


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
   _A		       =						'pt'
elif is_tf_available():
   _A		       =						'tf'
else:
   _A		       =						'jax'






class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		List[str]      =  ByTaTokenizer
    UpperCAmelCase__						:		List[Any]      =  False






    def _a					(			self    )  ->       Any:
         super().setUp()
         __UpperCamelCase    							=ByTaTokenizer()
         tokenizer.save_pretrained(self.tmpdirname    )






    @cached_property
    def _a					(			self    )  ->       Any:
         return ByTaTokenizer.from_pretrained('google/byt5-small'    )






    def _a					(			self					,   **A_    )  ->       ByTaTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_					,   A_=False					,   A_=20					,   A_=5    )  ->       Tuple[str, list]:
         # XXX The default common tokenizer tests assume that every ID is decodable on its own.
         # This assumption is invalid for ByT5 because single bytes might not be
         # valid utf-8 (byte 128 for instance).
         # Here we're overriding the smallest possible method to provide
         # a clean sequence without making the same assumption.

         __UpperCamelCase    							=[]
         for i in range(len(A_    )    ):
              try:
                   __UpperCamelCase    							=tokenizer.decode([i]					,   clean_up_tokenization_spaces=A_    )
              except UnicodeDecodeError:
                   pass
              toks.append((i, tok)    )

         __UpperCamelCase    							=list(filter(lambda A_    : re.match(r'^[ a-zA-Z]+$'					,   t[1]    )					,   A_    )    )
         __UpperCamelCase    							=list(filter(lambda A_    : [t[0]] == tokenizer.encode(t[1]					,   add_special_tokens=A_    )					,   A_    )    )
         if max_length is not None and len(A_    ) > max_length:
              __UpperCamelCase    							=toks[:max_length]
         if min_length is not None and len(A_    ) < min_length and len(A_    ) > 0:
              while len(A_    ) < min_length:
                   __UpperCamelCase    							=toks + toks
        # toks_str = [t[1] for t in toks]
         __UpperCamelCase    							=[t[0] for t in toks]

         # Ensure consistency
         __UpperCamelCase    							=tokenizer.decode(A_					,   clean_up_tokenization_spaces=A_    )
         if " " not in output_txt and len(A_    ) > 1:
              __UpperCamelCase    							=(
                  tokenizer.decode([toks_ids[0]]					,   clean_up_tokenization_spaces=A_    )
                  + ' '
                  + tokenizer.decode(toks_ids[1:]					,   clean_up_tokenization_spaces=A_    )
              )
         if with_prefix_space:
              __UpperCamelCase    							=' ' + output_txt
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
         return output_txt, output_ids






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.ta_base_tokenizer
         __UpperCamelCase    							=tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']    )
         __UpperCamelCase    							=tokenizer(['hi', 'I went to the gym', '']    )
         self.assertListEqual(batch_with_eos_added['input_ids']					,   batch_without_eos_added['input_ids']    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.ta_base_tokenizer
         __UpperCamelCase    							='Unicode €.'
         __UpperCamelCase    							=tokenizer(A_    )
         __UpperCamelCase    							=[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
         self.assertEqual(encoded['input_ids']					,   A_    )

         # decoding
         __UpperCamelCase    							=tokenizer.decode(A_    )
         self.assertEqual(A_					,   'Unicode €.</s>'    )

