



# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
__SCREAMING_SNAKE_CASE :      List[str]             =1e-10
__SCREAMING_SNAKE_CASE :      str             ="This is incredible! I love it, this is the best chicken I have ever had."
__SCREAMING_SNAKE_CASE :      Tuple             =100


class   SCREAMING_SNAKE_CASE__				(   nn.Module					):
	"""simple docstring"""


	def __init__(		self					,   A					,   A="gpt2-medium"					,   A=False					,   A="cpu"			) ->							Union[str, Any]:
				super().__init__()
				A:      Optional[Any]														=   GPTaTokenizer.from_pretrained(__UpperCamelCase			)
				A:      str														=   GPTaLMHeadModel.from_pretrained(__UpperCamelCase			)
				A:      Union[str, Any]														=   self.encoder.transformer.config.hidden_size
				A:      Union[str, Any]														=   ClassificationHead(class_size=__UpperCamelCase					,   embed_size=self.embed_size			)
				A:      Tuple														=   cached_mode
				A:      int														=   device


	def      a__ (		self			) ->							int:
				return self.classifier_head


	def      a__ (		self			) ->							Optional[int]:


				for param in self.encoder.parameters():
							A:      str														=   False
				self.classifier_head.train()


	def      a__ (		self					,   A			) ->							Dict:
				A:      List[Any]														=   x.ne(0			).unsqueeze(2			).repeat(1					,   1					,   self.embed_size			).float().to(self.device			).detach()
				A:      List[Any]														=   self.encoder.transformer(__UpperCamelCase			)['''last_hidden_state''']
				A:      Optional[int]														=   hidden * mask
				A:      Optional[int]														=   torch.sum(__UpperCamelCase					,   dim=1			) / (torch.sum(__UpperCamelCase					,   dim=1			).detach() + EPSILON)
				return avg_hidden





	def      a__ (		self					,   A			) ->							Optional[Any]:


				if self.cached_mode:
							A:      Dict														=   x.to(self.device			)
				else:
							A:      Union[str, Any]														=   self.avg_representation(x.to(self.device			)			)

				A:      Optional[int]														=   self.classifier_head(__UpperCamelCase			)
				A:      str														=   nn.functional.log_softmax(__UpperCamelCase					,   dim=-1			)

				return probs







class   SCREAMING_SNAKE_CASE__				(   data.Dataset					):
	"""simple docstring"""


	def __init__(		self					,   A					,   A			) ->							Optional[int]:
				A:      List[Any]														=   X
				A:      Optional[Any]														=   y


	def __len__(		self			) ->							Any:
				return len(self.X			)





	def __getitem__(		self					,   A			) ->							Any:
				A:      str														=   {}
				A:      Tuple														=   self.X[index]
				A:      Any														=   self.y[index]
				return data







def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[int]				):


			'''simple docstring'''







			def pad_sequences(lowerCamelCase__   :      str				):
						A:      List[Any]														=   [len(snake_case_				) for seq in sequences]

						A:      Optional[Any]														=   torch.zeros(len(snake_case_				)							,							max(snake_case_				)				).long()  # padding value = 0

						for i, seq in enumerate(snake_case_				):
									A:      Any														=   lengths[i]
									A:      Tuple														=   seq[:end]

						return padded_sequences, lengths

			A:      Tuple														=   {}
			for key in data[0].keys():
						A:      List[str]														=   [d[key] for d in data]

			A:      Any														=   pad_sequences(item_info["""X"""]				)
			A:      int														=   torch.tensor(item_info["""y"""]							,							dtype=torch.long				)

			return x_batch, y_batch



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Any				):


			'''simple docstring'''







			A:      int														=   {}
			for key in data[0].keys():
						A:      int														=   [d[key] for d in data]

