





"""simple docstring"""




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			__UpperCamelCase							(		lowerCamelCase__						,			unittest.TestCase		):
							lowerCamelCase  :   List[Any]    =LEDTokenizer
							lowerCamelCase  :   str    =LEDTokenizerFast
							lowerCamelCase  :   Optional[Any]    =True






							def        __a  (  self )      ->					List[Any]:
								super().setUp()
								a :						List[str]      =							[
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								a :						Any      =							dict(zip(SCREAMING_SNAKE_CASE__ ,   range(len(SCREAMING_SNAKE_CASE__ ) ) ) )
								a :						List[str]      =							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								a :						Optional[Any]      =							{'''unk_token''': '''<unk>'''}

								a :						List[Any]      =							os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES["vocab_file"] )
								a :						int      =							os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES["merges_file"] )
								with open(self.vocab_file ,   "w" ,   encoding="utf-8" ) as fp:
									fp.write(json.dumps(SCREAMING_SNAKE_CASE__ ) + "\n" )
								with open(self.merges_file ,   "w" ,   encoding="utf-8" ) as fp:
									fp.write("\n".join(SCREAMING_SNAKE_CASE__ ) )






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					Union[str, Any]:
								kwargs.update(self.special_tokens_map )
								return self.tokenizer_class.from_pretrained(self.tmpdirname ,   **SCREAMING_SNAKE_CASE__ )






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					List[str]:
								kwargs.update(self.special_tokens_map )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,   **SCREAMING_SNAKE_CASE__ )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Any:
								return "lower newer", "lower newer"






							@cached_property
							def        __a  (  self )      ->					Union[str, Any]:
								return LEDTokenizer.from_pretrained("allenai/led-base-16384" )






							@cached_property
							def        __a  (  self )      ->					List[str]:
								return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )






							@require_torch
							def        __a  (  self )      ->					Any:
								a :						List[str]      =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								a :						Dict      =							[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a :						Union[str, Any]      =							tokenizer(SCREAMING_SNAKE_CASE__ ,   max_length=len(SCREAMING_SNAKE_CASE__ ) ,   padding=SCREAMING_SNAKE_CASE__ ,   return_tensors="pt" )
									self.assertIsInstance(SCREAMING_SNAKE_CASE__ ,   SCREAMING_SNAKE_CASE__ )

									self.assertEqual((2, 9) ,   batch.input_ids.shape )
									self.assertEqual((2, 9) ,   batch.attention_mask.shape )
									a :						Tuple      =							batch.input_ids.tolist()[0]
									self.assertListEqual(SCREAMING_SNAKE_CASE__ ,   SCREAMING_SNAKE_CASE__ )






							@require_torch
							def        __a  (  self )      ->					Union[str, Any]:
								a :						Union[str, Any]      =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a :						Tuple      =							tokenizer(SCREAMING_SNAKE_CASE__ ,   padding=SCREAMING_SNAKE_CASE__ ,   return_tensors="pt" )
									self.assertIn("input_ids" ,   SCREAMING_SNAKE_CASE__ )
									self.assertIn("attention_mask" ,   SCREAMING_SNAKE_CASE__ )
									self.assertNotIn("labels" ,   SCREAMING_SNAKE_CASE__ )
									self.assertNotIn("decoder_attention_mask" ,   SCREAMING_SNAKE_CASE__ )






							@require_torch
							def        __a  (  self )      ->					int:
								a :						int      =							[
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a :						Dict      =							tokenizer(text_target=SCREAMING_SNAKE_CASE__ ,   max_length=32 ,   padding="max_length" ,   return_tensors="pt" )
									self.assertEqual(32 ,   targets["input_ids"].shape[1] )






							@require_torch
							def        __a  (  self )      ->					int:



								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a :						List[str]      =							tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"] ,   padding=SCREAMING_SNAKE_CASE__ ,   truncation=SCREAMING_SNAKE_CASE__ ,   return_tensors="pt" )
									self.assertIsInstance(SCREAMING_SNAKE_CASE__ ,   SCREAMING_SNAKE_CASE__ )
									self.assertEqual(batch.input_ids.shape ,   (2, 5122) )






