



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
UpperCAmelCase_		   =				logging.getLogger(__name__)

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[Any]							)     ->    Dict:
   np.random.seed(args.seed							)
   torch.manual_seed(args.seed							)
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    List[str]:
   _A						     =					argparse.ArgumentParser()
   parser.add_argument(
       '''--model_name_or_path'''  ,      default=_snake_case  ,      type=_snake_case  ,      required=_snake_case  ,      )
   parser.add_argument('''--prompt'''  ,      type=_snake_case  ,      default=''''''							)
   parser.add_argument('''--length'''  ,      type=_snake_case  ,      default=20							)
   parser.add_argument('''--stop_token'''  ,      type=_snake_case  ,      default=_snake_case  ,      help='''Token at which text generation is stopped'''							)
   parser.add_argument(
       '''--temperature'''  ,      type=_snake_case  ,      default=1.0  ,      help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''  ,      )
   parser.add_argument(
       '''--repetition_penalty'''  ,      type=_snake_case  ,      default=1.0  ,      help='''primarily useful for CTRL model; in that case, use 1.2'''							)
   parser.add_argument('''--k'''  ,      type=_snake_case  ,      default=0							)
   parser.add_argument('''--penalty_alpha'''  ,      type=_snake_case  ,      default=0.0							)
   parser.add_argument('''--p'''  ,      type=_snake_case  ,      default=0.9							)

   parser.add_argument('''--prefix'''  ,      type=_snake_case  ,      default=''''''  ,      help='''Text added prior to input.'''							)
   parser.add_argument('''--padding_text'''  ,      type=_snake_case  ,      default=''''''  ,      help='''Deprecated, the use of `--prefix` is preferred.'''							)
   parser.add_argument('''--xlm_language'''  ,      type=_snake_case  ,      default=''''''  ,      help='''Optional language when used with the XLM model.'''							)

   parser.add_argument('''--seed'''  ,      type=_snake_case  ,      default=42  ,      help='''random seed for initialization'''							)
   parser.add_argument('''--no_cuda'''  ,      action='''store_true'''  ,      help='''Avoid using CUDA when available'''							)
   parser.add_argument(
       '''--fp16'''  ,      action='''store_true'''  ,      help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''  ,      )
   _A						     =					parser.parse_args()

   _A						     =					torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
   _A						     =					0 if args.no_cuda else torch.cuda.device_count()

   logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''							)

   set_seed(_snake_case							)

   # Initialize the model and tokenizer
   _A						     =					AutoTokenizer.from_pretrained(args.model_name_or_path							)
   _A						     =					AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

   # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
   # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
   model.to(args.device							)

   if args.fpaa:
      model.half()

   logger.info(_snake_case							)
   _A						     =					args.prompt if args.prompt else input('''Model prompt >>> '''							)

   _A						     =					tokenizer(_snake_case  ,      return_tensors='''pt'''  ,      add_special_tokens=_snake_case							)
   _A						     =					{key: value.to(args.device							) for key, value in inputs.items()}

   _A						     =					model.generate(
       **_snake_case  ,      max_length=args.length + len(inputs['''input_ids'''][0]							)  ,      penalty_alpha=args.penalty_alpha  ,      top_k=args.k  ,      )

   _A						     =					[]
   for generated_sequence_idx, generated_sequence in enumerate(_snake_case							):
      print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''							)
      _A						     =					generated_sequence.tolist()

      # Decode text
      _A						     =					tokenizer.decode(_snake_case  ,      clean_up_tokenization_spaces=_snake_case  ,      add_special_tokens=_snake_case							)

      # Remove all text after the stop token
      _A						     =					text[: text.find(args.stop_token							) if args.stop_token else None]

      # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
      _A						     =					(
          prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0]  ,      clean_up_tokenization_spaces=_snake_case							)							) :]
      )

      generated_sequences.append(_snake_case							)
      print(_snake_case							)

   return generated_sequences


if __name__ == "__main__":
   main()








import numpy as np
import requests

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int  ,      _snake_case  :Dict  ,      _snake_case  :Tuple  ,      _snake_case  :Tuple  ,      _snake_case  :str							)     ->    int:
   _A						     =					len_data

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

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Any  ,      _snake_case  :Optional[Any]  ,      _snake_case  :List[Any]							)     ->    Tuple:
   _A						     =					np.dot(_snake_case  ,      data_x.transpose()							)
   prod -= data_y.transpose()
   _A						     =					np.sum(np.square(_snake_case							)							)
   _A						     =					sum_elem / (2 * len_data)
   return error

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Union[str, Any]							)     ->    List[str]:
   _A						     =					100_000
   _A						     =					0.000_1550

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

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

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

   return theta

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[int]							)     ->    Tuple:
   _A						     =					sum(abs(y - predicted_y[i]							) for i, y in enumerate(_snake_case							)							)
   return total / len(_snake_case							)

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   _A						     =					collect_dataset()

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

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


if __name__ == "__main__":
   main()



