



'''simple docstring'''





import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase = True							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = False							,   UpperCamelCase = 100							,   UpperCamelCase = 0.01							,   UpperCamelCase = 1							,   ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= False
    lowerCAmelCase__			:  Optional[Any]											= search_prob
    lowerCAmelCase__			:  Tuple											= start_temperate
    lowerCAmelCase__			:  Optional[Any]											= []
    lowerCAmelCase__			:  str											= 0
    lowerCAmelCase__			:  Tuple											= None

    while not search_end:
        lowerCAmelCase__			:  List[str]											= current_state.score()
        if best_state is None or current_score > best_state.score():
            lowerCAmelCase__			:  List[str]											= current_state
        scores.append(UpperCamelCase      )
        iterations += 1
        lowerCAmelCase__			:  Dict											= None
        lowerCAmelCase__			:  List[Any]											= current_state.get_neighbors()
        while (
            next_state is None and neighbors
        ):  # till we do not find a neighbor that we can move to
            lowerCAmelCase__			:  Tuple											= random.randint(0							,   len(UpperCamelCase      ) - 1      )  # picking a random neighbor
            lowerCAmelCase__			:  str											= neighbors.pop(UpperCamelCase      )
            lowerCAmelCase__			:  Optional[Any]											= picked_neighbor.score() - current_score

            if (
                picked_neighbor.x > max_x
                or picked_neighbor.x < min_x
                or picked_neighbor.y > max_y
                or picked_neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds

            if not find_max:
                lowerCAmelCase__			:  Union[str, Any]											= change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                lowerCAmelCase__			:  Tuple											= picked_neighbor
            else:
                lowerCAmelCase__			:  List[Any]											= (math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    lowerCAmelCase__			:  List[Any]											= picked_neighbor
        lowerCAmelCase__			:  Optional[int]											= current_temp - (current_temp * rate_of_decrease)

        if current_temp < threshold_temp or next_state is None:
            # temperature below threshold, or could not find a suitable neighbor
            lowerCAmelCase__			:  str											= True
        else:
            lowerCAmelCase__			:  Optional[int]											= next_state

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(UpperCamelCase      )							,   UpperCamelCase      )
        plt.xlabel("""Iterations"""      )
        plt.ylabel("""Function values"""      )
        plt.show()
    return best_state


if __name__ == "__main__":
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





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

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     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()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     '''The maximum 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





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

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=False, visualization=True)
 print(
     '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=True, visualization=True)




 print(
     '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )







'''simple docstring'''





import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
 from transformers.models.rag.configuration_rag import RagConfig
 from transformers.models.rag.tokenization_rag import RagTokenizer






@require_faiss
@require_torch
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  str											= tempfile.mkdtemp()
      lowerCAmelCase__			:  List[Any]											= 8

      # DPR tok
      lowerCAmelCase__			:  int											= [
          """[UNK]""",
          """[CLS]""",
          """[SEP]""",
          """[PAD]""",
          """[MASK]""",
          """want""",
          """##want""",
          """##ed""",
          """wa""",
          """un""",
          """runn""",
          """##ing""",
          """,""",
          """low""",
          """lowest""",
      ]
      lowerCAmelCase__			:  List[Any]											= os.path.join(self.tmpdirname  ,"""dpr_tokenizer"""				)
      os.makedirs(__UpperCAmelCase  ,exist_ok=__UpperCAmelCase				)
      lowerCAmelCase__			:  Dict											= os.path.join(__UpperCAmelCase  ,DPR_VOCAB_FILES_NAMES["""vocab_file"""]				)
      with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""				) as vocab_writer:
          vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