         __UpperCamelCase    							=tokenizer('e è é ê ë'    )
         __UpperCamelCase    							=[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
         self.assertEqual(encoded['input_ids']					,   A_    )
         # decoding
         __UpperCamelCase    							=tokenizer.decode(A_    )
         self.assertEqual(A_					,   'e è é ê ë</s>'    )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'    )    )					,   'e è é ê ë</s>'    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.ta_base_tokenizer
         __UpperCamelCase    							=['A long paragraph for summarization.', 'Another paragraph for summarization.']
         # fmt: off
         __UpperCamelCase    							=[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
         # fmt: on
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   return_tensors=A_    )
         self.assertIsInstance(A_					,   A_    )

         if FRAMEWORK != "jax":
              __UpperCamelCase    							=list(batch.input_ids.numpy()[0]    )
         else:
              __UpperCamelCase    							=list(batch.input_ids.tolist()[0]    )

         self.assertListEqual(A_					,   A_    )

         self.assertEqual((2, 37)					,   batch.input_ids.shape    )
         self.assertEqual((2, 37)					,   batch.attention_mask.shape    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.ta_base_tokenizer
         __UpperCamelCase    							=['A long paragraph for summarization.', 'Another paragraph for summarization.']
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   return_tensors=A_    )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('input_ids'					,   A_    )
         self.assertIn('attention_mask'					,   A_    )
         self.assertNotIn('decoder_input_ids'					,   A_    )
         self.assertNotIn('decoder_attention_mask'					,   A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.ta_base_tokenizer
         __UpperCamelCase    							=[
             'Summary of the text.',
             'Another summary.',
         ]
         __UpperCamelCase    							=tokenizer(
             text_target=A_					,   max_length=32					,   padding='max_length'					,   truncation=A_					,   return_tensors=A_    )
         self.assertEqual(32					,   targets['input_ids'].shape[1]    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=self.ta_base_tokenizer
         __UpperCamelCase    							=['A long paragraph for summarization. </s>']
         __UpperCamelCase    							=['Summary of the text. </s>']
         # fmt: off
         __UpperCamelCase    							=[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
         __UpperCamelCase    							=[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
         # fmt: on

         __UpperCamelCase    							=tokenizer(A_					,   text_target=A_    )

         self.assertEqual(A_					,   batch['input_ids'][0]    )
         self.assertEqual(A_					,   batch['labels'][0]    )






    def _a					(			self    )  ->       Tuple:
         # safety check on max_len default value so we are sure the test works
         __UpperCamelCase    							=self.get_tokenizers()
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   self.assertNotEqual(tokenizer.model_max_length					,   42    )

        # Now let's start the test
         __UpperCamelCase    							=self.get_tokenizers()
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   __UpperCamelCase    							=tempfile.mkdtemp()

                   __UpperCamelCase    							=' He is very happy, UNwant\u00E9d,running'
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   tokenizer.save_pretrained(A_    )

                   __UpperCamelCase    							=tokenizer.__class__.from_pretrained(A_    )
                   __UpperCamelCase    							=after_tokenizer.encode(A_					,   add_special_tokens=A_    )
                   self.assertListEqual(A_					,   A_    )

                   shutil.rmtree(A_    )

         __UpperCamelCase    							=self.get_tokenizers(model_max_length=42    )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   __UpperCamelCase    							=tempfile.mkdtemp()

                   __UpperCamelCase    							=' He is very happy, UNwant\u00E9d,running'
                   tokenizer.add_tokens(['bim', 'bambam']    )
                   __UpperCamelCase    							=tokenizer.additional_special_tokens
                   additional_special_tokens.append('new_additional_special_token'    )
                   tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}    )
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   tokenizer.save_pretrained(A_    )

                   __UpperCamelCase    							=tokenizer.__class__.from_pretrained(A_    )
                   __UpperCamelCase    							=after_tokenizer.encode(A_					,   add_special_tokens=A_    )
                   self.assertListEqual(A_					,   A_    )
                   self.assertIn('new_additional_special_token'					,   after_tokenizer.additional_special_tokens    )
                   self.assertEqual(after_tokenizer.model_max_length					,   42    )

                   __UpperCamelCase    							=tokenizer.__class__.from_pretrained(A_					,   model_max_length=43    )
                   self.assertEqual(tokenizer.model_max_length					,   43    )

                   shutil.rmtree(A_    )






