

'''simple docstring'''




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase            =		get_tests_dir("fixtures/test_sentencepiece.model")






@require_sentencepiece
@require_tokenizers
class     _SCREAMING_SNAKE_CASE       (       __a						,unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE	:Any     			=	XLMRobertaTokenizer
 __SCREAMING_SNAKE_CASE	:Optional[Any]     			=	XLMRobertaTokenizerFast
 __SCREAMING_SNAKE_CASE	:Dict     			=	True
 __SCREAMING_SNAKE_CASE	:Dict     			=	True



 def 					snake_case__    (       self						:       Optional[Any]     ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      __magic_name__		  =							XLMRobertaTokenizer(a__					, keep_accents=a__     )
      tokenizer.save_pretrained(self.tmpdirname     )



 def 					snake_case__    (       self						:       Optional[Any]     ):
      __magic_name__		  =							'<pad>'
      __magic_name__		  =							1

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



 def 					snake_case__    (       self						:       Optional[int]     ):
      __magic_name__		  =							list(self.get_tokenizer().get_vocab().keys()     )

      self.assertEqual(vocab_keys[0]					, '''<s>'''     )
      self.assertEqual(vocab_keys[1]					, '''<pad>'''     )
      self.assertEqual(vocab_keys[-1]					, '''<mask>'''     )
      self.assertEqual(len(a__     )					, 1002     )



 def 					snake_case__    (       self						:       str     ):
      self.assertEqual(self.get_tokenizer().vocab_size					, 1002     )



 def 					snake_case__    (       self						:       Tuple     ):
      __magic_name__		  =							XLMRobertaTokenizer(a__					, keep_accents=a__     )

      __magic_name__		  =							tokenizer.tokenize('''This is a test'''     )
      self.assertListEqual(a__					, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']     )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(a__     )					, [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					, )

      __magic_name__		  =							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''     )
      self.assertListEqual(
          a__					, [
              SPIECE_UNDERLINE + '''I''',
              SPIECE_UNDERLINE + '''was''',
              SPIECE_UNDERLINE + '''b''',
              '''or''',
              '''n''',
              SPIECE_UNDERLINE + '''in''',
              SPIECE_UNDERLINE + '''''',
              '''9''',
              '''2''',
              '''0''',
              '''0''',
              '''0''',
              ''',''',
              SPIECE_UNDERLINE + '''and''',
              SPIECE_UNDERLINE + '''this''',
              SPIECE_UNDERLINE + '''is''',
              SPIECE_UNDERLINE + '''f''',
              '''al''',
              '''s''',
              '''é''',
              '''.''',
          ]					, )
      __magic_name__		  =							tokenizer.convert_tokens_to_ids(a__     )
      self.assertListEqual(
          a__					, [
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]					, )

      __magic_name__		  =							tokenizer.convert_ids_to_tokens(a__     )
      self.assertListEqual(
          a__					, [
              SPIECE_UNDERLINE + '''I''',
              SPIECE_UNDERLINE + '''was''',
              SPIECE_UNDERLINE + '''b''',
              '''or''',
              '''n''',
              SPIECE_UNDERLINE + '''in''',
              SPIECE_UNDERLINE + '''''',
              '''<unk>''',
              '''2''',
              '''0''',
              '''0''',
              '''0''',
              ''',''',
              SPIECE_UNDERLINE + '''and''',
              SPIECE_UNDERLINE + '''this''',
              SPIECE_UNDERLINE + '''is''',
              SPIECE_UNDERLINE + '''f''',
              '''al''',
              '''s''',
              '''<unk>''',
              '''.''',
          ]					, )



 def 					snake_case__    (       self						:       Optional[int]     ):
      if not self.test_slow_tokenizer:
           # as we don't have a slow version, we can't compare the outputs between slow and fast versions
           return

      __magic_name__		  =							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                __magic_name__		  =							self.rust_tokenizer_class.from_pretrained(a__					, **a__     )
                __magic_name__		  =							self.tokenizer_class.from_pretrained(a__					, **a__     )

                __magic_name__		  =							tempfile.mkdtemp()

                __magic_name__		  =							tokenizer_r.save_pretrained(a__     )
                __magic_name__		  =							tokenizer_p.save_pretrained(a__     )

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )
                __magic_name__		  =							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f     )
                self.assertSequenceEqual(a__					, a__     )

                # Checks everything loads correctly in the same way
                __magic_name__		  =							tokenizer_r.from_pretrained(a__     )
                __magic_name__		  =							tokenizer_p.from_pretrained(a__     )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(a__					, a__     )     )
                     # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                     # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                shutil.rmtree(a__     )

                # Save tokenizer rust, legacy_format=True
                __magic_name__		  =							tempfile.mkdtemp()

                __magic_name__		  =							tokenizer_r.save_pretrained(a__					, legacy_format=a__     )
                __magic_name__		  =							tokenizer_p.save_pretrained(a__     )