			A:      Union[str, Any]														=   torch.cat(item_info["""X"""]							,							0				)
			A:      List[Any]														=   torch.tensor(item_info["""y"""]							,							dtype=torch.long				)

			return x_batch, y_batch



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Optional[int]							,							lowerCamelCase__   :      Tuple=0							,							lowerCamelCase__   :      List[str]=10							,							lowerCamelCase__   :      Dict="cpu"				):


			'''simple docstring'''







			A:      int														=   0
			discriminator.train_custom()
			for batch_idx, (input_t, target_t) in enumerate(snake_case_				):
						A:      str														=   input_t.to(snake_case_				), target_t.to(snake_case_				)

						optimizer.zero_grad()

						A:      Dict														=   discriminator(snake_case_				)
						A:      List[str]														=   nn.functional.nll_loss(snake_case_							,							snake_case_				)
						loss.backward(retain_graph=snake_case_				)
						optimizer.step()

						samples_so_far += len(snake_case_				)

						if batch_idx % log_interval == 0:
									print(
									    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
									        epoch + 1							,							snake_case_							,							len(data_loader.dataset				)							,							100 * samples_so_far / len(data_loader.dataset				)							,							loss.item()							,							)				)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Optional[int]="cpu"				):


			'''simple docstring'''







			discriminator.eval()
			A:      int														=   0
			A:      Tuple														=   0
			with torch.no_grad():
						for input_t, target_t in data_loader:
									A:      Optional[int]														=   input_t.to(snake_case_				), target_t.to(snake_case_				)
									A:      Tuple														=   discriminator(snake_case_				)
									# sum up batch loss
									test_loss += nn.functional.nll_loss(snake_case_							,							snake_case_							,							reduction="""sum"""				).item()
									# get the index of the max log-probability
									A:      Dict														=   output_t.argmax(dim=1							,							keepdim=snake_case_				)
									correct += pred_t.eq(target_t.view_as(snake_case_				)				).sum().item()

			test_loss /= len(data_loader.dataset				)

			print(
			    """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
			        snake_case_							,							snake_case_							,							len(data_loader.dataset				)							,							100.0 * correct / len(data_loader.dataset				)				)				)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      Any							,							lowerCamelCase__   :      Optional[int]=False							,							lowerCamelCase__   :      Optional[Any]="cpu"				):


			'''simple docstring'''







			A:      Any														=   model.tokenizer.encode(snake_case_				)
			A:      Optional[Any]														=   torch.tensor([input_t]							,							dtype=torch.long							,							device=snake_case_				)
			if cached:
						A:      List[str]														=   model.avg_representation(snake_case_				)

			A:      Optional[Any]														=   model(snake_case_				).data.cpu().numpy().flatten().tolist()
			print("""Input sentence:"""							,							snake_case_				)
			print(
			    """Predictions:"""							,							""", """.join("""{}: {:.4f}""".format(snake_case_							,							math.exp(snake_case_				)				) for c, log_prob in zip(snake_case_							,							snake_case_				)				)							,							)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      Optional[int]							,							lowerCamelCase__   :      Union[str, Any]							,							lowerCamelCase__   :      str=False							,							lowerCamelCase__   :      Optional[Any]="cpu"				):


			'''simple docstring'''







			A:      int														=   torch.utils.data.DataLoader(dataset=snake_case_							,							batch_size=snake_case_							,							collate_fn=snake_case_				)

			A:      Optional[int]														=   []
			A:      Any														=   []
			for batch_idx, (x, y) in enumerate(tqdm(snake_case_							,							ascii=snake_case_				)				):
						with torch.no_grad():
									A:      Union[str, Any]														=   x.to(snake_case_				)
									A:      Dict														=   discriminator.avg_representation(snake_case_				).cpu().detach()
									A:      List[Any]														=   torch.unbind(avg_rep.unsqueeze(1				)				)
									xs += avg_rep_list
									ys += y.cpu().numpy().tolist()

