






"""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:
 _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:
 _snake_case					   =     None
 _snake_case					   =     None
 _snake_case					   =     None

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    _a						: Union[str, Any]    =			unittest.skip("""test requires Datasets, Faiss, psutil"""       )(UpperCamelCase__       )
  return test_case






@require_distributed_retrieval
class  UpperCamelCase    (			snake_case_   ):


 def _lowercase			(  self	:		Tuple							)      -> Optional[Any]:
   _a						: str    =			tempfile.mkdtemp()
   _a						: Dict    =			8

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

   _a						: Any    =			os.path.join(self.tmpdirname							,		"""bart_tokenizer"""							)
   os.makedirs(UpperCAmelCase__							,		exist_ok=UpperCAmelCase__							)
   _a						: Union[str, Any]    =			os.path.join(UpperCAmelCase__							,		BART_VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: Any    =			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 _lowercase			(  self	:		int							)      -> DPRQuestionEncoderTokenizer:
   return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		"""dpr_tokenizer"""							)							)


 def _lowercase			(  self	:		Optional[int]							)      -> BartTokenizer:
   return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		"""bart_tokenizer"""							)							)


 def _lowercase			(  self	:		Union[str, Any]							)      -> Union[str, Any]:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase			(  self	:		int							)      -> Optional[Any]:
   _a						: int    =			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	:		List[str]							,		UpperCAmelCase__	:		bool							,		UpperCAmelCase__	:		List[str]=12345							)      -> RagPyTorchDistributedRetriever:
   _a						: Union[str, Any]    =			self.get_dummy_dataset()
   _a						: Tuple    =			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:
     _a						: Dict    =			dataset
     _a						: List[str]    =			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 _lowercase			(  self	:		Optional[Any]							,		UpperCAmelCase__	:		bool							)      -> RagRayDistributedRetriever:
   # Have to run in local mode because sys.path modifications at top of
   # file are not propogated to remote workers.
   # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
   ray.init(local_mode=UpperCAmelCase__							)
   _a						: Dict    =			RagConfig(
       retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		)
   _a						: Optional[int]    =			ray.remote(UpperCAmelCase__							)
   _a						: Any    =			[remote_cls.remote() for _ in range(1							)]
   with patch("""transformers.models.rag.retrieval_rag.load_dataset"""							) as mock_load_dataset:
     _a						: Optional[int]    =			self.get_dummy_dataset()
     _a						: str    =			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 _lowercase			(  self	:		Tuple							,		UpperCAmelCase__	:		bool							,		UpperCAmelCase__	:		bool							,		UpperCAmelCase__	:		int=12345							)      -> Tuple:
   _a						: Any    =			self.get_dummy_dataset()
   _a						: Dict    =			RagConfig(
       retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name="""custom"""							,		)
   if from_disk:
     _a						: List[Any]    =			os.path.join(self.tmpdirname							,		"""dataset"""							)
     _a						: str    =			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
     _a						: Dict    =			RagPyTorchDistributedRetriever(
         UpperCAmelCase__							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
   else:
     _a						: Tuple    =			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 _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		bool							,		UpperCAmelCase__	:		bool							)      -> List[Any]:
   # Have to run in local mode because sys.path modifications at top of
   # file are not propogated to remote workers.
   # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
   ray.init(local_mode=UpperCAmelCase__							)
   _a						: str    =			self.get_dummy_dataset()
   _a						: Tuple    =			RagConfig(
       retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name="""custom"""							,		)
   _a						: Optional[int]    =			ray.remote(UpperCAmelCase__							)
   _a						: Optional[int]    =			[remote_cls.remote() for _ in range(1							)]
   if from_disk:
     _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		"""dataset"""							)
     _a						: Tuple    =			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
     _a						: List[Any]    =			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:
     _a						: int    =			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 _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		RagRetriever							,		UpperCAmelCase__	:		np.array							,		UpperCAmelCase__	:		int							)      -> None:
   _a   ,						_a   ,						_a						: List[Any]    =			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 _lowercase			(  self	:		Optional[Any]							)      -> str:
   _a						: List[str]    =			1
   _a						: Optional[Any]    =			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 _lowercase			(  self	:		Any							)      -> Dict:
   _a						: List[Any]    =			1
   _a						: Optional[int]    =			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 _lowercase			(  self	:		int							)      -> int:
   _a						: List[str]    =			1
   _a						: List[str]    =			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 _lowercase			(  self	:		List[Any]							)      -> List[Any]:
   _a						: str    =			1
   _a						: Optional[Any]    =			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 _lowercase			(  self	:		int							)      -> List[Any]:
   _a						: List[str]    =			1
   _a						: int    =			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 _lowercase			(  self	:		str							)      -> Tuple:
   _a						: Dict    =			1
   _a						: List[Any]    =			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 os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Union[str, Any]    =      BertweetTokenizer
 UpperCamelCase      : Optional[Any]    =      False


 def _lowercase			(  self	:		Dict							)      -> List[Any]:
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _a						: Tuple    =			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
   _a						: str    =			dict(zip(UpperCAmelCase__							,		range(len(UpperCAmelCase__							)							)							)							)
   _a						: Dict    =			["""#version: 0.2""", """a m</w>"""]
   _a						: int    =			{"""unk_token""": """<unk>"""}

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: List[Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""merges_file"""]							)
   with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     for token in vocab_tokens:
       fp.write(f"""{token} {vocab_tokens[token]}\n"""							)
   with open(self.merges_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write("""\n""".join(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Any							,		**UpperCAmelCase__	:		Optional[Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return BertweetTokenizer.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   _a						: List[str]    =			"""I am VinAI Research"""
   _a						: Optional[int]    =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
   return input_text, output_text







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   _a						: Optional[int]    =			BertweetTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map							)
   _a						: Tuple    =			"""I am VinAI Research"""
   _a						: Dict    =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
   _a						: str    =			tokenizer.tokenize(UpperCAmelCase__							)
   self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a						: Tuple    =			tokens + [tokenizer.unk_token]

   _a						: Tuple    =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__							)							,		UpperCAmelCase__							)