                # Checks it save with the same files
                self.assertSequenceEqual(a__					, a__     )

                # Checks everything loads correctly in the same way
                __magic_name__		  =							tokenizer_r.from_pretrained(a__     )
                __magic_name__		  =							tokenizer_p.from_pretrained(a__     )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(a__					, a__     )     )

                shutil.rmtree(a__     )

                # Save tokenizer rust, legacy_format=False
                __magic_name__		  =							tempfile.mkdtemp()

                __magic_name__		  =							tokenizer_r.save_pretrained(a__					, legacy_format=a__     )
                __magic_name__		  =							tokenizer_p.save_pretrained(a__     )

                # Checks it saved the tokenizer.json file
                self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files     )     )

                # Checks everything loads correctly in the same way
                __magic_name__		  =							tokenizer_r.from_pretrained(a__     )
                __magic_name__		  =							tokenizer_p.from_pretrained(a__     )

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(a__					, a__     )     )

                shutil.rmtree(a__     )



 @cached_property
 def 					snake_case__    (       self						:       Tuple     ):
      return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''     )



 def 					snake_case__    (       self						:       str     ):
      with tempfile.NamedTemporaryFile() as f:
           shutil.copyfile(a__					, f.name     )
           __magic_name__		  =							XLMRobertaTokenizer(f.name					, keep_accents=a__     )
           __magic_name__		  =							pickle.dumps(a__     )
      pickle.loads(a__     )



 def 					snake_case__    (       self						:       Optional[int]     ):
      if not self.test_rust_tokenizer:
           return

      __magic_name__		  =							self.get_tokenizer()
      __magic_name__		  =							self.get_rust_tokenizer()

      __magic_name__		  =							'I was born in 92000, and this is falsé.'

      __magic_name__		  =							tokenizer.tokenize(a__     )
      __magic_name__		  =							rust_tokenizer.tokenize(a__     )
      self.assertListEqual(a__					, a__     )

      __magic_name__		  =							tokenizer.encode(a__					, add_special_tokens=a__     )
      __magic_name__		  =							rust_tokenizer.encode(a__					, add_special_tokens=a__     )
      self.assertListEqual(a__					, a__     )

      __magic_name__		  =							self.get_rust_tokenizer()
      __magic_name__		  =							tokenizer.encode(a__     )
      __magic_name__		  =							rust_tokenizer.encode(a__     )
      self.assertListEqual(a__					, a__     )



 @slow
 def 					snake_case__    (       self						:       Tuple     ):
      __magic_name__		  =							'Hello World!'
      __magic_name__		  =							[0, 3_5378, 6661, 38, 2]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(a__					, self.big_tokenizer.encode(a__     )     )



 @slow
 def 					snake_case__    (       self						:       Tuple     ):
      __magic_name__		  =							(
          'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
          ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
      )
      __magic_name__		  =							[
          0,
          3293,
          83,
          10,
          4552,
          4989,
          7986,
          678,
          10,
          5915,
          111,
          17_9459,
          12_4850,
          4,
          6044,
          237,
          12,
          6,
          5,
          6,
          4,
          6780,
          705,
          15,
          1388,
          44,
          378,
          1_0114,
          711,
          152,
          20,
          6,
          5,
          2_2376,
          642,
          1221,
          1_5190,
          3_4153,
          450,
          5608,
          959,
          1119,
          5_7702,
          136,
          186,
          47,
          1098,
          2_9367,
          47,
          # 4426, # What fairseq tokenizes from "<unk>": "_<"
          # 3678, # What fairseq tokenizes from "<unk>": "unk"
          # 2740, # What fairseq tokenizes from "<unk>": ">"
          3,  # What we tokenize from "<unk>": "<unk>"
          6,  # Residue from the tokenization: an extra sentencepiece underline
          4,
          6044,
          237,
          6284,
          5_0901,
          528,
          31,
          90,
          34,
          927,
          2,
      ]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(a__					, self.big_tokenizer.encode(a__     )     )



 @slow
 def 					snake_case__    (       self						:       str     ):
      __magic_name__		  =							{'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]], '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, 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, 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, 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, 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='''xlm-roberta-base'''					, revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''					, )










'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =set()

   # Replace all the whitespace in our sentence
   _UpperCAmelCase  :  Dict				       =input_str.replace(' '			,       ''				)
   for alpha in input_str:
      if "a" <= alpha.lower() <= "z":
         frequency.add(alpha.lower()				)
   return len(__lowerCamelCase				) == 2_6





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   _UpperCAmelCase  :  Tuple				       =[False] * 2_6
   for char in input_str:
      if char.islower():
         _UpperCAmelCase  :  Dict				       =True
      elif char.isupper():
         _UpperCAmelCase  :  List[str]				       =True
   return all(__lowerCamelCase				)





def lowerCamelCase__     (		__lowerCamelCase			:				str = "The quick brown fox jumps over the lazy dog"			,       ):







   '''simple docstring'''




   return len({char for char in input_str.lower() if char.isalpha()}				) == 2_6





def lowerCamelCase__     (		):







   '''simple docstring'''




   from timeit import timeit

   _UpperCAmelCase  :  List[Any]				       ='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()

