


'''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:
				UpperCAmelCase_		:       Optional[int]							       =		None

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







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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







@require_distributed_retrieval
class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				tempfile.mkdtemp()
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				8

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

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






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






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






							def UpperCAmelCase_     (   self  ):
											shutil.rmtree(self.tmpdirname  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				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   , __snake_case   , __snake_case=1_2345  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.get_dummy_dataset()
											_SCREAMING_SNAKE_CASE			:	int							=				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:
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				dataset
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				RagPyTorchDistributedRetriever(
															    __snake_case   , question_encoder_tokenizer=self.get_dpr_tokenizer()   , generator_tokenizer=self.get_bart_tokenizer()   , )
															if init_retrieval:
																			retriever.init_retrieval(__snake_case  )
											return retriever






							def UpperCAmelCase_     (   self   , __snake_case  ):
											# 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=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size   , question_encoder=DPRConfig().to_dict()   , generator=BartConfig().to_dict()   , )
											_SCREAMING_SNAKE_CASE			:	int							=				ray.remote(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Any							=				[remote_cls.remote() for _ in range(1  )]
											with patch("""transformers.models.rag.retrieval_rag.load_dataset"""  ) as mock_load_dataset:
															_SCREAMING_SNAKE_CASE			:	int							=				self.get_dummy_dataset()
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				RagRayDistributedRetriever(
															    __snake_case   , question_encoder_tokenizer=self.get_dpr_tokenizer()   , generator_tokenizer=self.get_bart_tokenizer()   , retrieval_workers=__snake_case   , )
															if init_retrieval:
																			retriever.init_retrieval()
											return retriever






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case=1_2345  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.get_dummy_dataset()
											_SCREAMING_SNAKE_CASE			:	Any							=				RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size   , question_encoder=DPRConfig().to_dict()   , generator=BartConfig().to_dict()   , index_name="""custom"""   , )
											if from_disk:
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				os.path.join(self.tmpdirname   , """dataset"""  )
															_SCREAMING_SNAKE_CASE			:	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
															_SCREAMING_SNAKE_CASE			:	Tuple							=				RagPyTorchDistributedRetriever(
															    __snake_case   , question_encoder_tokenizer=self.get_dpr_tokenizer()   , generator_tokenizer=self.get_bart_tokenizer()   , )
											else:
															_SCREAMING_SNAKE_CASE			:	Any							=				RagPyTorchDistributedRetriever(
															    __snake_case   , question_encoder_tokenizer=self.get_dpr_tokenizer()   , generator_tokenizer=self.get_bart_tokenizer()   , index=CustomHFIndex(config.retrieval_vector_size   , __snake_case  )   , )
											if init_retrieval:
															retriever.init_retrieval(__snake_case  )
											return retriever






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case  ):
											# 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=__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				self.get_dummy_dataset()
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size   , question_encoder=DPRConfig().to_dict()   , generator=BartConfig().to_dict()   , index_name="""custom"""   , )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				ray.remote(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				[remote_cls.remote() for _ in range(1  )]
											if from_disk:
															_SCREAMING_SNAKE_CASE			:	str							=				os.path.join(self.tmpdirname   , """dataset"""  )
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				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
															_SCREAMING_SNAKE_CASE			:	Tuple							=				RagRayDistributedRetriever(
															    __snake_case   , question_encoder_tokenizer=self.get_dpr_tokenizer()   , generator_tokenizer=self.get_bart_tokenizer()   , retrieval_workers=__snake_case   , index=CustomHFIndex.load_from_disk(
															        vector_size=config.retrieval_vector_size   , dataset_path=config.passages_path   , index_path=config.index_path   , )   , )
											else:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				RagRayDistributedRetriever(
															    __snake_case   , question_encoder_tokenizer=self.get_dpr_tokenizer()   , generator_tokenizer=self.get_bart_tokenizer()   , retrieval_workers=__snake_case   , index=CustomHFIndex(config.retrieval_vector_size   , __snake_case  )   , )
											if init_retrieval:
															retriever.init_retrieval()
											return retriever






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	List[Any]							=				retriever.retrieve(__snake_case   , n_docs=__snake_case  )
											self.assertEqual(retrieved_doc_embeds.shape   , (2, n_docs, self.retrieval_vector_size)  )
											self.assertEqual(len(__snake_case  )   , 2  )
											self.assertEqual(sorted(doc_dicts[0]  )   , ["""embeddings""", """id""", """text""", """title"""]  )
											self.assertEqual(len(doc_dicts[0]["""id"""]  )   , __snake_case  )
											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  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				1
											_SCREAMING_SNAKE_CASE			:	Dict							=				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=__snake_case  )   , __snake_case   , __snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				1
											_SCREAMING_SNAKE_CASE			:	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_pytorch_retriever(init_retrieval=__snake_case   , from_disk=__snake_case  )   , __snake_case   , __snake_case   , )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				1
											_SCREAMING_SNAKE_CASE			:	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=__snake_case   , from_disk=__snake_case  )   , __snake_case   , __snake_case   , )






							@require_ray
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				1
											_SCREAMING_SNAKE_CASE			:	Tuple							=				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=__snake_case  )   , __snake_case   , __snake_case  )
											ray.shutdown()






							@require_ray
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				1
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				np.array(
											    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   , dtype=np.floataa  )
											with self.assertRaises(__snake_case  ):
															self.distributed_retriever_check(
															    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__snake_case   , from_disk=__snake_case  )   , __snake_case   , __snake_case   , )
											ray.shutdown()






							@require_ray
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				1
											_SCREAMING_SNAKE_CASE			:	Tuple							=				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=__snake_case   , from_disk=__snake_case  )   , __snake_case   , __snake_case  )
											ray.shutdown()








'''simple docstring'''


import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							A_							:				Optional[Any]				     =  EsmTokenizer






							def UpperCAmelCase_     (   self  ):
											super().setUp()
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tempfile.mkdtemp()
											# fmt: off
											_SCREAMING_SNAKE_CASE			:	List[str]							=				["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
											# fmt: on
											_SCREAMING_SNAKE_CASE			:	Any							=				os.path.join(self.tmpdirname   , 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]  )  )






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return [self.get_tokenizer(**__snake_case  )]






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return self.tokenizer_class.from_pretrained(self.tmpdirname   , **__snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	int							=				tokenizer.tokenize("""LAGVS"""  )
											self.assertListEqual(__snake_case   , ["""L""", """A""", """G""", """V""", """S"""]  )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case  )   , [4, 5, 6, 7, 8]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				"""LAGVS"""
											self.assertListEqual(tokenizer.encode(__snake_case  )   , [0, 4, 5, 6, 7, 8, 2]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	str							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_tokenizers(fast=__snake_case  )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""  ):
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<unk>"""
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<mask>"""

																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer.tokenize(__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.tokenize(__snake_case  )

																			self.assertEqual(len(__snake_case  )   , 1  )
																			self.assertEqual(len(__snake_case  )   , 1  )
																			self.assertEqual(token_a[0]   , __snake_case  )
																			self.assertEqual(token_a[0]   , __snake_case  )




