'''simple docstring'''

import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =BartTokenizer
   lowerCamelCase_            =BartTokenizerFast
   lowerCamelCase_            =True
   lowerCamelCase_            =filter_roberta_detectors


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

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


   def __UpperCAmelCase							( self					:						str ,		**__lowerCAmelCase					:						Tuple)   ->       List[str]:
         kwargs.update(self.special_tokens_map)
         return self.tokenizer_class.from_pretrained(self.tmpdirname ,		**__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Dict ,		**__lowerCAmelCase					:						Tuple)   ->       List[Any]:
         kwargs.update(self.special_tokens_map)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,		**__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						Optional[Any])   ->       Union[str, Any]:
         return "lower newer", "lower newer"


   @cached_property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Tuple:
         return BartTokenizer.from_pretrained("facebook/bart-large")


   @cached_property
   def __UpperCAmelCase							( self					:						Dict)   ->       Tuple:
         return BartTokenizerFast.from_pretrained("facebook/bart-large")


   @require_torch
   def __UpperCAmelCase							( self					:						int)   ->       int:
         lowercase_     			=   ["A long paragraph for summarization.", "Another paragraph for summarization."]
         lowercase_     			=   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     			=   tokenizer(__lowerCAmelCase ,		max_length=len(__lowerCAmelCase) ,		padding=__lowerCAmelCase ,		return_tensors="pt")
               self.assertIsInstance(__lowerCAmelCase ,		__lowerCAmelCase)

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


   @require_torch
   def __UpperCAmelCase							( self					:						List[Any])   ->       Optional[Any]:
         lowercase_     			=   ["A long paragraph for summarization.", "Another paragraph for summarization."]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     			=   tokenizer(__lowerCAmelCase ,		padding=__lowerCAmelCase ,		return_tensors="pt")
               # check if input_ids are returned and no labels
               self.assertIn("input_ids" ,		__lowerCAmelCase)
               self.assertIn("attention_mask" ,		__lowerCAmelCase)
               self.assertNotIn("labels" ,		__lowerCAmelCase)
               self.assertNotIn("decoder_attention_mask" ,		__lowerCAmelCase)


   @require_torch
   def __UpperCAmelCase							( self					:						List[str])   ->       Any:
         lowercase_     			=   [
             "Summary of the text.",
             "Another summary.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     			=   tokenizer(text_target=__lowerCAmelCase ,		max_length=32 ,		padding="max_length" ,		return_tensors="pt")
               self.assertEqual(32 ,		targets["input_ids"].shape[1])


   @require_torch
   def __UpperCAmelCase							( self					:						Optional[int])   ->       List[Any]:
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     			=   tokenizer(
                   ["I am a small frog" * 1024, "I am a small frog"] ,		padding=__lowerCAmelCase ,		truncation=__lowerCAmelCase ,		return_tensors="pt")
               self.assertIsInstance(__lowerCAmelCase ,		__lowerCAmelCase)
               self.assertEqual(batch.input_ids.shape ,		(2, 1024))


   @require_torch
   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       Optional[int]:
         lowercase_     			=   ["A long paragraph for summarization."]
         lowercase_     			=   [
             "Summary of the text.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     			=   tokenizer(__lowerCAmelCase ,		return_tensors="pt")
               lowercase_     			=   tokenizer(text_target=__lowerCAmelCase ,		return_tensors="pt")
               lowercase_     			=   inputs["input_ids"]
               lowercase_     			=   targets["input_ids"]
               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					:						Optional[int])   ->       Dict:
         pass


   def __UpperCAmelCase							( self					:						Dict)   ->       Optional[int]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                     lowercase_     			=   self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase ,		**__lowerCAmelCase)
                     lowercase_     			=   self.tokenizer_class.from_pretrained(__lowerCAmelCase ,		**__lowerCAmelCase)
                     lowercase_     			=   "A, <mask> AllenNLP sentence."
                     lowercase_     			=   tokenizer_r.encode_plus(__lowerCAmelCase ,		add_special_tokens=__lowerCAmelCase ,		return_token_type_ids=__lowerCAmelCase)
                     lowercase_     			=   tokenizer_p.encode_plus(__lowerCAmelCase ,		add_special_tokens=__lowerCAmelCase ,		return_token_type_ids=__lowerCAmelCase)

                     # 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"]) ,		)

                     lowercase_     			=   tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                     lowercase_     			=   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(
                         __lowerCAmelCase ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
                     self.assertSequenceEqual(
                         __lowerCAmelCase ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])







'''simple docstring'''

import timeit





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Any)   ->       Optional[int]:
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):  # Initializes a matrix identical to the one provided.
               lowercase_     			=   arg
               lowercase_     			=   len(__lowerCAmelCase)
         else:  # Initializes a square matrix of the given size and set values to zero.
               lowercase_     			=   arg
               lowercase_     			=   [[0 for _ in range(self.n)] for _ in range(self.n)]


   def __mul__( self					:						int ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   Matrix(self.n)
         for i in range(self.n):
               for j in range(self.n):
                     for k in range(self.n):
                           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
         return matrix




def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Dict					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   Matrix([[1, 0], [0, 1]]					)
      while b > 0:
            if b & 1:
                  matrix *= a
            a *= a
            b >>= 1
      return matrix


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]					)						->				Dict:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa
      lowercase_     			=   Matrix([[1, 1], [1, 0]]					)
      lowercase_     			=   modular_exponentiation(__lowerCamelCase			,		n - 2					)
      return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def __a			(						__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    List[str]					)						->				Optional[Any]:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa

      lowercase_     			=   fa
      lowercase_     			=   fa
      n -= 2

      while n > 0:
            lowercase_			,				lowercase_     			=   fn_a + fn_a, fn_a
            n -= 1

      return fn_a


def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
      lowercase_     			=   "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print("With matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
      lowercase_     			=   "simple_fibonacci(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print(
          "Without matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Tuple:
      '''simple docstring'''


      matrix_exponentiation_time()
      simple_fibonacci_time()


if __name__ == "__main__":
       main()