			A:      Any														=   torch.utils.data.DataLoader(
			    dataset=Dataset(snake_case_							,							snake_case_				)							,							batch_size=snake_case_							,							shuffle=snake_case_							,							collate_fn=snake_case_				)

			return data_loader



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      str							,							lowerCamelCase__   :      Any=None							,							lowerCamelCase__   :      Any="gpt2-medium"							,							lowerCamelCase__   :      Any=10							,							lowerCamelCase__   :      str=64							,							lowerCamelCase__   :      int=10							,							lowerCamelCase__   :      str=False							,							lowerCamelCase__   :      Tuple=False							,							lowerCamelCase__   :      int=False							,							):


			'''simple docstring'''







			A:      Optional[int]														=   '''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

			print("""Preprocessing {} dataset...""".format(snake_case_				)				)
			A:      List[str]														=   time.time()

			if dataset == "SST":
						A:      Tuple														=   ['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
						A:      Dict														=   {c: i for i, c in enumerate(snake_case_				)}

						A:      Any														=   Discriminator(
						    class_size=len(snake_case_				)							,							pretrained_model=snake_case_							,							cached_mode=snake_case_							,							device=snake_case_				).to(snake_case_				)

						A:      int														=   torchtext_data.Field()
						A:      int														=   torchtext_data.Field(sequential=snake_case_				)
						A:      int														=   datasets.SST.splits(
						    snake_case_							,							snake_case_							,							fine_grained=snake_case_							,							train_subtrees=snake_case_							,							)

						A:      int														=   []
						A:      Any														=   []
						for i in trange(len(snake_case_				)							,							ascii=snake_case_				):
									A:      Tuple														=   TreebankWordDetokenizer().detokenize(vars(train_data[i]				)["""text"""]				)
									A:      str														=   discriminator.tokenizer.encode(snake_case_				)
									A:      Optional[int]														=   torch.tensor([50256] + seq							,							device=snake_case_							,							dtype=torch.long				)
									x.append(snake_case_				)
									y.append(classaidx[vars(train_data[i]				)["""label"""]]				)
						A:      Union[str, Any]														=   Dataset(snake_case_							,							snake_case_				)

						A:      List[str]														=   []
						A:      Dict														=   []
						for i in trange(len(snake_case_				)							,							ascii=snake_case_				):
									A:      str														=   TreebankWordDetokenizer().detokenize(vars(test_data[i]				)["""text"""]				)
									A:      List[str]														=   discriminator.tokenizer.encode(snake_case_				)
									A:      Optional[int]														=   torch.tensor([50256] + seq							,							device=snake_case_							,							dtype=torch.long				)
									test_x.append(snake_case_				)
									test_y.append(classaidx[vars(test_data[i]				)["""label"""]]				)
						A:      str														=   Dataset(snake_case_							,							snake_case_				)

						A:      Optional[Any]														=   {
						    '''class_size''': len(snake_case_				),
						    '''embed_size''': discriminator.embed_size,
						    '''pretrained_model''': pretrained_model,
						    '''class_vocab''': classaidx,
						    '''default_class''': 2,
						}

			elif dataset == "clickbait":
						A:      List[Any]														=   ['''non_clickbait''', '''clickbait''']
						A:      str														=   {c: i for i, c in enumerate(snake_case_				)}

						A:      Union[str, Any]														=   Discriminator(
						    class_size=len(snake_case_				)							,							pretrained_model=snake_case_							,							cached_mode=snake_case_							,							device=snake_case_				).to(snake_case_				)

						with open("""datasets/clickbait/clickbait_train_prefix.txt"""				) as f:
									A:      Dict														=   []
									for i, line in enumerate(snake_case_				):
												try:
															data.append(eval(snake_case_				)				)
												except Exception:
															print("""Error evaluating line {}: {}""".format(snake_case_							,							snake_case_				)				)
															continue
						A:      int														=   []
						A:      List[Any]														=   []
						with open("""datasets/clickbait/clickbait_train_prefix.txt"""				) as f:
									for i, line in enumerate(tqdm(snake_case_							,							ascii=snake_case_				)				):
												try:
															A:      Union[str, Any]														=   eval(snake_case_				)
															A:      List[Any]														=   discriminator.tokenizer.encode(d["""text"""]				)

															if len(snake_case_				) < max_length_seq:
																		A:      List[Any]														=   torch.tensor([50256] + seq							,							device=snake_case_							,							dtype=torch.long				)
															else:
																		print("""Line {} is longer than maximum length {}""".format(snake_case_							,							snake_case_				)				)
																		continue
															x.append(snake_case_				)
															y.append(d["""label"""]				)
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(snake_case_				)				)
															pass

