




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			:	Optional[Any]							 =     get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   XLMRobertaTokenizer
    UpperCAmelCase__													=   XLMRobertaTokenizerFast
    UpperCAmelCase__													=   True
    UpperCAmelCase__													=   True


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->List[Any]:





           '''simple docstring'''

           super().setUp()

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


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           A__   				=					'''<pad>'''
           A__   				=					1

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


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Any:





           '''simple docstring'''

           A__   				=					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(UpperCAmelCase__)      ,				1_002)


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





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size      ,				1_002)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					XLMRobertaTokenizer(UpperCAmelCase__      ,				keep_accents=UpperCAmelCase__)

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

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

           A__   				=					tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
           self.assertListEqual(
               UpperCAmelCase__      ,				[
                   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''',
                   '''.''',
               ]      ,				)
           A__   				=					tokenizer.convert_tokens_to_ids(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__      ,				[
                   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 ^
               ]      ,				)

           A__   				=					tokenizer.convert_ids_to_tokens(UpperCAmelCase__)
           self.assertListEqual(
               UpperCAmelCase__      ,				[
                   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  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[str]:





           '''simple docstring'''

           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

           A__   				=					(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})"""):
                         A__   				=					self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__      ,				**UpperCAmelCase__)
                         A__   				=					self.tokenizer_class.from_pretrained(UpperCAmelCase__      ,				**UpperCAmelCase__)

                         A__   				=					tempfile.mkdtemp()

                         A__   				=					tokenizer_r.save_pretrained(UpperCAmelCase__)
                         A__   				=					tokenizer_p.save_pretrained(UpperCAmelCase__)

                         # 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))
                         A__   				=					tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f)
                         self.assertSequenceEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

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

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

                         shutil.rmtree(UpperCAmelCase__)

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

                         A__   				=					tokenizer_r.save_pretrained(UpperCAmelCase__      ,				legacy_format=UpperCAmelCase__)
                         A__   				=					tokenizer_p.save_pretrained(UpperCAmelCase__)

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

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

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

                         shutil.rmtree(UpperCAmelCase__)

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

                         A__   				=					tokenizer_r.save_pretrained(UpperCAmelCase__      ,				legacy_format=UpperCAmelCase__)
                         A__   				=					tokenizer_p.save_pretrained(UpperCAmelCase__)

                         # 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
                         A__   				=					tokenizer_r.from_pretrained(UpperCAmelCase__)
                         A__   				=					tokenizer_p.from_pretrained(UpperCAmelCase__)

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

                         shutil.rmtree(UpperCAmelCase__)


    @cached_property
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->str:





           '''simple docstring'''

           return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''')


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





           '''simple docstring'''

           with tempfile.NamedTemporaryFile() as f:
                  shutil.copyfile(UpperCAmelCase__      ,				f.name)
                  A__   				=					XLMRobertaTokenizer(f.name      ,				keep_accents=UpperCAmelCase__)
                  A__   				=					pickle.dumps(UpperCAmelCase__)
           pickle.loads(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           if not self.test_rust_tokenizer:
                  return

           A__   				=					self.get_tokenizer()
           A__   				=					self.get_rust_tokenizer()

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

           A__   				=					tokenizer.tokenize(UpperCAmelCase__)
           A__   				=					rust_tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

           A__   				=					tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
           A__   				=					rust_tokenizer.encode(UpperCAmelCase__      ,				add_special_tokens=UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

           A__   				=					self.get_rust_tokenizer()
           A__   				=					tokenizer.encode(UpperCAmelCase__)
           A__   				=					rust_tokenizer.encode(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[int]:





           '''simple docstring'''

           A__   				=					'''Hello World!'''
           A__   				=					[0, 35_378, 6_661, 38, 2]
           # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
           # xlmr.eval()
           # xlmr.encode(symbols)

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


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

           A__   				=					(
               '''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'''
           )
           A__   				=					[
               0,
               3_293,
               83,
               10,
               4_552,
               4_989,
               7_986,
               678,
               10,
               5_915,
               111,
               179_459,
               124_850,
               4,
               6_044,
               237,
               12,
               6,
               5,
               6,
               4,
               6_780,
               705,
               15,
               1_388,
               44,
               378,
               10_114,
               711,
               152,
               20,
               6,
               5,
               22_376,
               642,
               1_221,
               15_190,
               34_153,
               450,
               5_608,
               959,
               1_119,
               57_702,
               136,
               186,
               47,
               1_098,
               29_367,
               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,
               6_044,
               237,
               6_284,
               50_901,
               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(UpperCAmelCase__      ,				self.big_tokenizer.encode(UpperCAmelCase__))





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					{'''input_ids''': [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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=UpperCAmelCase__      ,				model_name='''xlm-roberta-base'''      ,				revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''      ,				)








# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       return (-y * np.log(lowercase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.dot(lowercase_				,       lowercase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowercase_		)		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

       for iterations in range(lowercase_		):
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					np.dot(x.T				,       h - y		) / y.size
              A__   				=					theta - alpha * gradient  # updating the weights
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					cost_function(lowercase_				,       lowercase_		)
              if iterations % 100 == 0:
                     print(f"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
       return theta


# In[68]:

if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     datasets.load_iris()
  _lowerCamelCase			:	Any							 =     iris.data[:, :2]
  _lowerCamelCase			:	Dict							 =     (iris.target != 0) * 1

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


