"""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 					lowerCAmelCase_      (     lowerCAmelCase       ,      unittest.TestCase   ):



    """simple docstring"""
    _lowerCAmelCase       :      Optional[int]     					=  MvpTokenizer
    _lowerCAmelCase       :      int     					=  MvpTokenizerFast
    _lowerCAmelCase       :      Optional[int]     					=  True
    _lowerCAmelCase       :      str     					=  filter_roberta_detectors

    def 						snake_case	(			self      ):






          """simple docstring"""

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

          snake_case        =     os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']      )
          snake_case        =     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 						snake_case	(			self						,      **lowerCAmelCase      ):






          """simple docstring"""

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

    def 						snake_case	(			self						,      **lowerCAmelCase      ):






          """simple docstring"""

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

    def 						snake_case	(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          return "lower newer", "lower newer"

    @cached_property
    def 						snake_case	(			self      ):






          """simple docstring"""

          return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )

    @cached_property
    def 						snake_case	(			self      ):






          """simple docstring"""

          return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )

    @require_torch
    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          snake_case        =     [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case        =     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      )
                snake_case        =     batch.input_ids.tolist()[0]
                self.assertListEqual(lowerCAmelCase						,      lowerCAmelCase      )
                # Test that special tokens are reset

    @require_torch
    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case        =     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 						snake_case	(			self      ):






          """simple docstring"""

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

    @require_torch
    def 						snake_case	(			self      ):






          """simple docstring"""

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case        =     tokenizer(
                    ['I am a small frog' * 10_24, 'I am a small frog']						,      padding=lowerCAmelCase						,      truncation=lowerCAmelCase						,      return_tensors='pt'      )
                self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )
                self.assertEqual(batch.input_ids.shape						,      (2, 10_24)      )

    @require_torch
    def 						snake_case	(			self      ):






          """simple docstring"""

          snake_case        =     ['A long paragraph for summarization.']
          snake_case        =     [
              'Summary of the text.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                snake_case        =     tokenizer(lowerCAmelCase						,      text_target=lowerCAmelCase						,      return_tensors='pt'      )
                snake_case        =     inputs['input_ids']
                snake_case        =     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 						snake_case	(			self      ):






          """simple docstring"""

          pass

    def 						snake_case	(			self      ):






          """simple docstring"""

          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
                      snake_case        =     self.rust_tokenizer_class.from_pretrained(lowerCAmelCase						,      **lowerCAmelCase      )
                      snake_case        =     self.tokenizer_class.from_pretrained(lowerCAmelCase						,      **lowerCAmelCase      )
                      snake_case        =     'A, <mask> AllenNLP sentence.'
                      snake_case        =     tokenizer_r.encode_plus(lowerCAmelCase						,      add_special_tokens=lowerCAmelCase						,      return_token_type_ids=lowerCAmelCase      )
                      snake_case        =     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']      )						,      )

                      snake_case        =     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
                      snake_case        =     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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
                      self.assertSequenceEqual(tokens_r['input_ids']						,      [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 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 cmath
import math





def 				lowerCAmelCase__      (     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float   )   ->			complex:





						"""simple docstring"""






						snake_case        =     math.radians(_UpperCamelCase   )
						snake_case        =     math.radians(_UpperCamelCase   )

						# Convert voltage and current to rectangular form
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )

						# Calculate apparent power
						return voltage_rect * current_rect


if __name__ == "__main__":
					import doctest

					doctest.testmod()


