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 						__snake_case						(			__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






      _snake_case     		=							BartTokenizer
      _snake_case     		=							BartTokenizerFast
      _snake_case     		=							True
      _snake_case     		=							filter_roberta_detectors



      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[str])					->Optional[Any]:


          """simple docstring"""


          super().setUp()
          _lowerCamelCase  :		Tuple			    =    [
              """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(_UpperCamelCase     ,	range(len(_UpperCamelCase))))
          _lowerCamelCase  :		Optional[int]			    =    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
          _lowerCamelCase  :		str			    =    {"""unk_token""": """<unk>"""}

          _lowerCamelCase  :		Optional[Any]			    =    os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES["""vocab_file"""])
          _lowerCamelCase  :		Tuple			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	**_UpperCamelCase  :					List[Any])					->List[str]:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	**_UpperCamelCase  :					List[Any])					->Dict:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any     ,	_UpperCamelCase  :					List[str])					->Optional[Any]:


          """simple docstring"""


          return "lower newer", "lower newer"



      @cached_property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


          return BartTokenizer.from_pretrained("""facebook/bart-large""")



      @cached_property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int])					->Dict:


          """simple docstring"""


          return BartTokenizerFast.from_pretrained("""facebook/bart-large""")



      @require_torch
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->str:


          """simple docstring"""


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

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              _lowerCamelCase  :		List[Any]			    =    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)
              _lowerCamelCase  :		List[str]			    =    batch.input_ids.tolist()[0]
              self.assertListEqual(_UpperCamelCase     ,	_UpperCamelCase)
              # Test that special tokens are reset



      @require_torch
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              _lowerCamelCase  :		List[Any]			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Any:


          """simple docstring"""


          _lowerCamelCase  :		List[str]			    =    [
              """Summary of the text.""",
              """Another summary.""",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              _lowerCamelCase  :		List[str]			    =    tokenizer(text_target=_UpperCamelCase     ,	max_length=32     ,	padding="""max_length"""     ,	return_tensors="""pt""")
              self.assertEqual(32     ,	targets["""input_ids"""].shape[1])



      @require_torch
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->Dict:


          """simple docstring"""


          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              _lowerCamelCase  :		List[Any]			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Dict:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    ["""A long paragraph for summarization."""]
          _lowerCamelCase  :		Tuple			    =    [
              """Summary of the text.""",
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              _lowerCamelCase  :		Any			    =    tokenizer(_UpperCamelCase     ,	return_tensors="""pt""")
              _lowerCamelCase  :		Any			    =    tokenizer(text_target=_UpperCamelCase     ,	return_tensors="""pt""")
              _lowerCamelCase  :		Tuple			    =    inputs["""input_ids"""]
              _lowerCamelCase  :		int			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->List[Any]:


          """simple docstring"""


          pass




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->str:


          """simple docstring"""


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                  _lowerCamelCase  :		Tuple			    =    self.rust_tokenizer_class.from_pretrained(_UpperCamelCase     ,	**_UpperCamelCase)
                  _lowerCamelCase  :		Union[str, Any]			    =    self.tokenizer_class.from_pretrained(_UpperCamelCase     ,	**_UpperCamelCase)
                  _lowerCamelCase  :		Any			    =    """A, <mask> AllenNLP sentence."""
                  _lowerCamelCase  :		Any			    =    tokenizer_r.encode_plus(_UpperCamelCase     ,	add_special_tokens=_UpperCamelCase     ,	return_token_type_ids=_UpperCamelCase)
                  _lowerCamelCase  :		Any			    =    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"""])     ,	)

                  _lowerCamelCase  :		List[Any]			    =    tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
                  _lowerCamelCase  :		Tuple			    =    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(
                      _UpperCamelCase     ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
                  self.assertSequenceEqual(
                      _UpperCamelCase     ,	["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])





from __future__ import annotations







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    [None] * (len(__A						) + len(__A						))

    _lowerCamelCase  :		Optional[int]			    =    0  # pointer to current index for left Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the right Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the sorted array Half

    while pointera < len(__A						) and pointera < len(__A						):
        if left_half[pointera] < right_half[pointera]:
            _lowerCamelCase  :		Optional[Any]			    =    left_half[pointera]
            pointera += 1
            index += 1
        else:
            _lowerCamelCase  :		List[Any]			    =    right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(__A						):
        _lowerCamelCase  :		int			    =    left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(__A						):
        _lowerCamelCase  :		Optional[Any]			    =    right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array







def     A__	(       __A						):







    '''simple docstring'''
    if len(__A						) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    _lowerCamelCase  :		int			    =    0 + (len(__A						) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    _lowerCamelCase  :		List[str]			    =    array[:middle]
    _lowerCamelCase  :		Optional[int]			    =    array[middle:]

    return merge(merge_sort(__A						) ,			merge_sort(__A						)						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




