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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE				:Any									=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:Union[str, Any]									=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE				:Union[str, Any]									=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE				:str									=							{
    'gpt-neox-20b': 2048,
}





class 				UpperCAmelCase (    __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   :					str				,A   :					List[Any]=None				,A   :					str=None				,A   :					str=None				,A   :					Tuple="<|endoftext|>"				,A   :					Union[str, Any]="<|endoftext|>"				,A   :					Union[str, Any]="<|endoftext|>"				,A   :					Union[str, Any]=False				,**A   :					List[Any]				,):
					super().__init__(
					    A				,A				,tokenizer_file=A				,unk_token=A				,bos_token=A				,eos_token=A				,add_prefix_space=A				,**A				,)

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

					__A       =					add_prefix_space






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					str				,A   :					Optional[str] = None					):
					__A       =					self._tokenizer.model.save(A				,name=A					)
					return tuple(A					)







	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					"Conversation"					):
					__A       =					[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(A				,add_special_tokens=A					) + [self.eos_token_id]					)

					if len(A					) > self.model_max_length:
									__A       =					input_ids[-self.model_max_length :]
					return input_ids





import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
							# Create universe of discourse in Python using linspace ()
							SCREAMING_SNAKE_CASE__   			=   np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

							# Create two fuzzy sets by defining any membership function
							# (trapmf(), gbellmf(), gaussmf(), etc).
							SCREAMING_SNAKE_CASE__   			=   [0, 2_5, 5_0]
							SCREAMING_SNAKE_CASE__   			=   [2_5, 5_0, 7_5]
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)

							# Compute the different operations using inbuilt functions.
							SCREAMING_SNAKE_CASE__   			=   np.ones(7_5)
							SCREAMING_SNAKE_CASE__   			=   np.zeros((7_5,))
							# 1. Union = max(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, young, X, middle_aged)[1]
							# 2. Intersection = min(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, middle_aged)[1]
							# 3. Complement (A) = (1- min(µA(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_not(young)
							# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
							# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
							SCREAMING_SNAKE_CASE__   			=   young + middle_aged - (young * middle_aged)
							# 6. Algebraic Product = (µA(x) * µB(x))
							SCREAMING_SNAKE_CASE__   			=   young * middle_aged
							# 7. Bounded Sum = min[1,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
							# 8. Bounded difference = min[0,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

							# max-min composition
							# max-product composition

							# Plot each set A, set B and each operation result using plot() and subplot().
							from matplotlib import pyplot as plt

							plt.figure()

							plt.subplot(4, 3, 1)
							plt.plot(X, young)
							plt.title('''Young''')
							plt.grid(True)

							plt.subplot(4, 3, 2)
							plt.plot(X, middle_aged)
							plt.title('''Middle aged''')
							plt.grid(True)

							plt.subplot(4, 3, 3)
							plt.plot(X, union)
							plt.title('''union''')
							plt.grid(True)

							plt.subplot(4, 3, 4)
							plt.plot(X, intersection)
							plt.title('''intersection''')
							plt.grid(True)

							plt.subplot(4, 3, 5)
							plt.plot(X, complement_a)
							plt.title('''complement_a''')
							plt.grid(True)

							plt.subplot(4, 3, 6)
							plt.plot(X, difference)
							plt.title('''difference a/b''')
							plt.grid(True)

							plt.subplot(4, 3, 7)
							plt.plot(X, alg_sum)
							plt.title('''alg_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 8)
							plt.plot(X, alg_product)
							plt.title('''alg_product''')
							plt.grid(True)

							plt.subplot(4, 3, 9)
							plt.plot(X, bdd_sum)
							plt.title('''bdd_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 1_0)
							plt.plot(X, bdd_difference)
							plt.title('''bdd_difference''')
							plt.grid(True)

							plt.subplots_adjust(hspace=0.5)
							plt.show()




