code: 
  
  """simple docstring"""
  
  
  
  
  
  
  
  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:
    SCREAMING_SNAKE_CASE_     				=   None
  
  if is_ray_available():
    import ray  # noqa: E402 # isort:skip
    from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
  else:
    SCREAMING_SNAKE_CASE_     				=   None
    SCREAMING_SNAKE_CASE_     				=   None
    SCREAMING_SNAKE_CASE_     				=   None
  def 			__UpperCAmelCase		(		a_			:		Optional[Any]				)     ->       int:
     if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        _UpperCAmelCase   	=					unittest.skip("test requires Datasets, Faiss, psutil"				)(snake_case_				)
     return test_case
  
  
  
  
  
  
  @require_distributed_retrieval
  class     __a			(					UpperCamelCase_			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					tempfile.mkdtemp()
            _UpperCAmelCase   	=					8
  
            # DPR tok
            _UpperCAmelCase   	=					[
                '''[UNK]''',
                '''[CLS]''',
                '''[SEP]''',
                '''[PAD]''',
                '''[MASK]''',
                '''want''',
                '''##want''',
                '''##ed''',
                '''wa''',
                '''un''',
                '''runn''',
                '''##ing''',
                ''',''',
                '''low''',
                '''lowest''',
            ]
            _UpperCAmelCase   	=					os.path.join(self.tmpdirname							,"dpr_tokenizer"		)
            os.makedirs(__UpperCamelCase							,exist_ok=__UpperCamelCase		)
            _UpperCAmelCase   	=					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
            _UpperCAmelCase   	=					[
                '''l''',
                '''o''',
                '''w''',
                '''e''',
                '''r''',
                '''s''',
                '''t''',
                '''i''',
                '''d''',
                '''n''',
                '''\u0120''',
                '''\u0120l''',
                '''\u0120n''',
                '''\u0120lo''',
                '''\u0120low''',
                '''er''',
                '''\u0120lowest''',
                '''\u0120newer''',
                '''\u0120wider''',
                '''<unk>''',
            ]
            _UpperCAmelCase   	=					dict(zip(__UpperCamelCase							,range(len(__UpperCamelCase		)		)		)		)
            _UpperCAmelCase   	=					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
            _UpperCAmelCase   	=					{'''unk_token''': '''<unk>'''}
  
