



'''simple docstring'''





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
 from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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

_lowerCAmelCase										=     {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
_lowerCAmelCase										=     '''>>zh<<'''
_lowerCAmelCase										=     '''Helsinki-NLP/'''

if is_torch_available():
 _lowerCAmelCase										=     '''pt'''
elif is_tf_available():
 _lowerCAmelCase										=     '''tf'''
else:
 _lowerCAmelCase										=     '''jax'''






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




  __lowercase							:				str           =				MarianTokenizer
  __lowercase							:				str           =				False
  __lowercase							:				Optional[Any]           =				True




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      super().setUp()
      lowerCAmelCase__			:  Tuple											= ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
      lowerCAmelCase__			:  Optional[int]											= dict(zip(__UpperCAmelCase  ,range(len(__UpperCAmelCase				)				)				)				)
      lowerCAmelCase__			:  Union[str, Any]											= Path(self.tmpdirname				)
      save_json(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""vocab"""]				)
      save_json(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]				)
      if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
          copyfile(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""source_spm"""]				)
          copyfile(__UpperCAmelCase  ,save_dir / VOCAB_FILES_NAMES["""target_spm"""]				)

      lowerCAmelCase__			:  Optional[int]											= MarianTokenizer.from_pretrained(self.tmpdirname				)
      tokenizer.save_pretrained(self.tmpdirname				)




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




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




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  Dict											= """</s>"""
      lowerCAmelCase__			:  List[str]											= 0

      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				)   ->		Tuple:
      lowerCAmelCase__			:  str											= list(self.get_tokenizer().get_vocab().keys()				)

      self.assertEqual(vocab_keys[0]  ,"""</s>"""				)
      self.assertEqual(vocab_keys[1]  ,"""<unk>"""				)
      self.assertEqual(vocab_keys[-1]  ,"""<pad>"""				)
      self.assertEqual(len(__UpperCAmelCase				)  ,9				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self.assertEqual(self.get_tokenizer().vocab_size  ,9				)




  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  str											= MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""				)
      lowerCAmelCase__			:  Optional[int]											= en_de_tokenizer(["""I am a small frog"""]  ,return_tensors=__UpperCAmelCase				)
      self.assertIsInstance(__UpperCAmelCase  ,__UpperCAmelCase				)
      lowerCAmelCase__			:  Optional[Any]											= [38, 121, 14, 697, 3_8848, 0]
      self.assertListEqual(__UpperCAmelCase  ,batch.input_ids[0]				)

      lowerCAmelCase__			:  List[Any]											= tempfile.mkdtemp()
      en_de_tokenizer.save_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= [x.name for x in Path(__UpperCAmelCase				).glob("""*"""				)]
      self.assertIn("""source.spm"""  ,__UpperCAmelCase				)
      MarianTokenizer.from_pretrained(__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  Optional[Any]											= self.get_tokenizer()

      lowerCAmelCase__			:  Tuple											= tok(
          ["""I am a small frog""" * 1000, """I am a small frog"""]  ,padding=__UpperCAmelCase  ,truncation=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase				)
      self.assertIsInstance(__UpperCAmelCase  ,__UpperCAmelCase				)
      self.assertEqual(batch.input_ids.shape  ,(2, 512)				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Tuple											= self.get_tokenizer()
      lowerCAmelCase__			:  Tuple											= tok(["""I am a tiny frog""", """I am a small frog"""]  ,padding=__UpperCAmelCase  ,return_tensors=__UpperCAmelCase				)
      self.assertIsInstance(__UpperCAmelCase  ,__UpperCAmelCase				)
      self.assertEqual(batch_smaller.input_ids.shape  ,(2, 10)				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		int:
      # fmt: off
      lowerCAmelCase__			:  str											= {"""input_ids""": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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="""Helsinki-NLP/opus-mt-en-de"""  ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""  ,decode_kwargs={"""use_source_tokenizer""": True}  ,)






  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  Tuple											= MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""				)

      lowerCAmelCase__			:  int											= """Tämä on testi"""
      lowerCAmelCase__			:  Union[str, Any]											= """This is a test"""

      lowerCAmelCase__			:  Optional[int]											= [76, 7, 2047, 2]
      lowerCAmelCase__			:  int											= [69, 12, 11, 940, 2]

      lowerCAmelCase__			:  int											= tokenizer(__UpperCAmelCase				).input_ids
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= tokenizer(text_target=__UpperCAmelCase				).input_ids
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Union[str, Any]											= tokenizer.decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase				)
      self.assertEqual(__UpperCAmelCase  ,__UpperCAmelCase				)







'''simple docstring'''





# 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  (	UpperCamelCase      ):



    """simple docstring"""





    return 1 / (1 + np.exp(-z      ))
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    return (-y * np.log(UpperCamelCase      ) - (1 - y) * np.log(1 - h      )).mean()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase      )      )      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=70000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.zeros(x.shape[1]      )

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


# In[68]:

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

 _lowerCAmelCase										=     0.1
 _lowerCAmelCase										=     logistic_reg(alpha, x, y, max_iterations=7_0000)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



     """simple docstring"""





     return sigmoid_function(
         np.dot(UpperCamelCase							,   UpperCamelCase      )      )  # 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))										=     (x[:, 0].min(), x[:, 0].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 1].min(), x[:, 1].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 _lowerCAmelCase										=     np.c_[xxa.ravel(), xxa.ravel()]
 _lowerCAmelCase										=     predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()




 plt.show()


