






"""simple docstring"""





import math







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   Optional[int]:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     x
          lowercase__:							str         =     y
          lowercase__:							Optional[Any]         =     step_size
          lowercase__:							Union[str, Any]         =     function_to_optimize





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							str         =     self.step_size
          return [
              SearchProblem(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, 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)			->   Optional[int]:




          '''simple docstring'''




          return hash(str(self))





   def __eq__(       self					, UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




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





   def __str__(       self)			->   Dict:




          '''simple docstring'''




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







def 							A(    snake_case_		,  snake_case_ = True		,  snake_case_ = math.inf		,  snake_case_ = -math.inf		,  snake_case_ = math.inf		,  snake_case_ = -math.inf		,  snake_case_ = False		,  snake_case_ = 10000		,  ):
       """simple docstring"""





       lowercase__:							Dict         =     search_prob
       lowercase__:							Dict         =     []  # list to store the current score at each iteration
       lowercase__:							Union[str, Any]         =     0
       lowercase__:							Union[str, Any]         =     False
       lowercase__:							Union[str, Any]         =     set()
       while not solution_found and iterations < max_iter:
              visited.add(snake_case_						)
              iterations += 1
              lowercase__:							Tuple         =     current_state.score()
              scores.append(snake_case_						)
              lowercase__:							List[str]         =     current_state.get_neighbors()
              lowercase__:							Dict         =     -math.inf
              lowercase__:							List[Any]         =     math.inf
              lowercase__:							Dict         =     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
                     lowercase__:							Tuple         =     neighbor.score() - current_score
                     if find_max:  # finding max
                            # going to direction with greatest ascent
                            if change > max_change and change > 0:
                                   lowercase__:							str         =     change
                                   lowercase__:							List[str]         =     neighbor
                     else:  # finding min
                            # to direction with greatest descent
                            if change < min_change and change < 0:
                                   lowercase__:							Optional[int]         =     change
                                   lowercase__:							Tuple         =     neighbor
              if next_state is not None:
                     # we found at least one neighbor which improved the current state
                     lowercase__:							int         =     next_state
              else:
                     # since we have no neighbor that improves the solution we stop the search
                     lowercase__:							str         =     True

       if visualization:
              from matplotlib import pyplot as plt

              plt.plot(range(snake_case_						)		,  snake_case_						)
              plt.xlabel("Iterations"						)
              plt.ylabel("Function values"						)
              plt.show()

       return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()






  def 							A(    snake_case_		,  snake_case_						):
         """simple docstring"""





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

  # starting the problem with initial coordinates (3, 4)
  UpperCamelCase					=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  UpperCamelCase					=     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)
  UpperCamelCase					=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  UpperCamelCase					=     hill_climbing(
      prob, find_max=False, max_x=1_00, 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 							A(    snake_case_		,  snake_case_						):
         """simple docstring"""





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

  UpperCamelCase					=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  UpperCamelCase					=     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()}"
  )












import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
	from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
	__A						:		str          =						None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	__A						:		int          =						None
	__A						:		Tuple          =						None
	__A						:		Union[str, Any]          =						None





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								__lowerCAmelCase					=  unittest.skip("""test requires Datasets, Faiss, psutil"""   )(UpperCamelCase__   )
				return test_case

@require_distributed_retrieval
class 		lowercase_       (	lowerCAmelCase__				):




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  tempfile.mkdtemp()
									__lowerCAmelCase					=  8

									# DPR tok
									__lowerCAmelCase					=  [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dpr_tokenizer""")
									os.makedirs(_lowercase,     exist_ok=_lowercase)
									__lowerCAmelCase					=  os.path.join(_lowercase,     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					=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									__lowerCAmelCase					=  dict(zip(_lowercase,     range(len(_lowercase))))
									__lowerCAmelCase					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									__lowerCAmelCase					=  {"""unk_token""": """<unk>"""}

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




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,     """dpr_tokenizer"""))




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,     """bart_tokenizer"""))




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									shutil.rmtree(self.tmpdirname)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  Dataset.from_dict(
									    {
									        """id""": ["""0""", """1"""],
									        """text""": ["""foo""", """bar"""],
									        """title""": ["""Foo""", """Bar"""],
									        """embeddings""": [np.ones(self.retrieval_vector_size), 2 * np.ones(self.retrieval_vector_size)],
									    })
									dataset.add_faiss_index("""embeddings""",     string_factory="""Flat""",     metric_type=faiss.METRIC_INNER_PRODUCT)
									return dataset




