
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase__							:							Optional[int]									=			logging.get_logger(__name__)

lowercase__							:							Optional[Any]									=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase__							:							List[Any]									=			{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__							:							List[Any]									=			{
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}






class        SCREAMING_SNAKE_CASE__    (							_UpperCAmelCase    ):

							"""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']
							_snake_case     		=  RobertaTokenizer



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="replace"     ,	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_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													super().__init__(
													    lowercase__     ,	lowercase__     ,	tokenizer_file=lowercase__     ,	errors=lowercase__     ,	bos_token=lowercase__     ,	eos_token=lowercase__     ,	sep_token=lowercase__     ,	cls_token=lowercase__     ,	unk_token=lowercase__     ,	pad_token=lowercase__     ,	mask_token=lowercase__     ,	add_prefix_space=lowercase__     ,	trim_offsets=lowercase__     ,	**lowercase__     ,	)

													__UpperCamelCase       =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
													if pre_tok_state.get('''add_prefix_space'''     ,	lowercase__			) != add_prefix_space:
																			__UpperCamelCase       =			getattr(lowercase__     ,	pre_tok_state.pop('''type'''			)			)
																			__UpperCamelCase       =			add_prefix_space
																			__UpperCamelCase       =			pre_tok_class(**lowercase__			)

													__UpperCamelCase       =			add_prefix_space

													__UpperCamelCase       =			"post_processor"
													__UpperCamelCase       =			getattr(self.backend_tokenizer     ,	lowercase__     ,	lowercase__			)
													if tokenizer_component_instance:
																			__UpperCamelCase       =			json.loads(tokenizer_component_instance.__getstate__()			)

																			# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																			if "sep" in state:
																									__UpperCamelCase       =			tuple(state['''sep''']			)
																			if "cls" in state:
																									__UpperCamelCase       =			tuple(state['''cls''']			)

																			__UpperCamelCase       =			False

																			if state.get('''add_prefix_space'''     ,	lowercase__			) != add_prefix_space:
																									__UpperCamelCase       =			add_prefix_space
																									__UpperCamelCase       =			True

																			if state.get('''trim_offsets'''     ,	lowercase__			) != trim_offsets:
																									__UpperCamelCase       =			trim_offsets
																									__UpperCamelCase       =			True

																			if changes_to_apply:
																									__UpperCamelCase       =			getattr(lowercase__     ,	state.pop('''type'''			)			)
																									__UpperCamelCase       =			component_class(**lowercase__			)
																									setattr(self.backend_tokenizer     ,	lowercase__     ,	lowercase__			)



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





													'''simple docstring'''






													if self._mask_token is None:
																			if self.verbose:
																									logger.error('''Using mask_token, but it is not set yet.'''			)
																			return None
													return str(self._mask_token			)



							@mask_token.setter
							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			AddedToken(lowercase__     ,	lstrip=lowercase__     ,	rstrip=lowercase__			) if isinstance(lowercase__     ,	lowercase__			) else value
													__UpperCamelCase       =			value



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			kwargs.get('''is_split_into_words'''     ,	lowercase__			)
													assert self.add_prefix_space or not is_split_into_words, (
													    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowercase__     ,	**lowercase__			)



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			kwargs.get('''is_split_into_words'''     ,	lowercase__			)

													assert self.add_prefix_space or not is_split_into_words, (
													    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowercase__     ,	**lowercase__			)



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





													'''simple docstring'''






													__UpperCamelCase       =			self._tokenizer.model.save(lowercase__     ,	name=lowercase__			)
													return tuple(lowercase__			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
													if token_ids_a is None:
																			return output

													return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



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





													'''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]






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()




