

import math



class        _UpperCAmelCase :
  '''simple docstring'''







  def __init__( self	: Any		,						UpperCamelCase__	: Dict		,						UpperCamelCase__	: int		,						UpperCamelCase__	: Tuple ):
        A    =				x
        A    =				y
        A    =				z







  def __repr__( self	: Tuple ):
        return f'''Point({self.x}, {self.y}, {self.z})'''


def 			__UpperCamelCase    (lowerCAmelCase					:  Point,    lowerCAmelCase					:  Point     )     ->  float:
      return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()






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        _UpperCAmelCase (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				BartTokenizer
  SCREAMING_SNAKE_CASE							:				List[str]					      =				BartTokenizerFast
  SCREAMING_SNAKE_CASE							:				Any					      =				True
  SCREAMING_SNAKE_CASE							:				Union[str, Any]					      =				filter_roberta_detectors







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

        A    =				os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES['vocab_file'] )
        A    =				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 	UpperCamelCase       ( self	: Dict		,						**UpperCamelCase__	: List[Any] ):
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: Any		,						**UpperCamelCase__	: Union[str, Any] ):
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: int		,						UpperCamelCase__	: str ):
        return "lower newer", "lower newer"







  @cached_property
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        return BartTokenizer.from_pretrained('facebook/bart-large' )







  @cached_property
  def 	UpperCamelCase       ( self	: str ):
        return BartTokenizerFast.from_pretrained('facebook/bart-large' )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A    =				[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

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







  @require_torch
  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						padding=UpperCamelCase__		,						return_tensors='pt' )
              # check if input_ids are returned and no labels
              self.assertIn('input_ids'		,						UpperCamelCase__ )
              self.assertIn('attention_mask'		,						UpperCamelCase__ )
              self.assertNotIn('labels'		,						UpperCamelCase__ )
              self.assertNotIn('decoder_attention_mask'		,						UpperCamelCase__ )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				[
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(text_target=UpperCamelCase__		,						max_length=32		,						padding='max_length'		,						return_tensors='pt' )
              self.assertEqual(32		,						targets['input_ids'].shape[1] )







  @require_torch
  def 	UpperCamelCase       ( self	: int ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(
                  ['I am a small frog' * 1024, 'I am a small frog']		,						padding=UpperCamelCase__		,						truncation=UpperCamelCase__		,						return_tensors='pt' )
              self.assertIsInstance(UpperCamelCase__		,						UpperCamelCase__ )
              self.assertEqual(batch.input_ids.shape		,						(2, 1024) )







  @require_torch
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				['A long paragraph for summarization.']
        A    =				[
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						return_tensors='pt' )
              A    =				tokenizer(text_target=UpperCamelCase__		,						return_tensors='pt' )
              A    =				inputs['input_ids']
              A    =				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	: List[str] ):
        pass







  def 	UpperCamelCase       ( self	: Tuple ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                    A    =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				self.tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				'A, <mask> AllenNLP sentence.'
                    A    =				tokenizer_r.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )
                    A    =				tokenizer_p.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )

                    # 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'] )		,						)

                    A    =				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
                    A    =				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, 50264, 3823, 487, 21992, 3645, 4, 2] )
                    self.assertSequenceEqual(tokens_r['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

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