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		bool,     _lowercase:		Any=12345):


									'''simple docstring'''


									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     )
									with patch("""transformers.models.rag.retrieval_rag.load_dataset""") as mock_load_dataset:
													__lowerCAmelCase					=  dataset
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     )
													if init_retrieval:
																	retriever.init_retrieval(_lowercase)
									return retriever




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		bool):


									'''simple docstring'''


									ray.init(local_mode=_lowercase)
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     )
									__lowerCAmelCase					=  ray.remote(_lowercase)
									__lowerCAmelCase					=  [remote_cls.remote() for _ in range(1)]
									with patch("""transformers.models.rag.retrieval_rag.load_dataset""") as mock_load_dataset:
													__lowerCAmelCase					=  self.get_dummy_dataset()
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     )
													if init_retrieval:
																	retriever.init_retrieval()
									return retriever




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		bool,     _lowercase:		bool,     _lowercase:		int=12345):


									'''simple docstring'''


									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     index_name="""custom""",     )
									if from_disk:
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dataset""")
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """index.faiss""")
													dataset.get_index("""embeddings""").save(os.path.join(self.tmpdirname,     """index.faiss"""))
													dataset.drop_index("""embeddings""")
													dataset.save_to_disk(os.path.join(self.tmpdirname,     """dataset"""))
													del dataset
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     )
									else:
													__lowerCAmelCase					=  RagPyTorchDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     index=CustomHFIndex(config.retrieval_vector_size,     _lowercase),     )
									if init_retrieval:
													retriever.init_retrieval(_lowercase)
									return retriever




					def     _lowercase						(   self:		List[Any],     _lowercase:		bool,     _lowercase:		bool):


									'''simple docstring'''


									ray.init(local_mode=_lowercase)
									__lowerCAmelCase					=  self.get_dummy_dataset()
									__lowerCAmelCase					=  RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size,     question_encoder=DPRConfig().to_dict(),     generator=BartConfig().to_dict(),     index_name="""custom""",     )
									__lowerCAmelCase					=  ray.remote(_lowercase)
									__lowerCAmelCase					=  [remote_cls.remote() for _ in range(1)]
									if from_disk:
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """dataset""")
													__lowerCAmelCase					=  os.path.join(self.tmpdirname,     """index.faiss""")
													dataset.get_index("""embeddings""").save(os.path.join(self.tmpdirname,     """index.faiss"""))
													dataset.drop_index("""embeddings""")
													dataset.save_to_disk(os.path.join(self.tmpdirname,     """dataset"""))
													del dataset
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     index=CustomHFIndex.load_from_disk(
													        vector_size=config.retrieval_vector_size,     dataset_path=config.passages_path,     index_path=config.index_path,     ),     )
									else:
													__lowerCAmelCase					=  RagRayDistributedRetriever(
													    _lowercase,     question_encoder_tokenizer=self.get_dpr_tokenizer(),     generator_tokenizer=self.get_bart_tokenizer(),     retrieval_workers=_lowercase,     index=CustomHFIndex(config.retrieval_vector_size,     _lowercase),     )
									if init_retrieval:
													retriever.init_retrieval()
									return retriever




					def     _lowercase						(   self:		int,     _lowercase:		RagRetriever,     _lowercase:		np.array,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  retriever.retrieve(_lowercase,     n_docs=_lowercase)
									self.assertEqual(retrieved_doc_embeds.shape,     (2, n_docs, self.retrieval_vector_size))
									self.assertEqual(len(_lowercase),     2)
									self.assertEqual(sorted(doc_dicts[0]),     ["""embeddings""", """id""", """text""", """title"""])
									self.assertEqual(len(doc_dicts[0]["""id"""]),     _lowercase)
									self.assertEqual(doc_dicts[0]["""id"""][0],     """1""")  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]["""id"""][0],     """0""")  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist(),     [[1], [0]])




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowercase),     _lowercase,     _lowercase)




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )




					@require_ray
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_ray_distributed_retriever(init_retrieval=_lowercase),     _lowercase,     _lowercase)
									ray.shutdown()




					@require_ray
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)
									with self.assertRaises(_lowercase):
													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase,     )
									ray.shutdown()







					@require_ray
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  1
									__lowerCAmelCase					=  np.array(
									    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)],     dtype=np.floataa)

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowercase,     from_disk=_lowercase),     _lowercase,     _lowercase)
									ray.shutdown()





