'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__     :	str				=			get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCamelCase__     :	Dict				=			250_004
UpperCamelCase__     :	Optional[int]				=			250_020


@require_sentencepiece
@require_tokenizers
class 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      int      							=   MBartaaTokenizer
      UpperCAmelCase_      :      Union[str, Any]      							=   MBartaaTokenizerFast
      UpperCAmelCase_      :      List[str]      							=   True
      UpperCAmelCase_      :      Optional[int]      							=   True




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase__							:				Dict   				=  MBartaaTokenizer(__UpperCamelCase					,src_lang='''en_XX'''					,tgt_lang='''ro_RO'''					,keep_accents=__UpperCamelCase							)
         tokenizer.save_pretrained(self.tmpdirname							)




      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  '''<s>'''
         UpperCAmelCase__							:				int   				=  0

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  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							)					,1054							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         self.assertEqual(self.get_tokenizer().vocab_size					,1054							)




      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  MBartaaTokenizer(__UpperCamelCase					,src_lang='''en_XX'''					,tgt_lang='''ro_RO'''					,keep_accents=__UpperCamelCase							)

         UpperCAmelCase__							:				str   				=  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]]					,)

         UpperCAmelCase__							:				Any   				=  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''', '''.''']					,)
         UpperCAmelCase__							:				List[str]   				=  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]
             ]					,)

         UpperCAmelCase__							:				Any   				=  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>''', '''.''']					,)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  {'''input_ids''': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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='''facebook/mbart-large-50'''					,revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''					,)




      def 		lowerCAmelCase__					(				self							)			->			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

         UpperCAmelCase__							:				List[str]   				=  (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
               UpperCAmelCase__							:				str   				=  self.rust_tokenizer_class.from_pretrained(__UpperCamelCase					,**__UpperCamelCase							)
               UpperCAmelCase__							:				Tuple   				=  self.tokenizer_class.from_pretrained(__UpperCamelCase					,**__UpperCamelCase							)

               UpperCAmelCase__							:				str   				=  tempfile.mkdtemp()

               UpperCAmelCase__							:				int   				=  tokenizer_r.save_pretrained(__UpperCamelCase							)
               UpperCAmelCase__							:				Any   				=  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							)							)
               UpperCAmelCase__							:				Dict   				=  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
               UpperCAmelCase__							:				str   				=  tokenizer_r.from_pretrained(__UpperCamelCase							)
               UpperCAmelCase__							:				List[Any]   				=  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
               UpperCAmelCase__							:				Tuple   				=  tempfile.mkdtemp()

               UpperCAmelCase__							:				int   				=  tokenizer_r.save_pretrained(__UpperCamelCase					,legacy_format=__UpperCamelCase							)
               UpperCAmelCase__							:				List[Any]   				=  tokenizer_p.save_pretrained(__UpperCamelCase							)

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

               # Checks everything loads correctly in the same way
               UpperCAmelCase__							:				List[str]   				=  tokenizer_r.from_pretrained(__UpperCamelCase							)
               UpperCAmelCase__							:				List[Any]   				=  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
               UpperCAmelCase__							:				Dict   				=  tempfile.mkdtemp()

               UpperCAmelCase__							:				str   				=  tokenizer_r.save_pretrained(__UpperCamelCase					,legacy_format=__UpperCamelCase							)
               UpperCAmelCase__							:				Union[str, Any]   				=  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
               UpperCAmelCase__							:				List[Any]   				=  tokenizer_r.from_pretrained(__UpperCamelCase							)
               UpperCAmelCase__							:				str   				=  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							)




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Optional[Any]      							=   '''facebook/mbart-large-50-one-to-many-mmt'''
      UpperCAmelCase_      :      str      							=   [
          ''' UN Chief Says There Is No Military Solution in Syria''',
          ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
      ]
      UpperCAmelCase_      :      Optional[Any]      							=   [
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
          '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
          ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
          ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
      ]
      UpperCAmelCase_      :      List[Any]      							=   [EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]




      @classmethod
      def 		lowerCAmelCase__					(				cls							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name					,src_lang='''en_XX'''					,tgt_lang='''ro_RO'''							)
         UpperCAmelCase__							:				Union[str, Any]   				=  1
         return cls




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



         '''simple docstring'''



         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']					,250001							)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']					,250004							)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']					,250020							)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']					,250038							)




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
         self.assertListEqual(self.expected_src_tokens					,__UpperCamelCase							)




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



         '''simple docstring'''



         self.assertIn(__UpperCamelCase					,self.tokenizer.all_special_ids							)
         UpperCAmelCase__							:				List[str]   				=  [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
         UpperCAmelCase__							:				Optional[int]   				=  self.tokenizer.decode(__UpperCamelCase					,skip_special_tokens=__UpperCamelCase							)
         UpperCAmelCase__							:				Optional[int]   				=  self.tokenizer.decode(generated_ids[1:]					,skip_special_tokens=__UpperCamelCase							)
         self.assertEqual(__UpperCamelCase					,__UpperCamelCase							)
         self.assertNotIn(self.tokenizer.eos_token					,__UpperCamelCase							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  ['''this is gunna be a long sentence ''' * 20]
         assert isinstance(src_text[0]					,__UpperCamelCase							)
         UpperCAmelCase__							:				List[str]   				=  10
         UpperCAmelCase__							:				str   				=  self.tokenizer(__UpperCamelCase					,max_length=__UpperCamelCase					,truncation=__UpperCamelCase							).input_ids[0]
         self.assertEqual(ids[0]					,__UpperCamelCase							)
         self.assertEqual(ids[-1]					,2							)
         self.assertEqual(len(__UpperCamelCase							)					,__UpperCamelCase							)