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=[]
         if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

         if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(A_    )

                   with open(os.path.join(A_					,   'special_tokens_map.json'    )					,   encoding='utf-8'    ) as json_file:
                        __UpperCamelCase    							=json.load(A_    )

                   with open(os.path.join(A_					,   'tokenizer_config.json'    )					,   encoding='utf-8'    ) as json_file:
                        __UpperCamelCase    							=json.load(A_    )

                   __UpperCamelCase    							=[f'<extra_id_{i}>' for i in range(125    )]

                   __UpperCamelCase    							=added_tokens_extra_ids + [
                       'an_additional_special_token'
                   ]
                   __UpperCamelCase    							=added_tokens_extra_ids + [
                       'an_additional_special_token'
                   ]

                   with open(os.path.join(A_					,   'special_tokens_map.json'    )					,   'w'					,   encoding='utf-8'    ) as outfile:
                        json.dump(A_					,   A_    )
                   with open(os.path.join(A_					,   'tokenizer_config.json'    )					,   'w'					,   encoding='utf-8'    ) as outfile:
                        json.dump(A_					,   A_    )

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   __UpperCamelCase    							=tokenizer_class.from_pretrained(
                       A_					,   )
                   self.assertIn(
                       'an_additional_special_token'					,   tokenizer_without_change_in_init.additional_special_tokens    )
                   # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       ['an_additional_special_token']					,   tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']    )    )					,   )

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   __UpperCamelCase    							=added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'					,   lstrip=A_    )]
                   __UpperCamelCase    							=tokenizer_class.from_pretrained(
                       A_					,   additional_special_tokens=A_					,   )

                   self.assertIn('a_new_additional_special_token'					,   tokenizer.additional_special_tokens    )
                   self.assertEqual(
                       ['a_new_additional_special_token']					,   tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']    )    )					,   )






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=[]
         if self.test_slow_tokenizer:
              tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

         if self.test_rust_tokenizer:
              tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
              with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(A_    )

                   __UpperCamelCase    							=tokenizer_class.from_pretrained(A_    )

                   self.assertTrue(tokenizer.decode([255]    ) == ''    )






    def _a					(			self    )  ->       int:
         pass






    def _a					(			self    )  ->       Tuple:
         pass






    def _a					(			self    )  ->       Tuple:
         pass






    def _a					(			self    )  ->       List[Any]:
         pass






    def _a					(			self    )  ->       List[str]:
         # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
         # and special added tokens as tokens
         __UpperCamelCase    							=self.get_tokenizers(fast=A_					,   do_lower_case=A_    )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   __UpperCamelCase    							=['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
                   __UpperCamelCase    							=tokenizer.convert_tokens_to_string(A_    )

                   self.assertIsInstance(A_					,   A_    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.get_tokenizers()
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   __UpperCamelCase    							=[
                       'bos_token',
                       'eos_token',
                       'unk_token',
                       'sep_token',
                       'pad_token',
                       'cls_token',
                       'mask_token',
                   ]

                   __UpperCamelCase    							=0
                   __UpperCamelCase    							=tokenizer.convert_ids_to_tokens(
                       A_					,   skip_special_tokens=A_    )

                   for attr in attributes_list:
                        setattr(A_					,   attr + '_id'					,   A_    )
                        self.assertEqual(getattr(A_					,   A_    )					,   A_    )
                        self.assertEqual(getattr(A_					,   attr + '_id'    )					,   A_    )