						A:      Optional[int]														=   Dataset(snake_case_							,							snake_case_				)
						A:      List[str]														=   int(0.9 * len(snake_case_				)				)
						A:      str														=   len(snake_case_				) - train_size
						A:      Any														=   torch.utils.data.random_split(snake_case_							,							[train_size, test_size]				)

						A:      int														=   {
						    '''class_size''': len(snake_case_				),
						    '''embed_size''': discriminator.embed_size,
						    '''pretrained_model''': pretrained_model,
						    '''class_vocab''': classaidx,
						    '''default_class''': 1,
						}

			elif dataset == "toxic":
						A:      int														=   ['''non_toxic''', '''toxic''']
						A:      List[str]														=   {c: i for i, c in enumerate(snake_case_				)}

						A:      Optional[int]														=   Discriminator(
						    class_size=len(snake_case_				)							,							pretrained_model=snake_case_							,							cached_mode=snake_case_							,							device=snake_case_				).to(snake_case_				)

						A:      int														=   []
						A:      List[str]														=   []
						with open("""datasets/toxic/toxic_train.txt"""				) as f:
									for i, line in enumerate(tqdm(snake_case_							,							ascii=snake_case_				)				):
												try:
															A:      Union[str, Any]														=   eval(snake_case_				)
															A:      Tuple														=   discriminator.tokenizer.encode(d["""text"""]				)

															if len(snake_case_				) < max_length_seq:
																		A:      List[str]														=   torch.tensor([50256] + seq							,							device=snake_case_							,							dtype=torch.long				)
															else:
																		print("""Line {} is longer than maximum length {}""".format(snake_case_							,							snake_case_				)				)
																		continue
															x.append(snake_case_				)
															y.append(int(np.sum(d["""label"""]				) > 0				)				)
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(snake_case_				)				)
															pass

						A:      Dict														=   Dataset(snake_case_							,							snake_case_				)
						A:      List[Any]														=   int(0.9 * len(snake_case_				)				)
						A:      Tuple														=   len(snake_case_				) - train_size
						A:      Optional[Any]														=   torch.utils.data.random_split(snake_case_							,							[train_size, test_size]				)

						A:      Dict														=   {
						    '''class_size''': len(snake_case_				),
						    '''embed_size''': discriminator.embed_size,
						    '''pretrained_model''': pretrained_model,
						    '''class_vocab''': classaidx,
						    '''default_class''': 0,
						}

			else:  # if dataset == "generic":
						# This assumes the input dataset is a TSV with the following structure:
						# class \t text

						if dataset_fp is None:
									raise ValueError("""When generic dataset is selected, dataset_fp needs to be specified aswell."""				)

						A:      Any														=   set()
						with open(snake_case_				) as f:
									A:      Union[str, Any]														=   csv.reader(snake_case_							,							delimiter="""\t"""				)
									for row in tqdm(snake_case_							,							ascii=snake_case_				):
												if row:
															classes.add(row[0]				)

						A:      int														=   sorted(snake_case_				)
						A:      int														=   {c: i for i, c in enumerate(snake_case_				)}

						A:      Any														=   Discriminator(
						    class_size=len(snake_case_				)							,							pretrained_model=snake_case_							,							cached_mode=snake_case_							,							device=snake_case_				).to(snake_case_				)