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



         '''simple docstring'''



         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']							)					,[250053, 250001]							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  tempfile.mkdtemp()
         UpperCAmelCase__							:				List[str]   				=  self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__UpperCamelCase							)
         UpperCAmelCase__							:				Optional[Any]   				=  MBartaaTokenizer.from_pretrained(__UpperCamelCase							)
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,__UpperCamelCase							)




      @require_torch
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.tokenizer(self.src_text					,text_target=self.tgt_text					,padding=__UpperCamelCase					,return_tensors='''pt'''							)
         UpperCAmelCase__							:				Optional[int]   				=  shift_tokens_right(batch['''labels''']					,self.tokenizer.pad_token_id							)

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]




      @require_torch
      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  self.tokenizer(
             self.src_text					,text_target=self.tgt_text					,padding=__UpperCamelCase					,truncation=__UpperCamelCase					,max_length=len(self.expected_src_tokens							)					,return_tensors='''pt'''					,)
         UpperCAmelCase__							:				List[str]   				=  shift_tokens_right(batch['''labels''']					,self.tokenizer.pad_token_id							)

         self.assertIsInstance(__UpperCamelCase					,__UpperCamelCase							)

         self.assertEqual((2, 14)					,batch.input_ids.shape							)
         self.assertEqual((2, 14)					,batch.attention_mask.shape							)
         UpperCAmelCase__							:				Any   				=  batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens					,__UpperCamelCase							)
         self.assertEqual(2					,batch.decoder_input_ids[0, 0]							)  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens					,[EN_CODE]							)
         self.assertEqual(self.tokenizer.suffix_tokens					,[self.tokenizer.eos_token_id]							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  self.tokenizer(self.src_text					,padding=__UpperCamelCase					,truncation=__UpperCamelCase					,max_length=3					,return_tensors='''pt'''							)
         UpperCAmelCase__							:				List[Any]   				=  self.tokenizer(
             text_target=self.tgt_text					,padding=__UpperCamelCase					,truncation=__UpperCamelCase					,max_length=10					,return_tensors='''pt'''							)
         UpperCAmelCase__							:				Optional[Any]   				=  targets['''input_ids''']
         UpperCAmelCase__							:				Union[str, Any]   				=  shift_tokens_right(__UpperCamelCase					,self.tokenizer.pad_token_id							)

         self.assertEqual(batch.input_ids.shape[1]					,3							)
         self.assertEqual(batch.decoder_input_ids.shape[1]					,10							)




      @require_torch
      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.tokenizer._build_translation_inputs(
             '''A test'''					,return_tensors='''pt'''					,src_lang='''en_XX'''					,tgt_lang='''ar_AR'''							)

         self.assertEqual(
             nested_simplify(__UpperCamelCase							)					,{
                 # en_XX, A, test, EOS
                 '''input_ids''': [[250004, 62, 3034, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 250001,
             }					,)





'''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 __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   return 1 / (1 + np.exp(-z    ))







def __UpperCamelCase(   _A					:				int  ,			_A					:				str    ):
   '''simple docstring'''
   return (-y * np.log(_A    ) - (1 - y) * np.log(1 - h    )).mean()







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Tuple  ,			_A					:				Any    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  np.dot(_A  ,			_A    )
   return np.sum(y * scores - np.log(1 + np.exp(_A    )    )    )







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Optional[int]  ,			_A					:				List[Any]  ,			_A					:				str=7_00_00    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  np.zeros(x.shape[1]    )

   for iterations in range(_A    ):
      UpperCAmelCase__							:				Tuple   				=  np.dot(_A  ,			_A    )
      UpperCAmelCase__							:				Optional[Any]   				=  sigmoid_function(_A    )
      UpperCAmelCase__							:				Optional[int]   				=  np.dot(x.T  ,			h - y    ) / y.size
      UpperCAmelCase__							:				List[Any]   				=  theta - alpha * gradient  # updating the weights
      UpperCAmelCase__							:				Tuple   				=  np.dot(_A  ,			_A    )
      UpperCAmelCase__							:				Tuple   				=  sigmoid_function(_A    )
      UpperCAmelCase__							:				Optional[int]   				=  cost_function(_A  ,			_A    )
      if iterations % 1_00 == 0:
         print(F'''loss: {j} \t'''    )  # printing the loss after every 100 iterations
   return theta


# In[68]:

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

  UpperCamelCase__     :	Any				=			0.1
  UpperCamelCase__     :	List[str]				=			logistic_reg(alpha, x, y, max_iterations=70_000)
  print('theta: ', theta)  # printing the theta i.e our weights vector







  def __UpperCamelCase(   _A					:				Any    ):
     '''simple docstring'''
     return sigmoid_function(
         np.dot(_A  ,			_A    )    )  # 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')
  ((UpperCamelCase__)				,     (UpperCamelCase__))     :	Any				=			(x[:, 0].min(), x[:, 0].max())
  ((UpperCamelCase__)				,     (UpperCamelCase__))     :	Optional[Any]				=			(x[:, 1].min(), x[:, 1].max())
  ((UpperCamelCase__)				,     (UpperCamelCase__))     :	Any				=			np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  UpperCamelCase__     :	str				=			np.c_[xxa.ravel(), xxa.ravel()]
  UpperCamelCase__     :	Union[str, Any]				=			predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()
  plt.show()




