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:
   lowerCamelCase__   = None

if is_ray_available():
   import ray  # noqa: E402 # isort:skip
   from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
   lowerCamelCase__   = None
   lowerCamelCase__   = None
   lowerCamelCase__   = None


def 							lowerCAmelCase__    (       a__      )	->List[str]:




  '''simple docstring'''

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







@require_distributed_retrieval
class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 def 						__UpperCAmelCase							(						self							:					str)		->     List[Any]:


   """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(lowercase_				,      exist_ok=lowercase_)
   _UpperCamelCase				=  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
   _UpperCamelCase				=  [
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   _UpperCamelCase				=  dict(zip(lowercase_				,      range(len(lowercase_))))
   _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(lowercase_				,      exist_ok=lowercase_)
   _UpperCamelCase				=  os.path.join(lowercase_				,      BART_VOCAB_FILES_NAMES["vocab_file"])
   _UpperCamelCase				=  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 						__UpperCAmelCase							(						self							:					Optional[int])		->     DPRQuestionEncoderTokenizer:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     BartTokenizer:


   """simple docstring"""







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







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Dict:


   """simple docstring"""







   shutil.rmtree(self.tmpdirname)







 def 						__UpperCAmelCase							(						self							:					Dict)		->     List[str]:


   """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 						__UpperCAmelCase							(						self							:					List[Any]				,      lowercase_							:					bool				,      lowercase_							:					Tuple=12345)		->     RagPyTorchDistributedRetriever:


   """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(
         lowercase_				,      question_encoder_tokenizer=self.get_dpr_tokenizer()				,      generator_tokenizer=self.get_bart_tokenizer()				,      )
     if init_retrieval:
       retriever.init_retrieval(lowercase_)
   return retriever







 def 						__UpperCAmelCase							(						self							:					Any				,      lowercase_							:					bool)		->     RagRayDistributedRetriever:


   """simple docstring"""







   ray.init(local_mode=lowercase_)
   _UpperCamelCase				=  RagConfig(
       retrieval_vector_size=self.retrieval_vector_size				,      question_encoder=DPRConfig().to_dict()				,      generator=BartConfig().to_dict()				,      )
   _UpperCamelCase				=  ray.remote(lowercase_)
   _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(
         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 						__UpperCAmelCase							(						self							:					Union[str, Any]				,      lowercase_							:					bool				,      lowercase_							:					bool				,      lowercase_							:					List[Any]=12345)		->     Optional[int]:


   """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(
         lowercase_				,      question_encoder_tokenizer=self.get_dpr_tokenizer()				,      generator_tokenizer=self.get_bart_tokenizer()				,      )
   else:
     _UpperCamelCase				=  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 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					bool				,      lowercase_							:					bool)		->     List[Any]:


   """simple docstring"""







   ray.init(local_mode=lowercase_)
   _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(lowercase_)
   _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(
         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:
     _UpperCamelCase				=  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 						__UpperCAmelCase							(						self							:					str				,      lowercase_							:					RagRetriever				,      lowercase_							:					np.array				,      lowercase_							:					int)		->     None:


   """simple docstring"""







   _UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase				=  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 						__UpperCAmelCase							(						self							:					Tuple)		->     Optional[int]:


   """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=lowercase_)				,      lowercase_				,      lowercase_)







 def 						__UpperCAmelCase							(						self							:					List[str])		->     Dict:


   """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=lowercase_				,      from_disk=lowercase_)				,      lowercase_				,      lowercase_				,      )







 def 						__UpperCAmelCase							(						self							:					Dict)		->     Tuple:


   """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=lowercase_				,      from_disk=lowercase_)				,      lowercase_				,      lowercase_				,      )







 @require_ray
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     int:


   """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=lowercase_)				,      lowercase_				,      lowercase_)
   ray.shutdown()







 @require_ray
 def 						__UpperCAmelCase							(						self							:					Any)		->     List[str]:


   """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(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 						__UpperCAmelCase							(						self							:					int)		->     Union[str, Any]:


   """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=lowercase_				,      from_disk=lowercase_)				,      lowercase_				,      lowercase_)
   ray.shutdown()





from itertools import combinations, product


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__      )	->bool:




  '''simple docstring'''

  if xa == ya == 0 or xa == ya == 0:
    return False
  _UpperCamelCase				=  xa * xa + ya * ya
  _UpperCamelCase				=  xa * xa + ya * ya
  _UpperCamelCase				=  (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
  return (
      a_square + b_square == c_square
      or a_square + c_square == b_square
      or b_square + c_square == a_square
  )


def 							lowerCAmelCase__    (       a__ = 50      )	->int:




  '''simple docstring'''

  return sum(
      1
      for pta, pta in combinations(product(range(limit + 1      )  ,			repeat=2      )  ,			2      )
      if is_right(*a__  ,			*a__      )      )


if __name__ == "__main__":
   print(F"{solution() = }")




