





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class    A__  (			__snake_case			,  unittest.TestCase     ):
      _UpperCAmelCase      :List[str]		       =  MvpTokenizer
      _UpperCAmelCase      :Tuple		       =  MvpTokenizerFast
      _UpperCAmelCase      :Tuple		       =  True
      _UpperCAmelCase      :str		       =  filter_roberta_detectors





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       super().setUp()
       UpperCamelCase      :   List[Any]			      =  [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       UpperCamelCase      :   Tuple			      =  dict(zip(A_			,				range(len(A_  )  )  )  )
       UpperCamelCase      :   Tuple			      =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       UpperCamelCase      :   int			      =  {"unk_token": "<unk>"}

       UpperCamelCase      :   Dict			      =  os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES["vocab_file"]  )
       UpperCamelCase      :   Any			      =  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(A_  ) + "\n"  )
       with open(self.merges_file			,				"w"			,				encoding="utf-8"  ) as fp:
        fp.write("\n".join(A_  )  )





      def 			__UpperCamelCase(					self			,				**A_  ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname			,				**A_  )





      def 			__UpperCamelCase(					self			,				**A_  ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map  )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,				**A_  )





      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       return "lower newer", "lower newer"





      @cached_property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return MvpTokenizer.from_pretrained("RUCAIBox/mvp"  )





      @cached_property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"  )





      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
       UpperCamelCase      :   Tuple			      =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase      :   Optional[int]			      =  tokenizer(A_			,				max_length=len(A_  )			,				padding=A_			,				return_tensors="pt"  )
        self.assertIsInstance(A_			,				A_  )

        self.assertEqual((2, 9)			,				batch.input_ids.shape  )
        self.assertEqual((2, 9)			,				batch.attention_mask.shape  )
        UpperCamelCase      :   Any			      =  batch.input_ids.tolist()[0]
        self.assertListEqual(A_			,				A_  )
        # Test that special tokens are reset





      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase      :   List[str]			      =  tokenizer(A_			,				padding=A_			,				return_tensors="pt"  )
        # check if input_ids are returned and no labels
        self.assertIn("input_ids"			,				A_  )
        self.assertIn("attention_mask"			,				A_  )
        self.assertNotIn("labels"			,				A_  )
        self.assertNotIn("decoder_attention_mask"			,				A_  )





      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  [
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase      :   List[str]			      =  tokenizer(text_target=A_			,				max_length=32			,				padding="max_length"			,				return_tensors="pt"  )
        self.assertEqual(32			,				targets["input_ids"].shape[1]  )





      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase      :   Optional[Any]			      =  tokenizer(
            ["I am a small frog" * 1024, "I am a small frog"]			,				padding=A_			,				truncation=A_			,				return_tensors="pt"  )
        self.assertIsInstance(A_			,				A_  )
        self.assertEqual(batch.input_ids.shape			,				(2, 1024)  )





      @require_torch
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  ["A long paragraph for summarization."]
       UpperCamelCase      :   Union[str, Any]			      =  [
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        UpperCamelCase      :   List[str]			      =  tokenizer(A_			,				text_target=A_			,				return_tensors="pt"  )
        UpperCamelCase      :   Union[str, Any]			      =  inputs["input_ids"]
        UpperCamelCase      :   Tuple			      =  inputs["labels"]
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
         UpperCamelCase      :   List[Any]			      =  self.rust_tokenizer_class.from_pretrained(A_			,				**A_  )
         UpperCamelCase      :   int			      =  self.tokenizer_class.from_pretrained(A_			,				**A_  )
         UpperCamelCase      :   int			      =  "A, <mask> AllenNLP sentence."
         UpperCamelCase      :   int			      =  tokenizer_r.encode_plus(A_			,				add_special_tokens=A_			,				return_token_type_ids=A_  )
         UpperCamelCase      :   Optional[Any]			      =  tokenizer_p.encode_plus(A_			,				add_special_tokens=A_			,				return_token_type_ids=A_  )

         # token_type_ids should put 0 everywhere
         self.assertEqual(sum(tokens_r["token_type_ids"]  )			,				sum(tokens_p["token_type_ids"]  )  )

         # attention_mask should put 1 everywhere, so sum over length should be 1
         self.assertEqual(
             sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )			,				sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )			,				)

         UpperCamelCase      :   List[Any]			      =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
         UpperCamelCase      :   Optional[Any]			      =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )

         # Rust correctly handles the space before the mask while python doesnt
         self.assertSequenceEqual(tokens_p["input_ids"]			,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
         self.assertSequenceEqual(tokens_r["input_ids"]			,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

         self.assertSequenceEqual(
             A_			,				["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
         self.assertSequenceEqual(
             A_			,				["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )







from __future__ import annotations

import math



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					float:
 UpperCamelCase      :   Tuple			      =  u
 for i in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Any			      =  temp * (u - i)
 return temp



def 						A_							(	)		->					None:
 UpperCamelCase      :   Union[str, Any]			      =  int(input("enter the numbers of values: "	)	)
 UpperCamelCase      :   list[list[float]]			      =  []
 for _ in range(_lowerCAmelCase	):
  y.append([]	)
 for i in range(_lowerCAmelCase	):
  for j in range(_lowerCAmelCase	):
   y[i].append(_lowerCAmelCase	)
   UpperCamelCase      :   List[str]			      =  0

 print("enter the values of parameters in a list: "	)
 UpperCamelCase      :   Optional[Any]			      =  list(map(_lowerCAmelCase				,  input().split()	)	)

 print("enter the values of corresponding parameters: "	)
 for i in range(_lowerCAmelCase	):
  UpperCamelCase      :   Tuple			      =  float(input()	)

 UpperCamelCase      :   Any			      =  int(input("enter the value to interpolate: "	)	)
 UpperCamelCase      :   List[Any]			      =  (value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1				,  _lowerCAmelCase	):
  for j in range(n - i	):
   UpperCamelCase      :   Optional[Any]			      =  y[j + 1][i - 1] - y[j][i - 1]

 UpperCamelCase      :   List[str]			      =  y[0][0]
 for i in range(1				,  _lowerCAmelCase	):
  summ += (ucal(_lowerCAmelCase				,  _lowerCAmelCase	) * y[0][i]) / math.factorial(_lowerCAmelCase	)

 print(F"""the value at {value} is {summ}"""	)


if __name__ == "__main__":
    main()