						A:      List[Any]														=   []
						A:      Any														=   []
						with open(snake_case_				) as f:
									A:      str														=   csv.reader(snake_case_							,							delimiter="""\t"""				)
									for i, row in enumerate(tqdm(snake_case_							,							ascii=snake_case_				)				):
												if row:
															A:      int														=   row[0]
															A:      str														=   row[1]

															try:
																		A:      str														=   discriminator.tokenizer.encode(snake_case_				)
																		if len(snake_case_				) < max_length_seq:
																					A:      List[str]														=   torch.tensor([50256] + seq							,							device=snake_case_							,							dtype=torch.long				)

																		else:
																					print("""Line {} is longer than maximum length {}""".format(snake_case_							,							snake_case_				)				)
																					continue

																		x.append(snake_case_				)
																		y.append(classaidx[label]				)

															except Exception:
																		print("""Error tokenizing line {}, skipping it""".format(snake_case_				)				)
																		pass

						A:      int														=   Dataset(snake_case_							,							snake_case_				)
						A:      Union[str, Any]														=   int(0.9 * len(snake_case_				)				)
						A:      Any														=   len(snake_case_				) - train_size
						A:      Union[str, Any]														=   torch.utils.data.random_split(snake_case_							,							[train_size, test_size]				)

						A:      Union[str, Any]														=   {
						    '''class_size''': len(snake_case_				),
						    '''embed_size''': discriminator.embed_size,
						    '''pretrained_model''': pretrained_model,
						    '''class_vocab''': classaidx,
						    '''default_class''': 0,
						}

			A:      Union[str, Any]														=   time.time()
			print("""Preprocessed {} data points""".format(len(snake_case_				) + len(snake_case_				)				)				)
			print("""Data preprocessing took: {:.3f}s""".format(end - start				)				)

			if cached:
						print("""Building representation cache..."""				)

						A:      Optional[Any]														=   time.time()

						A:      Any														=   get_cached_data_loader(snake_case_							,							snake_case_							,							snake_case_							,							shuffle=snake_case_							,							device=snake_case_				)

						A:      str														=   get_cached_data_loader(snake_case_							,							snake_case_							,							snake_case_							,							device=snake_case_				)

						A:      int														=   time.time()
						print("""Building representation cache took: {:.3f}s""".format(end - start				)				)

			else:
						A:      Dict														=   torch.utils.data.DataLoader(
						    dataset=snake_case_							,							batch_size=snake_case_							,							shuffle=snake_case_							,							collate_fn=snake_case_				)
						A:      List[str]														=   torch.utils.data.DataLoader(dataset=snake_case_							,							batch_size=snake_case_							,							collate_fn=snake_case_				)

			if save_model:
						with open("""{}_classifier_head_meta.json""".format(snake_case_				)							,							"""w"""				) as meta_file:
									json.dump(snake_case_							,							snake_case_				)

			A:      Union[str, Any]														=   optim.Adam(discriminator.parameters()							,							lr=0.00_01				)

			for epoch in range(snake_case_				):
						A:      str														=   time.time()
						print("""\nEpoch"""							,							epoch + 1				)

						train_epoch(
						    discriminator=snake_case_							,							data_loader=snake_case_							,							optimizer=snake_case_							,							epoch=snake_case_							,							log_interval=snake_case_							,							device=snake_case_							,							)
						evaluate_performance(data_loader=snake_case_							,							discriminator=snake_case_							,							device=snake_case_				)

						A:      Optional[int]														=   time.time()
						print("""Epoch took: {:.3f}s""".format(end - start				)				)

						print("""\nExample prediction"""				)
						predict(snake_case_							,							snake_case_							,							snake_case_							,							cached=snake_case_							,							device=snake_case_				)

