


'''simple docstring'''







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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    MvpTokenizer
     __magic_name__    			=    MvpTokenizerFast
     __magic_name__    			=    True
     __magic_name__    			=    filter_roberta_detectors







     def a	( self   ):

            '''simple docstring'''

            super().setUp()
            _lowerCAmelCase    :							List[Any]   			=       [
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
            ]
            _lowerCAmelCase    :							Dict   			=       dict(zip(snake_case__      ,	range(len(snake_case__   )   )   )   )
            _lowerCAmelCase    :							str   			=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            _lowerCAmelCase    :							str   			=       {'unk_token': '<unk>'}

            _lowerCAmelCase    :							int   			=       os.path.join(self.tmpdirname      ,	VOCAB_FILES_NAMES['vocab_file']   )
            _lowerCAmelCase    :							Union[str, 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(snake_case__   ) + '\n'   )
            with open(self.merges_file      ,	'w'      ,	encoding='utf-8'   ) as fp:
                   fp.write('\n'.join(snake_case__   )   )







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            return "lower newer", "lower newer"







     @cached_property
     def a	( self   ):

            '''simple docstring'''

            return MvpTokenizer.from_pretrained('RUCAIBox/mvp'   )







     @cached_property
     def a	( self   ):

            '''simple docstring'''

            return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'   )







     @require_torch
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            _lowerCAmelCase    :							Any   			=       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _lowerCAmelCase    :							Tuple   			=       tokenizer(snake_case__      ,	max_length=len(snake_case__   )      ,	padding=snake_case__      ,	return_tensors='pt'   )
                   self.assertIsInstance(snake_case__      ,	snake_case__   )

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







     @require_torch
     def a	( self   ):

            '''simple docstring'''

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







     @require_torch
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       [
                'Summary of the text.',
                'Another summary.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _lowerCAmelCase    :							Union[str, Any]   			=       tokenizer(text_target=snake_case__      ,	max_length=32      ,	padding='max_length'      ,	return_tensors='pt'   )
                   self.assertEqual(32      ,	targets['input_ids'].shape[1]   )







     @require_torch
     def a	( self   ):

            '''simple docstring'''

            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _lowerCAmelCase    :							List[str]   			=       tokenizer(
                       ['I am a small frog' * 1024, 'I am a small frog']      ,	padding=snake_case__      ,	truncation=snake_case__      ,	return_tensors='pt'   )
                   self.assertIsInstance(snake_case__      ,	snake_case__   )
                   self.assertEqual(batch.input_ids.shape      ,	(2, 1024)   )







     @require_torch
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       ['A long paragraph for summarization.']
            _lowerCAmelCase    :							Dict   			=       [
                'Summary of the text.',
            ]
            for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   _lowerCAmelCase    :							Optional[Any]   			=       tokenizer(snake_case__      ,	text_target=snake_case__      ,	return_tensors='pt'   )
                   _lowerCAmelCase    :							Union[str, Any]   			=       inputs['input_ids']
                   _lowerCAmelCase    :							List[Any]   			=       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 a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

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

                          # 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']   )      ,	)

                          _lowerCAmelCase    :							Tuple   			=       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
                          _lowerCAmelCase    :							List[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(
                              snake_case__      ,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']   )
                          self.assertSequenceEqual(
                              snake_case__      ,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']   )





'''simple docstring'''







def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=       (boundary[1] - boundary[0]) / steps
       _lowerCAmelCase    :							Any   			=       boundary[0]
       _lowerCAmelCase    :							List[str]   			=       boundary[1]
       _lowerCAmelCase    :							Tuple   			=       make_points(_A   ,						_A   ,						_A					)
       _lowerCAmelCase    :							Tuple   			=       0.0
       y += (h / 2.0) * f(_A					)
       for i in x_i:
              # print(i)
              y += h * f(_A					)
       y += (h / 2.0) * f(_A					)
       return y






def 			lowercase				(_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       a + h
       while x < (b - h):
              yield x
              _lowerCAmelCase    :							Any   			=       x + h






def 			lowercase				(_A					):  # enter your function here







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       (x - 0) * (x - 0)
       return y






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       0.0  # Lower bound of integration
       _lowerCAmelCase    :							Dict   			=       1.0  # Upper bound of integration
       _lowerCAmelCase    :							Optional[Any]   			=       10.0  # define number of steps or resolution
       _lowerCAmelCase    :							Optional[int]   			=       [a, b]  # define boundary of integration
       _lowerCAmelCase    :							List[Any]   			=       method_a(_A   ,						_A					)
       print(f'y = {y}'					)


if __name__ == "__main__":
       main()

