

'''simple docstring'''



import math

class 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      def __init__(					self :		int     ,					snake_case :		int     ,					snake_case :		int     ,					snake_case :		int     ,					snake_case :		List[str]   ):



          """simple docstring"""





          _snake_case : List[str]												=		x
          _snake_case : Optional[Any]												=		y
          _snake_case : str												=		step_size
          _snake_case : Optional[Any]												=		function_to_optimize




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          return self.function(self.x     ,					self.y   )




      def 	__UpperCAmelCase			(					self :		List[Any]   ):



          """simple docstring"""





          _snake_case : Dict												=		self.step_size
          return [
              SearchProblem(snake_case     ,					snake_case     ,					snake_case     ,					self.function   )
              for x, y in (
                  (self.x - step_size, self.y - step_size),
                  (self.x - step_size, self.y),
                  (self.x - step_size, self.y + step_size),
                  (self.x, self.y - step_size),
                  (self.x, self.y + step_size),
                  (self.x + step_size, self.y - step_size),
                  (self.x + step_size, self.y),
                  (self.x + step_size, self.y + step_size),
              )
          ]




      def __hash__(					self :		List[str]   ):



          """simple docstring"""





          return hash(str(self   )   )




      def __eq__(					self :		Optional[Any]     ,					snake_case :		Any   ):



          """simple docstring"""





          if isinstance(snake_case     ,					snake_case   ):
              return hash(str(self   )   ) == hash(str(snake_case   )   )
          return False




      def __str__(					self :		Any   ):



          """simple docstring"""





          return F"""x: {self.x} y: {self.y}"""



def   lowerCamelCase__       (      a__			,      a__ = True			,      a__ = math.inf			,      a__ = -math.inf			,      a__ = math.inf			,      a__ = -math.inf			,      a__ = False			,      a__ = 1_0_0_0_0			,      )		->    SearchProblem:



    """simple docstring"""







    _snake_case : Optional[Any]												=		search_prob
    _snake_case : List[Any]												=		[]  # list to store the current score at each iteration
    _snake_case : Dict												=		0
    _snake_case : Optional[int]												=		False
    _snake_case : str												=		set()
    while not solution_found and iterations < max_iter:
        visited.add(a__)
        iterations += 1
        _snake_case : Optional[Any]												=		current_state.score()
        scores.append(a__)
        _snake_case : Optional[Any]												=		current_state.get_neighbors()
        _snake_case : str												=		-math.inf
        _snake_case : Tuple												=		math.inf
        _snake_case : Optional[Any]												=		None  # to hold the next best neighbor
        for neighbor in neighbors:
            if neighbor in visited:
                continue  # do not want to visit the same state again
            if (
                neighbor.x > max_x
                or neighbor.x < min_x
                or neighbor.y > max_y
                or neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds
            _snake_case : Optional[Any]												=		neighbor.score() - current_score
            if find_max:  # finding max
                # going to direction with greatest ascent
                if change > max_change and change > 0:
                    _snake_case : Dict												=		change
                    _snake_case : List[Any]												=		neighbor
            else:  # finding min
                # to direction with greatest descent
                if change < min_change and change < 0:
                    _snake_case : List[Any]												=		change
                    _snake_case : int												=		neighbor
        if next_state is not None:
            # we found at least one neighbor which improved the current state
            _snake_case : Optional[int]												=		next_state
        else:
            # since we have no neighbor that improves the solution we stop the search
            _snake_case : List[str]												=		True

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(a__)			,      a__)
        plt.xlabel('Iterations')
        plt.ylabel('Function values')
        plt.show()

    return current_state


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   def   lowerCamelCase__       (      a__			,      a__)		->    str:



       """simple docstring"""







       return (x**2) + (y**2)

   # starting the problem with initial coordinates (3, 4)
   SCREAMING_SNAKE_CASE_    =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   SCREAMING_SNAKE_CASE_    =   hill_climbing(prob, find_max=False)
   print(
       "The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
       F'''{local_min.score()}'''
   )

   # starting the problem with initial coordinates (12, 47)
   SCREAMING_SNAKE_CASE_    =   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   SCREAMING_SNAKE_CASE_    =   hill_climbing(
       prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
   )
   print(
       "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
       F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
   )

   def   lowerCamelCase__       (      a__			,      a__)		->    int:



       """simple docstring"""







       return (3 * x**2) - (6 * y)

   SCREAMING_SNAKE_CASE_    =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   SCREAMING_SNAKE_CASE_    =   hill_climbing(prob, find_max=True)




   print(
       "The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
       F'''{local_min.score()}'''
   )






'''simple docstring'''



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,
)
SCREAMING_SNAKE_CASE_    =   logging.getLogger(__name__)

def   lowerCamelCase__       (      a__)		->    List[Any]:



    """simple docstring"""







    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

def   lowerCamelCase__       (      )		->    Tuple:



    """simple docstring"""







    _snake_case : Optional[int]												=		argparse.ArgumentParser()
    parser.add_argument(
        '--model_name_or_path'			,      default=a__			,      type=a__			,      required=a__			,      )
    parser.add_argument('--prompt'			,      type=a__			,      default='')
    parser.add_argument('--length'			,      type=a__			,      default=2_0)
    parser.add_argument('--stop_token'			,      type=a__			,      default=a__			,      help='Token at which text generation is stopped')
    parser.add_argument(
        '--temperature'			,      type=a__			,      default=1.0			,      help='temperature of 1.0 has no effect, lower tend toward greedy sampling'			,      )
    parser.add_argument(
        '--repetition_penalty'			,      type=a__			,      default=1.0			,      help='primarily useful for CTRL model; in that case, use 1.2')
    parser.add_argument('--k'			,      type=a__			,      default=0)
    parser.add_argument('--penalty_alpha'			,      type=a__			,      default=0.0)
    parser.add_argument('--p'			,      type=a__			,      default=0.9)

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

    parser.add_argument('--seed'			,      type=a__			,      default=4_2			,      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'			,      )
    _snake_case : Optional[int]												=		parser.parse_args()

    _snake_case : Optional[int]												=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
    _snake_case : int												=		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(a__)

    # Initialize the model and tokenizer
    _snake_case : Dict												=		AutoTokenizer.from_pretrained(args.model_name_or_path)
    _snake_case : List[Any]												=		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(a__)
    _snake_case : Any												=		args.prompt if args.prompt else input('Model prompt >>> ')

    _snake_case : List[str]												=		tokenizer(a__			,      return_tensors='pt'			,      add_special_tokens=a__)
    _snake_case : List[str]												=		{key: value.to(args.device) for key, value in inputs.items()}

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

    _snake_case : int												=		[]
    for generated_sequence_idx, generated_sequence in enumerate(a__):
        print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===""")
        _snake_case : Any												=		generated_sequence.tolist()

        # Decode text
        _snake_case : str												=		tokenizer.decode(a__			,      clean_up_tokenization_spaces=a__			,      add_special_tokens=a__)

        # Remove all text after the stop token
        _snake_case : str												=		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
        _snake_case : Tuple												=		(
            prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]			,      clean_up_tokenization_spaces=a__)) :]
        )

        generated_sequences.append(a__)
        print(a__)

    return generated_sequences


if __name__ == "__main__":
   main()



