






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						BlenderbotSmallTokenizer
     UpperCamelCase_				:		int								=						False


     def 					_A							(					self							:						Union[str, Any]   ):
       super().setUp()

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     dict(zip(UpperCAmelCase_       ,			range(len(UpperCAmelCase_   )   )   )   )

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
       SCREAMING_SNAKE_CASE							:   int						    =     {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["vocab_file"]   )
       SCREAMING_SNAKE_CASE							:   str						    =     os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["merges_file"]   )
       with open(self.vocab_file       ,			"w"       ,			encoding="utf-8"   ) as fp:
         fp.write(json.dumps(UpperCAmelCase_   ) + "\n"   )
       with open(self.merges_file       ,			"w"       ,			encoding="utf-8"   ) as fp:
         fp.write("\n".join(UpperCAmelCase_   )   )


     def 					_A							(					self							:						List[Any]       ,			**UpperCAmelCase_							:						str   ):
       kwargs.update(self.special_tokens_map   )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     "adapt act apte"
       SCREAMING_SNAKE_CASE							:   int						    =     "adapt act apte"
       return input_text, output_text


     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE							:   int						    =     BlenderbotSmallTokenizer(self.vocab_file       ,			self.merges_file       ,			**self.special_tokens_map   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     "adapt act apte"
       SCREAMING_SNAKE_CASE							:   List[str]						    =     ["adapt", "act", "ap@@", "te"]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tokenizer.tokenize(UpperCAmelCase_   )
       self.assertListEqual(UpperCAmelCase_       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

       SCREAMING_SNAKE_CASE							:   Tuple						    =     [0, 1, 2, 3, 4, 5]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"   )
       assert tok("sam"   ).input_ids == [1384]
       SCREAMING_SNAKE_CASE							:   str						    =     "I am a small frog."
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     tok([src_text]       ,			padding=UpperCAmelCase_       ,			truncation=UpperCAmelCase_   )["input_ids"]
       SCREAMING_SNAKE_CASE							:   int						    =     tok.batch_decode(UpperCAmelCase_       ,			skip_special_tokens=UpperCAmelCase_       ,			clean_up_tokenization_spaces=UpperCAmelCase_   )[0]
       assert src_text != decoded  # I wish it did!
       assert decoded == "i am a small frog ."






     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     "I am a small frog ."
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     "."
       SCREAMING_SNAKE_CASE							:   Dict						    =     tok(UpperCAmelCase_   )["input_ids"]
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tok(UpperCAmelCase_   )["input_ids"]

       assert encoded[-1] == encoded_dot[0]












'''simple docstring'''


from __future__ import annotations

import math
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   u
						for i in range(1    ,	a							):
												__snake_case						 =   temp * (u - i)
						return temp
def 						lowerCamelCase__   (				):
						__snake_case						 =   int(input('enter the numbers of values: '							)							)
						__snake_case						 =   []
						for _ in range(a							):
												y.append([]							)
						for i in range(a							):
												for j in range(a							):
																		y[i].append(a							)
																		__snake_case						 =   0

						print('enter the values of parameters in a list: '							)
						__snake_case						 =   list(map(a    ,	input().split()							)							)

						print('enter the values of corresponding parameters: '							)
						for i in range(a							):
												__snake_case						 =   float(input()							)

						__snake_case						 =   int(input('enter the value to interpolate: '							)							)
						__snake_case						 =   (value - x[0]) / (x[1] - x[0])

						# for calculating forward difference table

						for i in range(1    ,	a							):
												for j in range(n - i							):
																		__snake_case						 =   y[j + 1][i - 1] - y[j][i - 1]

						__snake_case						 =   y[0][0]
						for i in range(1    ,	a							):
												summ += (ucal(a    ,	a							) * y[0][i]) / math.factorial(a							)

						print(f'the value at {value} is {summ}'							)


if __name__ == "__main__":
	main()