            _UpperCAmelCase   	=					os.path.join(self.tmpdirname							,"bart_tokenizer"		)
            os.makedirs(__UpperCamelCase							,exist_ok=__UpperCamelCase		)
            _UpperCAmelCase   	=					os.path.join(__UpperCamelCase							,BART_VOCAB_FILES_NAMES["vocab_file"]		)
            _UpperCAmelCase   	=					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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,"dpr_tokenizer"		)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,"bart_tokenizer"		)		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            shutil.rmtree(self.tmpdirname		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					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    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_=1_2345		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.get_dummy_dataset()
            _UpperCAmelCase   	=					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:
               _UpperCAmelCase   	=					dataset
               _UpperCAmelCase   	=					RagPyTorchDistributedRetriever(
                   __UpperCamelCase							,question_encoder_tokenizer=self.get_dpr_tokenizer()							,generator_tokenizer=self.get_bart_tokenizer()							,)
               if init_retrieval:
                  retriever.init_retrieval(__UpperCamelCase		)
            return retriever
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            ray.init(local_mode=__UpperCamelCase		)
            _UpperCAmelCase   	=					RagConfig(
                retrieval_vector_size=self.retrieval_vector_size							,question_encoder=DPRConfig().to_dict()							,generator=BartConfig().to_dict()							,)
            _UpperCAmelCase   	=					ray.remote(__UpperCamelCase		)
            _UpperCAmelCase   	=					[remote_cls.remote() for _ in range(1		)]
            with patch("transformers.models.rag.retrieval_rag.load_dataset"		) as mock_load_dataset:
               _UpperCAmelCase   	=					self.get_dummy_dataset()
               _UpperCAmelCase   	=					RagRayDistributedRetriever(
                   __UpperCamelCase							,question_encoder_tokenizer=self.get_dpr_tokenizer()							,generator_tokenizer=self.get_bart_tokenizer()							,retrieval_workers=__UpperCamelCase							,)
               if init_retrieval:
                  retriever.init_retrieval()
            return retriever
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=1_2345		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.get_dummy_dataset()
            _UpperCAmelCase   	=					RagConfig(
                retrieval_vector_size=self.retrieval_vector_size							,question_encoder=DPRConfig().to_dict()							,generator=BartConfig().to_dict()							,index_name="custom"							,)
            if from_disk:
               _UpperCAmelCase   	=					os.path.join(self.tmpdirname							,"dataset"		)
               _UpperCAmelCase   	=					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
               _UpperCAmelCase   	=					RagPyTorchDistributedRetriever(
                   __UpperCamelCase							,question_encoder_tokenizer=self.get_dpr_tokenizer()							,generator_tokenizer=self.get_bart_tokenizer()							,)
            else:
               _UpperCAmelCase   	=					RagPyTorchDistributedRetriever(
                   __UpperCamelCase							,question_encoder_tokenizer=self.get_dpr_tokenizer()							,generator_tokenizer=self.get_bart_tokenizer()							,index=CustomHFIndex(config.retrieval_vector_size							,__UpperCamelCase		)							,)
            if init_retrieval:
               retriever.init_retrieval(__UpperCamelCase		)
            return retriever
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            ray.init(local_mode=__UpperCamelCase		)
            _UpperCAmelCase   	=					self.get_dummy_dataset()
            _UpperCAmelCase   	=					RagConfig(
                retrieval_vector_size=self.retrieval_vector_size							,question_encoder=DPRConfig().to_dict()							,generator=BartConfig().to_dict()							,index_name="custom"							,)
            _UpperCAmelCase   	=					ray.remote(__UpperCamelCase		)
            _UpperCAmelCase   	=					[remote_cls.remote() for _ in range(1		)]
            if from_disk:
               _UpperCAmelCase   	=					os.path.join(self.tmpdirname							,"dataset"		)
               _UpperCAmelCase   	=					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
               _UpperCAmelCase   	=					RagRayDistributedRetriever(
                   __UpperCamelCase							,question_encoder_tokenizer=self.get_dpr_tokenizer()							,generator_tokenizer=self.get_bart_tokenizer()							,retrieval_workers=__UpperCamelCase							,index=CustomHFIndex.load_from_disk(
                       vector_size=config.retrieval_vector_size							,dataset_path=config.passages_path							,index_path=config.index_path							,)							,)
            else:
               _UpperCAmelCase   	=					RagRayDistributedRetriever(
                   __UpperCamelCase							,question_encoder_tokenizer=self.get_dpr_tokenizer()							,generator_tokenizer=self.get_bart_tokenizer()							,retrieval_workers=__UpperCamelCase							,index=CustomHFIndex(config.retrieval_vector_size							,__UpperCamelCase		)							,)
            if init_retrieval:
               retriever.init_retrieval()
            return retriever
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					retriever.retrieve(__UpperCamelCase							,n_docs=__UpperCamelCase		)
            self.assertEqual(retrieved_doc_embeds.shape							,(2, n_docs, self.retrieval_vector_size)		)
            self.assertEqual(len(__UpperCamelCase		)							,2		)
            self.assertEqual(sorted(doc_dicts[0]		)							,["embeddings", "id", "text", "title"]		)
            self.assertEqual(len(doc_dicts[0]["id"]		)							,__UpperCamelCase		)
            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    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					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=__UpperCamelCase		)							,__UpperCamelCase							,__UpperCamelCase		)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					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=__UpperCamelCase							,from_disk=__UpperCamelCase		)							,__UpperCamelCase							,__UpperCamelCase							,)
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					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=__UpperCamelCase							,from_disk=__UpperCamelCase		)							,__UpperCamelCase							,__UpperCamelCase							,)
  
  
  
  
  
  
  
