

"""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()



"""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)

