List[str]									=			{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowercase__							:							Optional[Any]									=			{
    "xlm-roberta-base": 5_1_2,
    "xlm-roberta-large": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-english": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-german": 5_1_2,
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  VOCAB_FILES_NAMES
							_snake_case     		=  PRETRAINED_VOCAB_FILES_MAP
							_snake_case     		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case     		=  ['input_ids', 'attention_mask']



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_="<s>"     ,	SCREAMING_SNAKE_CASE_="</s>"     ,	SCREAMING_SNAKE_CASE_="</s>"     ,	SCREAMING_SNAKE_CASE_="<s>"     ,	SCREAMING_SNAKE_CASE_="<unk>"     ,	SCREAMING_SNAKE_CASE_="<pad>"     ,	SCREAMING_SNAKE_CASE_="<mask>"     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   None:





													'''simple docstring'''






													__UpperCamelCase       =			AddedToken(SCREAMING_SNAKE_CASE_     ,	lstrip=SCREAMING_SNAKE_CASE_     ,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) else mask_token

													__UpperCamelCase       =			{} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    bos_token=SCREAMING_SNAKE_CASE_     ,	eos_token=SCREAMING_SNAKE_CASE_     ,	unk_token=SCREAMING_SNAKE_CASE_     ,	sep_token=SCREAMING_SNAKE_CASE_     ,	cls_token=SCREAMING_SNAKE_CASE_     ,	pad_token=SCREAMING_SNAKE_CASE_     ,	mask_token=SCREAMING_SNAKE_CASE_     ,	sp_model_kwargs=self.sp_model_kwargs     ,	**SCREAMING_SNAKE_CASE_     ,	)

													__UpperCamelCase       =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
													self.sp_model.Load(str(SCREAMING_SNAKE_CASE_			)			)
													__UpperCamelCase       =			vocab_file

													# Original fairseq vocab and spm vocab must be "aligned":
													# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
													# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
													# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
													# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

													# Mimic fairseq token-to-id alignment for the first 4 token
													__UpperCamelCase       =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

													# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
													__UpperCamelCase       =			1

													__UpperCamelCase       =			len(self.sp_model			) + self.fairseq_offset
													__UpperCamelCase       =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}



							def __getstate__(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.__dict__.copy()
													__UpperCamelCase       =			None
													__UpperCamelCase       =			self.sp_model.serialized_model_proto()
													return state



							def __setstate__(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			d

													# for backward compatibility
													if not hasattr(self     ,	'''sp_model_kwargs'''			):
																			__UpperCamelCase       =			{}

													__UpperCamelCase       =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
													self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   List[int]:





													'''simple docstring'''






													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													__UpperCamelCase       =			[self.cls_token_id]
													__UpperCamelCase       =			[self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False			)->   List[int]:





													'''simple docstring'''






													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=SCREAMING_SNAKE_CASE_     ,	token_ids_a=SCREAMING_SNAKE_CASE_     ,	already_has_special_tokens=SCREAMING_SNAKE_CASE_			)

													if token_ids_a is None:
																			return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
													return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   List[int]:





													'''simple docstring'''






													__UpperCamelCase       =			[self.sep_token_id]
													__UpperCamelCase       =			[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 + sep + token_ids_a + sep			) * [0]



							@property
							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													return len(self.sp_model			) + self.fairseq_offset + 1  # Add the <mask> token



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_			): i for i in range(self.vocab_size			)}
													vocab.update(self.added_tokens_encoder			)
													return vocab



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													return self.sp_model.encode(SCREAMING_SNAKE_CASE_     ,	out_type=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
													__UpperCamelCase       =			self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_			)

													# Need to return unknown token if the SP model returned 0
													return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






													if index in self.fairseq_ids_to_tokens:
																			return self.fairseq_ids_to_tokens[index]
													return self.sp_model.IdToPiece(index - self.fairseq_offset			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			''''''.join(SCREAMING_SNAKE_CASE_			).replace(SCREAMING_SNAKE_CASE_     ,	''' '''			).strip()
													return out_string



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple[str]:





													'''simple docstring'''






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

													if os.path.abspath(self.vocab_file			) != os.path.abspath(SCREAMING_SNAKE_CASE_			) and os.path.isfile(self.vocab_file			):
																			copyfile(self.vocab_file     ,	SCREAMING_SNAKE_CASE_			)
													elif not os.path.isfile(self.vocab_file			):
																			with open(SCREAMING_SNAKE_CASE_     ,	'''wb'''			) as fi:
																									__UpperCamelCase       =			self.sp_model.serialized_model_proto()
																									fi.write(SCREAMING_SNAKE_CASE_			)

													return (out_vocab_file,)






from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 	A_ (							snake_case							:   int )      ->    int:



						'''simple docstring'''

						def is_in_circle(snake_case							:   float ,      snake_case							:   float ) -> bool:
												__UpperCamelCase       =			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
						__UpperCamelCase       =			mean(
						    int(is_in_circle(uniform(-1.0 ,      1.0 ) ,      uniform(-1.0 ,      1.0 ) ) )
						    for _ in range(snake_case ) )
						# The ratio of the area for circle to square is pi/4.
						__UpperCamelCase       =			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_ (							snake_case							:   int ,      snake_case							:   Callable[[float], float] ,      snake_case							:   float = 0.0 ,      snake_case							:   float = 1.0 ,      )      ->    float:



						'''simple docstring'''

						return mean(
						    function_to_integrate(uniform(snake_case ,      snake_case ) ) for _ in range(snake_case ) ) * (max_value - min_value)




def 	A_ (							snake_case							:   int ,      snake_case							:   float = 0.0 ,      snake_case							:   float = 1.0 )      ->    None:



						'''simple docstring'''

						def identity_function(snake_case							:   float ) -> float:
												return x

						__UpperCamelCase       =			area_under_curve_estimator(
						    snake_case ,      snake_case ,      snake_case ,      snake_case )
						__UpperCamelCase       =			(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_ (							snake_case							:   int )      ->    None:



						'''simple docstring'''

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

						__UpperCamelCase       =			area_under_curve_estimator(
						    snake_case ,      snake_case ,      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()




