




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


a													=			logging.get_logger(__name__)

a													=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a													=			{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

a													=			{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}




class       _A				(		__lowercase     ):
		__a					    =       VOCAB_FILES_NAMES
		__a					    =       PRETRAINED_VOCAB_FILES_MAP
		__a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       ["""input_ids""", """attention_mask"""]
		__a					    =       GPTaTokenizer






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE="<|endoftext|>"						,		_SCREAMING_SNAKE_CASE="<|endoftext|>"						,		_SCREAMING_SNAKE_CASE="<|endoftext|>"						,		_SCREAMING_SNAKE_CASE=False						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		tokenizer_file=_SCREAMING_SNAKE_CASE						,		unk_token=_SCREAMING_SNAKE_CASE						,		bos_token=_SCREAMING_SNAKE_CASE						,		eos_token=_SCREAMING_SNAKE_CASE						,		add_prefix_space=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)

								_UpperCAmelCase      =			kwargs.pop("""add_bos_token"""						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
								if pre_tok_state.get("""add_prefix_space"""						,		_SCREAMING_SNAKE_CASE			) != add_prefix_space:
														_UpperCAmelCase      =			getattr(_SCREAMING_SNAKE_CASE						,		pre_tok_state.pop("""type"""			)			)
														_UpperCAmelCase      =			add_prefix_space
														_UpperCAmelCase      =			pre_tok_class(**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			add_prefix_space






		def 				UpperCAmelCase (    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			kwargs.get("""is_split_into_words"""						,		_SCREAMING_SNAKE_CASE			)
								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(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			kwargs.get("""is_split_into_words"""						,		_SCREAMING_SNAKE_CASE			)

								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(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,		name=_SCREAMING_SNAKE_CASE			)
								return tuple(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			[]
								for is_user, text in conversation.iter_texts():
														input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,		add_special_tokens=_SCREAMING_SNAKE_CASE			) + [self.eos_token_id]			)

								if len(_SCREAMING_SNAKE_CASE			) > self.model_max_length:
														_UpperCAmelCase      =			input_ids[-self.model_max_length :]
								return input_ids





from __future__ import annotations

from scipy.special import comb  # type: ignore




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			list_of_points
								# Degree determines the flexibility of the curve.
								# Degree = 1 will produce a straight line.
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) - 1






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."
								_UpperCAmelCase      =			[]
								for i in range(len(self.list_of_points			)			):
														# basis function for each i
														output_values.append(
														    comb(self.degree						,		_SCREAMING_SNAKE_CASE			) * ((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(_SCREAMING_SNAKE_CASE			)						,		5			) == 1
								return output_values






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."

								_UpperCAmelCase      =			self.basis_function(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			0.0
								_UpperCAmelCase      =			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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = 0.01			):
								from matplotlib import pyplot as plt  # type: ignore

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

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

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

								plt.plot(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		color="""blue"""						,		label="""Curve of Degree """ + str(self.degree			)						,		)
								plt.scatter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		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