


import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

lowercase_			=  logging.get_logger(__name__)


lowercase_			=  {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_			=  {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

lowercase_			=  {"facebook/blenderbot-3B": 128}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase		     =							['input_ids', 'attention_mask']
    lowerCamelCase		     =							BlenderbotTokenizer


    def __init__(				self			:	Any,lowercase_			:	Optional[int]=None,lowercase_			:	List[str]=None,lowercase_			:	List[Any]=None,lowercase_			:	Union[str, Any]="replace",lowercase_			:	Optional[Any]="<s>",lowercase_			:	List[Any]="</s>",lowercase_			:	Dict="</s>",lowercase_			:	Optional[int]="<s>",lowercase_			:	Optional[int]="<unk>",lowercase_			:	Dict="<pad>",lowercase_			:	Optional[Any]="<mask>",lowercase_			:	List[str]=False,lowercase_			:	Tuple=True,**lowercase_			:	List[Any],)->   List[str]:





        '''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_,)

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

        A__   				=       add_prefix_space

        A__   				=       'post_processor'
        A__   				=       getattr(self.backend_tokenizer,lowercase_,lowercase_		)
        if tokenizer_component_instance:
            A__   				=       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:
                A__   				=       tuple(state['sep']		)
            if "cls" in state:
                A__   				=       tuple(state['cls']		)

            A__   				=       False

            if state.get('add_prefix_space',lowercase_		) != add_prefix_space:
                A__   				=       add_prefix_space
                A__   				=       True

            if state.get('trim_offsets',lowercase_		) != trim_offsets:
                A__   				=       trim_offsets
                A__   				=       True

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


    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def 						snake_case__					(				self			:	str		)->   str:





        '''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 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Dict		)->   Any:





        '''simple docstring'''







        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else value
        A__   				=       value


    def 						snake_case__					(				self			:	Optional[Any],*lowercase_			:	Dict,**lowercase_			:	Optional[int]		)->   BatchEncoding:





        '''simple docstring'''







        A__   				=       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 						snake_case__					(				self			:	Any,*lowercase_			:	Tuple,**lowercase_			:	int		)->   BatchEncoding:





        '''simple docstring'''







        A__   				=       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 						snake_case__					(				self			:	List[str],lowercase_			:	str,lowercase_			:	Optional[str] = None		)->   Tuple[str]:





        '''simple docstring'''







        A__   				=       self._tokenizer.model.save(lowercase_,name=lowercase_		)
        return tuple(lowercase_		)


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   List[int]:





        '''simple docstring'''







        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 + sep + token_ids_a + sep		) * [0]


    def 						snake_case__					(				self			:	Dict,lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   Dict:





        '''simple docstring'''







        return token_ids_a + [self.eos_token_id]


    def 						snake_case__					(				self			:	List[str],lowercase_			:	"Conversation"		)->   List[int]:





        '''simple docstring'''







        A__   				=       []
        for is_user, text in conversation.iter_texts():
            if is_user:
                # We need to space prefix as it's being done within blenderbot
                inputs.append(' ' + text		)
            else:
                # Generated responses should contain them already.
                inputs.append(lowercase_		)

        A__   				=       '  '.join(lowercase_		)
        A__   				=       self.encode(lowercase_		)
        if len(lowercase_		) > self.model_max_length:
            A__   				=       input_ids[-self.model_max_length :]
            logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'		)
        return input_ids




from __future__ import annotations

from scipy.special import comb  # type: ignore


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	list[tuple[float, float]]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       list_of_points
        # Degree determines the flexibility of the curve.
        # Degree = 1 will produce a straight line.
        A__   				=       len(lowercase_		) - 1


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	float		)->   list[float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."
        A__   				=       []
        for i in range(len(self.list_of_points		)		):
            # basis function for each i
            output_values.append(
                comb(self.degree,lowercase_		) * ((1 - t) ** (self.degree - i)) * (t**i)		)
        # the basis must sum up to 1 for it to produce a valid Bezier curve.
        assert round(sum(lowercase_		),5		) == 1
        return output_values


    def 						snake_case__					(				self			:	str,lowercase_			:	float		)->   tuple[float, float]:





        '''simple docstring'''







        assert 0 <= t <= 1, "Time t must be between 0 and 1."

        A__   				=       self.basis_function(lowercase_		)
        A__   				=       0.0
        A__   				=       0.0
        for i in range(len(self.list_of_points		)		):
            # For all points, sum up the product of i-th basis function and i-th point.
            x += basis_function[i] * self.list_of_points[i][0]
            y += basis_function[i] * self.list_of_points[i][1]
        return (x, y)


    def 						snake_case__					(				self			:	str,lowercase_			:	float = 0.01		)->   str:





        '''simple docstring'''







        from matplotlib import pyplot as plt  # type: ignore

        A__   				=       []  # x coordinates of points to plot
        A__   				=       []  # y coordinates of points to plot

        A__   				=       0.0
        while t <= 1:
            A__   				=       self.bezier_curve_function(lowercase_		)
            to_plot_x.append(value[0]		)
            to_plot_y.append(value[1]		)
            t += step_size

        A__   				=       [i[0] for i in self.list_of_points]
        A__   				=       [i[1] for i in self.list_of_points]

        plt.plot(
            lowercase_,lowercase_,color='blue',label='Curve of Degree ' + str(self.degree		),)
        plt.scatter(lowercase_,lowercase_,color='red',label='Control Points'		)
        plt.legend()
        plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
     BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
     BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