         @require_ray
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					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=__UpperCamelCase		)							,__UpperCamelCase							,__UpperCamelCase		)
            ray.shutdown()
  
  
  
  
  
  
  
         @require_ray
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					np.array(
                [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]							,dtype=np.floataa		)
            with self.assertRaises(__UpperCamelCase		):
               self.distributed_retriever_check(
                   self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__UpperCamelCase							,from_disk=__UpperCamelCase		)							,__UpperCamelCase							,__UpperCamelCase							,)
            ray.shutdown()
  
  
  
  
  
  
  
         @require_ray
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					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=__UpperCamelCase							,from_disk=__UpperCamelCase		)							,__UpperCamelCase							,__UpperCamelCase		)
            ray.shutdown()
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import numpy as np
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_=5							,lowerCamelCase_=5		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					depth
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					min_leaf_size
            _UpperCAmelCase   	=					None
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if labels.ndim != 1:
               print("Error: Input labels must be one dimensional"		)
  
            return np.mean((labels - prediction) ** 2		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if x.ndim != 1:
               print("Error: Input data set must be one dimensional"		)
               return
            if len(lowerCamelCase_		) != len(lowerCamelCase_		):
               print("Error: X and y have different lengths"		)
               return
            if y.ndim != 1:
               print("Error: Data set labels must be one dimensional"		)
               return
  
            if len(lowerCamelCase_		) < 2 * self.min_leaf_size:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            if self.depth == 1:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					self.mean_squared_error(lowerCamelCase_							,np.mean(lowerCamelCase_		)		) * 2
            for i in range(len(lowerCamelCase_		)		):
               if len(x[:i]		) < self.min_leaf_size:
                  continue
               elif len(x[i:]		) < self.min_leaf_size:
                  continue
               else:
                  _UpperCAmelCase   	=					self.mean_squared_error(x[:i]							,np.mean(y[:i]		)		)
                  _UpperCAmelCase   	=					self.mean_squared_error(x[i:]							,np.mean(y[i:]		)		)
                  _UpperCAmelCase   	=					error_left + error_right
                  if error < min_error:
                     _UpperCAmelCase   	=					i
                     _UpperCAmelCase   	=					error
  
            if best_split != 0:
               _UpperCAmelCase   	=					x[:best_split]
               _UpperCAmelCase   	=					y[:best_split]
               _UpperCAmelCase   	=					x[best_split:]
               _UpperCAmelCase   	=					y[best_split:]
  
               _UpperCAmelCase   	=					x[best_split]
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               self.left.train(lowerCamelCase_							,lowerCamelCase_		)
               self.right.train(lowerCamelCase_							,lowerCamelCase_		)
            else:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
  
            return
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if self.prediction is not None:
               return self.prediction
            elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                  return self.right.predict(lowerCamelCase_		)
               else:
                  return self.left.predict(lowerCamelCase_		)
            else:
               print("Error: Decision tree not yet trained"		)
               return None
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					float(0		)
            for label in labels:
               squared_error_sum += (label - prediction) ** 2
  
            return float(squared_error_sum / labels.size		)
  
  
  
  def 			__UpperCAmelCase		(		)     ->       Dict:
     _UpperCAmelCase   	=					np.arange(-1.0    ,  1.0    ,  0.005				)
     _UpperCAmelCase   	=					np.sin(a_				)
  
     _UpperCAmelCase   	=					DecisionTree(depth=10    ,  min_leaf_size=10				)
     tree.train(a_    ,  a_				)
  
     _UpperCAmelCase   	=					(np.random.rand(10				) * 2) - 1
     _UpperCAmelCase   	=					np.array([tree.predict(a_				) for x in test_cases]				)
     _UpperCAmelCase   	=					np.mean((predictions - test_cases) ** 2				)
  
     print("Test values: " + str(a_				)				)
     print("Predictions: " + str(a_				)				)
     print("Average error: " + str(a_				)				)
  
  
  if __name__ == "__main__":
    main()
    import doctest
  
    doctest.testmod(name='mean_squarred_error', verbose=True)
  
  