



'''simple docstring'''





import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase      						=	get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')






@require_sentencepiece
@require_tokenizers
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Union[str, Any]				=      PegasusTokenizer
  __lowercase							:      int				=      PegasusTokenizerFast
  __lowercase							:      str				=      True
  __lowercase							:      List[Any]				=      True




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      super().setUp()

      # We have a SentencePiece fixture for testing
      lowerCAmelCase__ :  Optional[Any]							      =					PegasusTokenizer(__UpperCAmelCase      )
      tokenizer.save_pretrained(self.tmpdirname      )




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      return PegasusTokenizer.from_pretrained("""google/pegasus-large"""      )




  def 							UpperCAmelCase_	(    self  ,**__UpperCAmelCase      )    ->		PegasusTokenizer:
      return PegasusTokenizer.from_pretrained(self.tmpdirname  ,**__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      return ("This is a test", "This is a test")




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  str							      =					"""</s>"""
      lowerCAmelCase__ :  Union[str, Any]							      =					1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase      )  ,__UpperCAmelCase      )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase      )  ,__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Any							      =					list(self.get_tokenizer().get_vocab().keys()      )

      self.assertEqual(vocab_keys[0]  ,"""<pad>"""      )
      self.assertEqual(vocab_keys[1]  ,"""</s>"""      )
      self.assertEqual(vocab_keys[-1]  ,"""v"""      )
      self.assertEqual(len(__UpperCAmelCase      )  ,1103      )




  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      self.assertEqual(self.get_tokenizer().vocab_size  ,1103      )




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      lowerCAmelCase__ :  List[str]							      =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
      lowerCAmelCase__ :  Union[str, Any]							      =					self.tokenizer_class.from_pretrained(self.tmpdirname      )
      lowerCAmelCase__ :  List[Any]							      =					(
          """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
          """ </s> <pad> <pad> <pad>"""
      )
      lowerCAmelCase__ :  List[str]							      =					rust_tokenizer([raw_input_str]  ,return_tensors=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      ).input_ids[0]
      lowerCAmelCase__ :  str							      =					py_tokenizer([raw_input_str]  ,return_tensors=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      ).input_ids[0]
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      lowerCAmelCase__ :  List[str]							      =					self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      lowerCAmelCase__ :  Union[str, Any]							      =					"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
      lowerCAmelCase__ :  Tuple							      =					[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
      lowerCAmelCase__ :  Union[str, Any]							      =					tokenizer([raw_input_str]  ,return_tensors=__UpperCAmelCase      ).input_ids[0]
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Any							      =					self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6103
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 103
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1024
      lowerCAmelCase__ :  Any							      =					"""To ensure a smooth flow of bank resolutions."""
      lowerCAmelCase__ :  str							      =					[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
      lowerCAmelCase__ :  Optional[Any]							      =					tokenizer([raw_input_str]  ,return_tensors=__UpperCAmelCase      ).input_ids[0]
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




  @require_torch
  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  str							      =					["""This is going to be way too long.""" * 150, """short example"""]
      lowerCAmelCase__ :  List[Any]							      =					["""not super long but more than 5 tokens""", """tiny"""]
      lowerCAmelCase__ :  str							      =					self._large_tokenizer(__UpperCAmelCase  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors="""pt"""      )
      lowerCAmelCase__ :  Dict							      =					self._large_tokenizer(
          text_target=__UpperCAmelCase  ,max_length=5  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors="""pt"""      )

      assert batch.input_ids.shape == (2, 1024)
      assert batch.attention_mask.shape == (2, 1024)
      assert targets["input_ids"].shape == (2, 5)
      assert len(__UpperCAmelCase      ) == 2  # input_ids, attention_mask.






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		str:
      # fmt: off
      lowerCAmelCase__ :  Any							      =					{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__UpperCAmelCase  ,model_name="""google/bigbird-pegasus-large-arxiv"""  ,revision="""ba85d0851d708441f91440d509690f1ab6353415"""  ,)







@require_sentencepiece
@require_tokenizers
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Union[str, Any]				=      PegasusTokenizer
  __lowercase							:      List[Any]				=      PegasusTokenizerFast
  __lowercase							:      Union[str, Any]				=      True
  __lowercase							:      Optional[int]				=      True




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      super().setUp()

      # We have a SentencePiece fixture for testing
      lowerCAmelCase__ :  Union[str, Any]							      =					PegasusTokenizer(__UpperCAmelCase  ,offset=0  ,mask_token_sent=__UpperCAmelCase  ,mask_token="""[MASK]"""      )
      tokenizer.save_pretrained(self.tmpdirname      )