							@require_torch
							def        __a  (  self )      ->					Optional[Any]:
								a :						Any      =							['''A long paragraph for summarization.''']
								a :						Optional[int]      =							[
								    '''Summary of the text.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a :						Optional[Any]      =							tokenizer(SCREAMING_SNAKE_CASE__ ,   return_tensors="pt" )
									a :						str      =							tokenizer(text_target=SCREAMING_SNAKE_CASE__ ,   return_tensors="pt" )
									a :						Tuple      =							inputs['''input_ids''']
									a :						List[str]      =							targets['''input_ids''']
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






							@require_torch
							def        __a  (  self )      ->					Optional[int]:



								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									a :						Optional[int]      =							['''Summary of the text.''', '''Another summary.''']
									a :						List[str]      =							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

									a :						Union[str, Any]      =							tokenizer(SCREAMING_SNAKE_CASE__ ,   padding=SCREAMING_SNAKE_CASE__ )
									a :						Tuple      =							[[0] * len(SCREAMING_SNAKE_CASE__ ) for x in encoded_output['''input_ids''']]
									a :						Optional[int]      =							tokenizer.pad(SCREAMING_SNAKE_CASE__ )
									self.assertSequenceEqual(outputs["global_attention_mask"] ,   SCREAMING_SNAKE_CASE__ )






							def        __a  (  self )      ->					Optional[int]:
								pass






							def        __a  (  self )      ->					Any:



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
										a :						Optional[int]      =							self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ )
										a :						List[str]      =							self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ ,   **SCREAMING_SNAKE_CASE__ )
										a :						Optional[Any]      =							'''A, <mask> AllenNLP sentence.'''
										a :						Any      =							tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__ ,   add_special_tokens=SCREAMING_SNAKE_CASE__ ,   return_token_type_ids=SCREAMING_SNAKE_CASE__ )
										a :						int      =							tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__ ,   add_special_tokens=SCREAMING_SNAKE_CASE__ ,   return_token_type_ids=SCREAMING_SNAKE_CASE__ )
										self.assertEqual(sum(tokens_r["token_type_ids"] ) ,   sum(tokens_p["token_type_ids"] ) )
										self.assertEqual(
										    sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) ,   sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) ,   )

										a :						Optional[Any]      =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
										a :						Dict      =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
										self.assertSequenceEqual(tokens_p["input_ids"] ,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
										self.assertSequenceEqual(tokens_r["input_ids"] ,   [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )

										self.assertSequenceEqual(
										    SCREAMING_SNAKE_CASE__ ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
										self.assertSequenceEqual(
										    SCREAMING_SNAKE_CASE__ ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )






"""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 				A_       ( snake_case__		)					->  str:
			return 1 / (1 + np.exp(-z		))





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			return (-y * np.log(snake_case__		) - (1 - y) * np.log(1 - h		)).mean()





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Union[str, Any]:
			_UpperCamelCase					:Tuple					     =	np.dot(snake_case__       ,					snake_case__		)
			return np.sum(y * scores - np.log(1 + np.exp(snake_case__		)		)		)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=7_00_00		)					->  Optional[int]:
			_UpperCamelCase					:Union[str, Any]					     =	np.zeros(x.shape[1]		)

			for iterations in range(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Tuple					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[Any]					     =	np.dot(x.T       ,					h - y		) / y.size
						_UpperCamelCase					:int					     =	theta - alpha * gradient  # updating the weights
						_UpperCamelCase					:Union[str, Any]					     =	np.dot(snake_case__       ,					snake_case__		)
						_UpperCamelCase					:Dict					     =	sigmoid_function(snake_case__		)
						_UpperCamelCase					:Optional[int]					     =	cost_function(snake_case__       ,					snake_case__		)
						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__				:Union[str, Any]			   =	datasets.load_iris()
					UpperCamelCase__				:Dict			   =	iris.data[:, :2]
					UpperCamelCase__				:Any			   =	(iris.target != 0) * 1

					UpperCamelCase__				:Tuple			   =	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 				A_       ( snake_case__		)					->  Optional[Any]:
								return sigmoid_function(
								    np.dot(snake_case__       ,					snake_case__		)		)  # 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__))				:Union[str, Any]			   =	(x[:, 0].min(), x[:, 0].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	(x[:, 1].min(), x[:, 1].max())
					((UpperCamelCase__)   ,			(UpperCamelCase__))				:Tuple			   =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
					UpperCamelCase__				:List[Any]			   =	np.c_[xxa.ravel(), xxa.ravel()]
					UpperCamelCase__				:List[Any]			   =	predict_prob(grid).reshape(xxa.shape)
					plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

					plt.legend()




					plt.show()

