





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	:					str			  =					get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")



@require_sentencepiece
@require_tokenizers
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Union[str, Any]						   =       PegasusTokenizer
      _UpperCAmelCase     :Optional[Any]						   =       PegasusTokenizerFast
      _UpperCAmelCase     :Optional[Any]						   =       True
      _UpperCAmelCase     :Optional[int]						   =       True





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().setUp()

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





      @cached_property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return PegasusTokenizer.from_pretrained("google/pegasus-large"       )





      def 	__UpperCamelCase(			self	,							**A_       ):
       '''simple docstring'''




       return PegasusTokenizer.from_pretrained(self.tmpdirname	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       return ("This is a test", "This is a test")





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       "</s>"
       UpperCamelCase     : List[str]	     =       1

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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[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(A_       )	,							1103       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.assertEqual(self.get_tokenizer().vocab_size	,							1103       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
       UpperCamelCase     : Union[str, Any]	     =       self.tokenizer_class.from_pretrained(self.tmpdirname       )
       UpperCamelCase     : Union[str, 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>"
       )
       UpperCamelCase     : Tuple	     =       rust_tokenizer([raw_input_str]	,							return_tensors=A_	,							add_special_tokens=A_       ).input_ids[0]
       UpperCamelCase     : str	     =       py_tokenizer([raw_input_str]	,							return_tensors=A_	,							add_special_tokens=A_       ).input_ids[0]
       self.assertListEqual(A_	,							A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[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
       UpperCamelCase     : int	     =       "To ensure a smooth flow of bank resolutions."
       UpperCamelCase     : int	     =       [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
       UpperCamelCase     : Tuple	     =       tokenizer([raw_input_str]	,							return_tensors=A_       ).input_ids[0]
       self.assertListEqual(A_	,							A_       )
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





      @require_torch
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       ["This is going to be way too long." * 150, "short example"]
       UpperCamelCase     : Optional[int]	     =       ["not super long but more than 5 tokens", "tiny"]
       UpperCamelCase     : Tuple	     =       self._large_tokenizer(A_	,							padding=A_	,							truncation=A_	,							return_tensors="pt"       )
       UpperCamelCase     : str	     =       self._large_tokenizer(
           text_target=A_	,							max_length=5	,							padding=A_	,							truncation=A_	,							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(A_       ) == 2  # input_ids, attention_mask.





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       {"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=A_	,							model_name="google/bigbird-pegasus-large-arxiv"	,							revision="ba85d0851d708441f91440d509690f1ab6353415"	,							)



@require_sentencepiece
@require_tokenizers
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :List[Any]						   =       PegasusTokenizer
      _UpperCAmelCase     :Dict						   =       PegasusTokenizerFast
      _UpperCAmelCase     :Tuple						   =       True
      _UpperCAmelCase     :Tuple						   =       True





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       super().setUp()

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





      @cached_property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"       )





      def 	__UpperCamelCase(			self	,							**A_       ):
       '''simple docstring'''




       return PegasusTokenizer.from_pretrained(self.tmpdirname	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       return ("This is a test", "This is a test")





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      @require_torch
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       ["This is going to be way too long." * 1000, "short example"]
       UpperCamelCase     : str	     =       ["not super long but more than 5 tokens", "tiny"]
       UpperCamelCase     : Union[str, Any]	     =       self._large_tokenizer(A_	,							padding=A_	,							truncation=A_	,							return_tensors="pt"       )
       UpperCamelCase     : Optional[Any]	     =       self._large_tokenizer(
           text_target=A_	,							max_length=5	,							padding=A_	,							truncation=A_	,							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(A_       ) == 2  # input_ids, attention_mask.





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       (
           "This is an example string that is used to test the original TF implementation against the HF"
           " implementation"
       )

       UpperCamelCase     : Optional[Any]	     =       self._large_tokenizer(A_       ).input_ids

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







def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : Union[str, Any]	     =       set()

 # Replace all the whitespace in our sentence
 UpperCamelCase     : Union[str, Any]	     =       input_str.replace(" "							,       ""					)
 for alpha in input_str:
  if "a" <= alpha.lower() <= "z":
   frequency.add(alpha.lower()					)
 return len(_lowerCAmelCase					) == 26



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 UpperCamelCase     : List[Any]	     =       [False] * 26
 for char in input_str:
  if char.islower():
   UpperCamelCase     : Tuple	     =       True
  elif char.isupper():
   UpperCamelCase     : str	     =       True
 return all(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase = "The quick brown fox jumps over the lazy dog"							,       )						->			bool:
 return len({char for char in input_str.lower() if char.isalpha()}					) == 26



def 		A_    (					)						->			None:
 from timeit import timeit

 UpperCamelCase     : Tuple	     =       "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
 print(timeit("is_pangram()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_faster()"							,       setup=_lowerCAmelCase					)					)
 print(timeit("is_pangram_fastest()"							,       setup=_lowerCAmelCase					)					)
 # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
 # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