						if save_model:
									# torch.save(discriminator.state_dict(),
									#           "{}_discriminator_{}.pt".format(
									#               args.dataset, epoch + 1
									#               ))
									torch.save(
									    discriminator.get_classifier().state_dict()							,							"""{}_classifier_head_epoch_{}.pt""".format(snake_case_							,							epoch + 1				)							,							)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE :      Dict             =argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
					parser.add_argument(
					    '--dataset',
					    type=str,
					    default='SST',
					    choices=('SST', 'clickbait', 'toxic', 'generic'),
					    help=(
					        'dataset to train the discriminator on.'
					        'In case of generic, the dataset is expected'
					        'to be a TSBV file with structure: class \\t text'
					    ),
					)
					parser.add_argument(
					    '--dataset_fp',
					    type=str,
					    default='',
					    help='File path of the dataset to use. Needed only in case of generic datadset',
					)
					parser.add_argument(
					    '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
					)
					parser.add_argument('--epochs', type=int, default=10, metavar='N', help='Number of training epochs')
					parser.add_argument(
					    '--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)'
					)
					parser.add_argument(
					    '--log_interval',
					    type=int,
					    default=10,
					    metavar='N',
					    help='how many batches to wait before logging training status',
					)
					parser.add_argument('--save_model', action='store_true', help='whether to save the model')
					parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
					parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
					__SCREAMING_SNAKE_CASE :      Tuple             =parser.parse_args()

					train_discriminator(**(vars(args)))










'''simple docstring'''
import numpy as np
import requests



def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      List[Any]														=   requests.get(
			    """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
			    """master/Week1/ADRvsRating.csv"""				)
			A:      List[str]														=   response.text.splitlines()
			A:      Dict														=   []
			for item in lines:
						A:      int														=   item.split(""","""				)
						data.append(lowerCamelCase__				)
			data.pop(0				)  # This is for removing the labels from the list
			A:      Tuple														=   np.matrix(lowerCamelCase__				)
			return dataset



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			A:      Union[str, Any]														=   len_data

			A:      Tuple														=   np.dot(lowerCamelCase__							,							data_x.transpose()				)
			prod -= data_y.transpose()
			A:      Optional[Any]														=   np.dot(lowerCamelCase__							,							lowerCamelCase__				)
			A:      Optional[Any]														=   theta - (alpha / n) * sum_grad
			return theta



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      int							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Union[str, Any]							,							lowerCamelCase__   :      Union[str, Any]				):


			'''simple docstring'''







			A:      str														=   np.dot(lowerCamelCase__							,							data_x.transpose()				)
			prod -= data_y.transpose()
			A:      Any														=   np.sum(np.square(lowerCamelCase__				)				)
			A:      List[Any]														=   sum_elem / (2 * len_data)
			return error



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Union[str, Any]				):


			'''simple docstring'''







			A:      Dict														=   100000
			A:      Dict														=   0.0_00_15_50

			A:      Union[str, Any]														=   data_x.shape[1]
			A:      Tuple														=   data_x.shape[0] - 1

			A:      str														=   np.zeros((1, no_features)				)

			for i in range(0							,							lowerCamelCase__				):
						A:      List[str]														=   run_steep_gradient_descent(lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__				)
						A:      str														=   sum_of_square_error(lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__				)
						print(f'At Iteration {i + 1} - Error is {error:.5f}'				)

			return theta



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      List[str]				):


			'''simple docstring'''







			A:      List[Any]														=   sum(abs(y - predicted_y[i]				) for i, y in enumerate(lowerCamelCase__				)				)
			return total / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      Optional[Any]														=   collect_dataset()

			A:      str														=   data.shape[0]
			A:      Union[str, Any]														=   np.c_[np.ones(lowerCamelCase__				), data[:, :-1]].astype(lowerCamelCase__				)
			A:      Any														=   data[:, -1].astype(lowerCamelCase__				)

			A:      Dict														=   run_linear_regression(lowerCamelCase__							,							lowerCamelCase__				)
			A:      Union[str, Any]														=   theta.shape[1]
			print("""Resultant Feature vector : """				)
			for i in range(0							,							lowerCamelCase__				):
						print(f'{theta[0, i]:.5f}'				)


if __name__ == "__main__":
					main()





