




"""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    (					_a       ,	unittest.TestCase						):
   _lowerCamelCase    :Union[str, Any]     				=     MvpTokenizer
   _lowerCamelCase    :int     				=     MvpTokenizerFast
   _lowerCamelCase    :Dict     				=     True
   _lowerCamelCase    :int     				=     filter_roberta_detectors






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Dict:

         """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__      :			Union[str, Any]   =	dict(zip(snake_case_   ,     range(len(snake_case_   )   )   )   )
         lowerCAmelCase__      :			Any   =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
         lowerCAmelCase__      :			Tuple   =	{"""unk_token""": """<unk>"""}

         lowerCAmelCase__      :			Any   =	os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES["""vocab_file"""]   )
         lowerCAmelCase__      :			Optional[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 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     **UpperCamelCase       :		Union[str, Any]   )			->						List[str]:

         """simple docstring"""
         kwargs.update(self.special_tokens_map   )
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case_   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     **UpperCamelCase       :		List[str]   )			->						Dict:

         """simple docstring"""
         kwargs.update(self.special_tokens_map   )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,     **snake_case_   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		List[str]   )			->						Any:

         """simple docstring"""
         return "lower newer", "lower newer"






   @cached_property
   def 	_lowerCAmelCase   (		self       :		int   )			->						List[str]:

         """simple docstring"""
         return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""   )






   @cached_property
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Any:

         """simple docstring"""
         return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Any   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
         lowerCAmelCase__      :			Dict   =	[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]:
               lowerCAmelCase__      :			List[Any]   =	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__      :			Union[str, Any]   =	batch.input_ids.tolist()[0]
               self.assertListEqual(snake_case_   ,     snake_case_   )
               # Test that special tokens are reset






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	["""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 	_lowerCAmelCase   (		self       :		Tuple   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	[
             """Summary of the text.""",
             """Another summary.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowerCAmelCase__      :			str   =	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 	_lowerCAmelCase   (		self       :		List[str]   )			->						Tuple:

         """simple docstring"""
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowerCAmelCase__      :			Optional[Any]   =	tokenizer(
                   ["""I am a small frog""" * 10_24, """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, 10_24)   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		str   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	["""A long paragraph for summarization."""]
         lowerCAmelCase__      :			Optional[Any]   =	[
             """Summary of the text.""",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowerCAmelCase__      :			int   =	tokenizer(snake_case_   ,     text_target=snake_case_   ,     return_tensors="""pt"""   )
               lowerCAmelCase__      :			int   =	inputs["""input_ids"""]
               lowerCAmelCase__      :			str   =	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 	_lowerCAmelCase   (		self       :		int   )			->						Tuple:

         """simple docstring"""
         pass






   def 	_lowerCAmelCase   (		self       :		str   )			->						str:

         """simple docstring"""
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                     lowerCAmelCase__      :			int   =	self.rust_tokenizer_class.from_pretrained(snake_case_   ,     **snake_case_   )
                     lowerCAmelCase__      :			Optional[int]   =	self.tokenizer_class.from_pretrained(snake_case_   ,     **snake_case_   )
                     lowerCAmelCase__      :			Optional[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__      :			int   =	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__      :			Any   =	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]   )
                     lowerCAmelCase__      :			List[str]   =	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(
                         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_				(			__UpperCAmelCase				)						->    str:
      if not all(char in """01""" for char in bin_string				):
            raise ValueError("""Non-binary value was passed to the function"""				)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""				)
      lowerCAmelCase__      :			List[str]   =	""""""
      while len(__UpperCAmelCase				) % 3 != 0:
            lowerCAmelCase__      :			Optional[Any]   =	"""0""" + bin_string
      lowerCAmelCase__      :			List[Any]   =	[
          bin_string[index : index + 3]
          for index in range(len(__UpperCAmelCase				)				)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCAmelCase__      :			Optional[int]   =	0
            for index, val in enumerate(__UpperCAmelCase				):
                  oct_val += int(2 ** (2 - index) * int(__UpperCAmelCase				)				)
            oct_string += str(__UpperCAmelCase				)
      return oct_string


if __name__ == "__main__":
  from doctest import testmod

  testmod()





