




'''simple docstring'''







import numpy as np
import requests






def 		_lowercase		(     ):



		'''simple docstring'''







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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	len_data

		_snake_case     	=	np.dot(lowercase_						,							data_x.transpose()  )
		prod -= data_y.transpose()
		_snake_case     	=	np.dot(lowercase_						,							lowercase_  )
		_snake_case     	=	theta - (alpha / n) * sum_grad
		return theta






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	np.dot(lowercase_						,							data_x.transpose()  )
		prod -= data_y.transpose()
		_snake_case     	=	np.sum(np.square(lowercase_  )  )
		_snake_case     	=	sum_elem / (2 * len_data)
		return error






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	100000
		_snake_case     	=	0.0001550

		_snake_case     	=	data_x.shape[1]
		_snake_case     	=	data_x.shape[0] - 1

		_snake_case     	=	np.zeros((1, no_features)  )

		for i in range(0						,							lowercase_  ):
				_snake_case     	=	run_steep_gradient_descent(lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )
				_snake_case     	=	sum_of_square_error(lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )
				print(f'''At Iteration {i + 1} - Error is {error:.5f}'''  )

		return theta






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	sum(abs(y - predicted_y[i]  ) for i, y in enumerate(lowercase_  )  )
		return total / len(lowercase_  )






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	collect_dataset()

		_snake_case     	=	data.shape[0]
		_snake_case     	=	np.c_[np.ones(lowercase_  ), data[:, :-1]].astype(lowercase_  )
		_snake_case     	=	data[:, -1].astype(lowercase_  )

		_snake_case     	=	run_linear_regression(lowercase_						,							lowercase_  )
		_snake_case     	=	theta.shape[1]
		print("Resultant Feature vector : "  )
		for i in range(0						,							lowercase_  ):
				print(f'''{theta[0, i]:.5f}'''  )


if __name__ == "__main__":
						main()











'''simple docstring'''







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_lowerCamelCase							:						Tuple							     =  logging.getLogger(__name__)





@dataclass
class 					lowerCamelCase_							:




					'''simple docstring'''






					lowercase__   =			field(
					    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
					lowercase__   =			field(default=UpperCAmelCase_,	metadata={"""help""": """Set this flag to use fast tokenization."""}							)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},	)






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )

		if len(sys.argv  ) == 2 and sys.argv[1].endswith(".json"  ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_snake_case	,   _snake_case	,   _snake_case     	=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
		else:
				_snake_case	,   _snake_case	,   _snake_case     	=	parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir  )
		    and os.listdir(training_args.output_dir  )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
				    " --overwrite_output_dir to overcome."  )

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,							datefmt="%m/%d/%Y %H:%M:%S"						,							level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,							)
		logger.warning(
		    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"						,							training_args.local_rank						,							training_args.device						,							training_args.n_gpu						,							bool(training_args.local_rank != -1  )						,							training_args.fpaa						,							)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank  ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("Training/evaluation parameters %s"						,							lowercase_  )

		# Prepare Question-Answering task
		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		_snake_case     	=	AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							)
		_snake_case     	=	AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							use_fast=lowercase_						,							)
		_snake_case     	=	AutoModelForQuestionAnswering.from_pretrained(
		    model_args.model_name_or_path						,							from_tf=bool(".ckpt" in model_args.model_name_or_path  )						,							config=lowercase_						,							cache_dir=model_args.cache_dir						,							)

		# Get datasets
		_snake_case     	=	hasattr(model.config						,							"lang2id"  )
		_snake_case     	=	(
		    SquadDataset(
		        lowercase_						,							tokenizer=lowercase_						,							is_language_sensitive=lowercase_						,							cache_dir=model_args.cache_dir  )
		    if training_args.do_train
		    else None
		)
		_snake_case     	=	(
		    SquadDataset(
		        lowercase_						,							tokenizer=lowercase_						,							mode="dev"						,							is_language_sensitive=lowercase_						,							cache_dir=model_args.cache_dir						,							)
		    if training_args.do_eval
		    else None
		)

		# Data collator
		_snake_case     	=	DataCollatorWithPadding(lowercase_						,							pad_to_multiple_of=8  ) if training_args.fpaa else None

		# Initialize our Trainer
		_snake_case     	=	Trainer(
		    model=lowercase_						,							args=lowercase_						,							train_dataset=lowercase_						,							eval_dataset=lowercase_						,							data_collator=lowercase_						,							)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir  )






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		main()


if __name__ == "__main__":
						main()