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""      )




  def 							UpperCAmelCase_	(    self  ,**__UpperCAmelCase      )    ->		PegasusTokenizer:
      return PegasusTokenizer.from_pretrained(self.tmpdirname  ,**__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      return ("This is a test", "This is a test")




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      lowerCAmelCase__ :  Dict							      =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
      lowerCAmelCase__ :  Optional[int]							      =					self.tokenizer_class.from_pretrained(self.tmpdirname      )
      lowerCAmelCase__ :  int							      =					(
          """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
          """ <pad> <pad> <pad>"""
      )
      lowerCAmelCase__ :  str							      =					rust_tokenizer([raw_input_str]  ,return_tensors=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      ).input_ids[0]
      lowerCAmelCase__ :  Any							      =					py_tokenizer([raw_input_str]  ,return_tensors=__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase      ).input_ids[0]
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase      )




  @require_torch
  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  Tuple							      =					["""This is going to be way too long.""" * 1000, """short example"""]
      lowerCAmelCase__ :  str							      =					["""not super long but more than 5 tokens""", """tiny"""]
      lowerCAmelCase__ :  Optional[int]							      =					self._large_tokenizer(__UpperCAmelCase  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors="""pt"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					self._large_tokenizer(
          text_target=__UpperCAmelCase  ,max_length=5  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors="""pt"""      )

      assert batch.input_ids.shape == (2, 4096)
      assert batch.attention_mask.shape == (2, 4096)
      assert targets["input_ids"].shape == (2, 5)
      assert len(__UpperCAmelCase      ) == 2  # input_ids, attention_mask.






  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  Any							      =					(
          """This is an example string that is used to test the original TF implementation against the HF"""
          """ implementation"""
      )

      lowerCAmelCase__ :  Dict							      =					self._large_tokenizer(__UpperCAmelCase      ).input_ids

      self.assertListEqual(
          __UpperCAmelCase  ,[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]  ,)







'''simple docstring'''





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					analyze_text(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					list(""" """ + ascii_lowercase			)
    # what is our total sum of probabilities.
    lowerCAmelCase__ :  List[Any]							      =					sum(single_char_strings.values()			)

    # one length string
    lowerCAmelCase__ :  Optional[int]							      =					0
    # for each alpha we go in our dict and if it is in it we calculate entropy
    for ch in my_alphas:
        if ch in single_char_strings:
            lowerCAmelCase__ :  List[Any]							      =					single_char_strings[ch]
            lowerCAmelCase__ :  List[Any]							      =					my_str / all_sum
            my_fir_sum += prob * math.loga(UpperCamelCase			)  # entropy formula.

    # print entropy
    print(f"""{round(-1 * my_fir_sum			):.1f}"""			)

    # two len string
    lowerCAmelCase__ :  Dict							      =					sum(two_char_strings.values()			)
    lowerCAmelCase__ :  int							      =					0
    # for each alpha (two in size) calculate entropy.
    for cha in my_alphas:
        for cha in my_alphas:
            lowerCAmelCase__ :  Union[str, Any]							      =					cha + cha
            if sequence in two_char_strings:
                lowerCAmelCase__ :  Dict							      =					two_char_strings[sequence]
                lowerCAmelCase__ :  Tuple							      =					int(UpperCamelCase			) / all_sum
                my_sec_sum += prob * math.loga(UpperCamelCase			)

    # print second entropy
    print(f"""{round(-1 * my_sec_sum			):.1f}"""			)

    # print the difference between them
    print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)			):.1f}"""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					Counter()  # type: ignore
    lowerCAmelCase__ :  Tuple							      =					Counter()  # type: ignore
    single_char_strings[text[-1]] += 1

    # first case when we have space at start.
    two_char_strings[" " + text[0]] += 1
    for i in range(0							,    len(UpperCamelCase			) - 1			):
        single_char_strings[text[i]] += 1
        two_char_strings[text[i : i + 2]] += 1
    return single_char_strings, two_char_strings
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    import doctest

    doctest.testmod()
    # text = (
    #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
    #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
    #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
    #     "beloved evident through no service elderly is. Blind there if every no so "
    #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
    #     "of message cottage windows do besides against uncivil.  Delightful "
    #     "unreserved impossible few estimating men favourable see entreaties. She "
    #     "propriety immediate was improving. He or entrance humoured likewise "
    #     "moderate. Much nor game son say feel. Fat make met can must form into "
    #     "gate. Me we offending prevailed discovery. "
    # )

    # calculate_prob(text)


if __name__ == "__main__":
 main()