      # BART tok
      lowerCAmelCase__			:  str											= [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      lowerCAmelCase__			:  List[Any]											= dict(zip(__UpperCAmelCase  ,range(len(__UpperCAmelCase				)				)				)				)
      lowerCAmelCase__			:  Optional[Any]											= ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      lowerCAmelCase__			:  Any											= {"""unk_token""": """<unk>"""}

      lowerCAmelCase__			:  str											= os.path.join(self.tmpdirname  ,"""bart_tokenizer"""				)
      os.makedirs(__UpperCAmelCase  ,exist_ok=__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= os.path.join(__UpperCAmelCase  ,BART_VOCAB_FILES_NAMES["""vocab_file"""]				)
      lowerCAmelCase__			:  Dict											= os.path.join(__UpperCAmelCase  ,BART_VOCAB_FILES_NAMES["""merges_file"""]				)
      with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""				) as fp:
          fp.write(json.dumps(__UpperCAmelCase				) + """\n"""				)
      with open(self.merges_file  ,"""w"""  ,encoding="""utf-8"""				) as fp:
          fp.write("""\n""".join(__UpperCAmelCase				)				)




  def 							UpperCAmelCase_     (   self				)   ->		DPRQuestionEncoderTokenizer:
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,"""dpr_tokenizer"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		BartTokenizer:
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,"""bart_tokenizer"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      shutil.rmtree(self.tmpdirname				)




  @require_tokenizers
  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Any											= os.path.join(self.tmpdirname  ,"""rag_tokenizer"""				)
      lowerCAmelCase__			:  Any											= RagConfig(question_encoder=DPRConfig().to_dict()  ,generator=BartConfig().to_dict()				)
      lowerCAmelCase__			:  str											= RagTokenizer(question_encoder=self.get_dpr_tokenizer()  ,generator=self.get_bart_tokenizer()				)
      rag_config.save_pretrained(__UpperCAmelCase				)
      rag_tokenizer.save_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= RagTokenizer.from_pretrained(__UpperCAmelCase  ,config=__UpperCAmelCase				)
      self.assertIsInstance(new_rag_tokenizer.question_encoder  ,__UpperCAmelCase				)
      self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()  ,rag_tokenizer.question_encoder.get_vocab()				)
      self.assertIsInstance(new_rag_tokenizer.generator  ,__UpperCAmelCase				)
      self.assertEqual(new_rag_tokenizer.generator.get_vocab()  ,rag_tokenizer.generator.get_vocab()				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[str]											= RagTokenizer.from_pretrained("""facebook/rag-token-nq"""				)
      lowerCAmelCase__			:  Optional[Any]											= [
          """who got the first nobel prize in physics""",
          """when is the next deadpool movie being released""",
          """which mode is used for short wave broadcast service""",
          """who is the owner of reading football club""",
          """when is the next scandal episode coming out""",
          """when is the last time the philadelphia won the superbowl""",
          """what is the most current adobe flash player version""",
          """how many episodes are there in dragon ball z""",
          """what is the first step in the evolution of the eye""",
          """where is gall bladder situated in human body""",
          """what is the main mineral in lithium batteries""",
          """who is the president of usa right now""",
          """where do the greasers live in the outsiders""",
          """panda is a national animal of which country""",
          """what is the name of manchester united stadium""",
      ]
      lowerCAmelCase__			:  Dict											= tokenizer(__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  str											= RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""				)
      lowerCAmelCase__			:  str											= [
          """who got the first nobel prize in physics""",
          """when is the next deadpool movie being released""",
          """which mode is used for short wave broadcast service""",
          """who is the owner of reading football club""",
          """when is the next scandal episode coming out""",
          """when is the last time the philadelphia won the superbowl""",
          """what is the most current adobe flash player version""",
          """how many episodes are there in dragon ball z""",
          """what is the first step in the evolution of the eye""",
          """where is gall bladder situated in human body""",
          """what is the main mineral in lithium batteries""",
          """who is the president of usa right now""",
          """where do the greasers live in the outsiders""",
          """panda is a national animal of which country""",
          """what is the name of manchester united stadium""",
      ]
      lowerCAmelCase__			:  Tuple											= tokenizer(__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)