                        setattr(A_					,   attr + '_id'					,   A_    )
                        self.assertEqual(getattr(A_					,   A_    )					,   A_    )
                        self.assertEqual(getattr(A_					,   attr + '_id'    )					,   A_    )

                   setattr(A_					,   'additional_special_tokens_ids'					,   []    )
                   self.assertListEqual(getattr(A_					,   'additional_special_tokens'    )					,   []    )
                   self.assertListEqual(getattr(A_					,   'additional_special_tokens_ids'    )					,   []    )

                   setattr(A_					,   'additional_special_tokens_ids'					,   [token_id_to_test_setters]    )
                   self.assertListEqual(getattr(A_					,   'additional_special_tokens'    )					,   [token_to_test_setters]    )
                   self.assertListEqual(getattr(A_					,   'additional_special_tokens_ids'    )					,   [token_id_to_test_setters]    )









from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase		       =							list[list[float | int]]




def    snake_case_     (  snake_case			,							snake_case     )  ->				Matrix:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(size + 1     )] for _ in range(snake_case     )]
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int
       lowercase__:   float

       for row in range(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[Any]  			=   matrix[row][col]

              lowercase__:   Optional[int]  			=   vector[row][0]

       lowercase__:   str  			=   0
       lowercase__:   Any  			=   0
       while row < size and col < size:
              # pivoting
              lowercase__:   List[Any]  			=   max((abs(augmented[rowa][col]     ), rowa) for rowa in range(snake_case			,							snake_case     )     )[
                  1
              ]
              if augmented[pivot_row][col] == 0:
                     col += 1
                     continue
              else:
                     lowercase__			,						lowercase__:   Optional[int]  			=   augmented[pivot_row], augmented[row]

              for rowa in range(row + 1			,							snake_case     ):
                     lowercase__:   Any  			=   augmented[rowa][col] / augmented[row][col]
                     lowercase__:   int  			=   0
                     for cola in range(col + 1			,							size + 1     ):
                            augmented[rowa][cola] -= augmented[row][cola] * ratio

              row += 1
              col += 1

       # back substitution
       for col in range(1			,							snake_case     ):
              for row in range(snake_case     ):
                     lowercase__:   Union[str, Any]  			=   augmented[row][col] / augmented[col][col]
                     for cola in range(snake_case			,							size + 1     ):
                            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
       return [
           [round(augmented[row][size] / augmented[row][row]			,							10     )] for row in range(snake_case     )
       ]




def    snake_case_     (  snake_case     )  ->				Callable[[int], int]:
       lowercase__:   int  			=   len(snake_case     )
       lowercase__:   Matrix  			=   [[0 for _ in range(snake_case     )] for _ in range(snake_case     )]
       lowercase__:   Matrix  			=   [[0] for _ in range(snake_case     )]
       lowercase__:   Matrix
       lowercase__:   int
       lowercase__:   int
       lowercase__:   int

       for x_val, y_val in enumerate(snake_case     ):
              for col in range(snake_case     ):
                     lowercase__:   List[str]  			=   (x_val + 1) ** (size - col - 1)
              lowercase__:   str  			=   y_val

       lowercase__:   Optional[int]  			=   solve(snake_case			,							snake_case     )

       def interpolated_func(snake_case     ) -> int:
              return sum(
                  round(coeffs[x_val][0]     ) * (var ** (size - x_val - 1))
                  for x_val in range(snake_case     )     )

       return interpolated_func




def    snake_case_     (  snake_case     )  ->				int:
       return (
           1
           - variable
           + variable**2
           - variable**3
           + variable**4
           - variable**5
           + variable**6
           - variable**7
           + variable**8
           - variable**9
           + variable**10
       )




def    snake_case_     (  snake_case = question_function			,							snake_case = 10     )  ->				int:
       lowercase__:   list[int]  			=   [func(snake_case     ) for x_val in range(1			,							order + 1     )]

       lowercase__:   list[Callable[[int], int]]  			=   [
           interpolate(data_points[:max_coeff]     ) for max_coeff in range(1			,							order + 1     )
       ]

       lowercase__:   int  			=   0
       lowercase__:   Callable[[int], int]
       lowercase__:   int

       for poly in polynomials:
              lowercase__:   List[str]  			=   1
              while func(snake_case     ) == poly(snake_case     ):
                     x_val += 1

              ret += poly(snake_case     )

       return ret


if __name__ == "__main__":
 print(F'''{solution() = }''')


