




"""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:
				lowercase_		=							None

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







def 			lowerCAmelCase  (     UpperCAmelCase	)  ->Optional[int]:






					"""simple docstring"""






					if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
										__magic_name__ :							List[str]		       =							unittest.skip('''test requires Datasets, Faiss, psutil'''	)(_lowerCamelCase	)
					return test_case





@require_distributed_retrieval
class 		A__    (       _a			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							tempfile.mkdtemp()
						__magic_name__ :							List[str]		       =							8

						# DPR tok
						__magic_name__ :							List[str]		       =							[
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """want""",
						    """##want""",
						    """##ed""",
						    """wa""",
						    """un""",
						    """runn""",
						    """##ing""",
						    """,""",
						    """low""",
						    """lowest""",
						]
						__magic_name__ :							List[Any]		       =							os.path.join(self.tmpdirname    ,					'''dpr_tokenizer'''						)
						os.makedirs(snake_case_    ,					exist_ok=snake_case_						)
						__magic_name__ :							Dict		       =							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
						__magic_name__ :							List[str]		       =							[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """\u0120""",
						    """\u0120l""",
						    """\u0120n""",
						    """\u0120lo""",
						    """\u0120low""",
						    """er""",
						    """\u0120lowest""",
						    """\u0120newer""",
						    """\u0120wider""",
						    """<unk>""",
						]
						__magic_name__ :							Dict		       =							dict(zip(snake_case_    ,					range(len(snake_case_						)						)						)						)
						__magic_name__ :							List[str]		       =							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
						__magic_name__ :							Any		       =							{"""unk_token""": """<unk>"""}

						__magic_name__ :							Any		       =							os.path.join(self.tmpdirname    ,					'''bart_tokenizer'''						)
						os.makedirs(snake_case_    ,					exist_ok=snake_case_						)
						__magic_name__ :							Union[str, Any]		       =							os.path.join(snake_case_    ,					BART_VOCAB_FILES_NAMES['''vocab_file''']						)
						__magic_name__ :							Optional[int]		       =							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 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






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






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






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






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						shutil.rmtree(self.tmpdirname						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							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    ,					lowerCamelCase    ,					lowerCamelCase=12345						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							self.get_dummy_dataset()
						__magic_name__ :							str		       =							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:
											__magic_name__ :							int		       =							dataset
											__magic_name__ :							List[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 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[Any]:




						"""simple docstring"""






						ray.init(local_mode=snake_case_						)
						__magic_name__ :							List[Any]		       =							RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size    ,					question_encoder=DPRConfig().to_dict()    ,					generator=BartConfig().to_dict()    ,					)
						__magic_name__ :							Union[str, Any]		       =							ray.remote(snake_case_						)
						__magic_name__ :							Union[str, Any]		       =							[remote_cls.remote() for _ in range(1						)]
						with patch('''transformers.models.rag.retrieval_rag.load_dataset'''						) as mock_load_dataset:
											__magic_name__ :							List[Any]		       =							self.get_dummy_dataset()
											__magic_name__ :							Union[str, 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 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase=12345						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							str		       =							self.get_dummy_dataset()
						__magic_name__ :							Optional[int]		       =							RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size    ,					question_encoder=DPRConfig().to_dict()    ,					generator=BartConfig().to_dict()    ,					index_name='''custom'''    ,					)
						if from_disk:
											__magic_name__ :							int		       =							os.path.join(self.tmpdirname    ,					'''dataset'''						)
											__magic_name__ :							Union[str, 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
											__magic_name__ :							Tuple		       =							RagPyTorchDistributedRetriever(
											    snake_case_    ,					question_encoder_tokenizer=self.get_dpr_tokenizer()    ,					generator_tokenizer=self.get_bart_tokenizer()    ,					)
						else:
											__magic_name__ :							Optional[int]		       =							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 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						ray.init(local_mode=snake_case_						)
						__magic_name__ :							Optional[Any]		       =							self.get_dummy_dataset()
						__magic_name__ :							str		       =							RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size    ,					question_encoder=DPRConfig().to_dict()    ,					generator=BartConfig().to_dict()    ,					index_name='''custom'''    ,					)
						__magic_name__ :							List[Any]		       =							ray.remote(snake_case_						)
						__magic_name__ :							Union[str, Any]		       =							[remote_cls.remote() for _ in range(1						)]
						if from_disk:
											__magic_name__ :							str		       =							os.path.join(self.tmpdirname    ,					'''dataset'''						)
											__magic_name__ :							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
											__magic_name__ :							int		       =							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:
											__magic_name__ :							int		       =							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 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							Optional[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 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Any		       =							1
						__magic_name__ :							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_pytorch_distributed_retriever(init_retrieval=snake_case_						)    ,					snake_case_    ,					snake_case_						)






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							1
						__magic_name__ :							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_custom_hf_index_pytorch_retriever(init_retrieval=snake_case_    ,					from_disk=snake_case_						)    ,					snake_case_    ,					snake_case_    ,					)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							1
						__magic_name__ :							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 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							1
						__magic_name__ :							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=snake_case_						)    ,					snake_case_    ,					snake_case_						)
						ray.shutdown()






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




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							1
						__magic_name__ :							Dict		       =							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 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Any		       =							1
						__magic_name__ :							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_custom_hf_index_ray_retriever(init_retrieval=snake_case_    ,					from_disk=snake_case_						)    ,					snake_case_    ,					snake_case_						)
						ray.shutdown()









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel





@require_torch
@require_sentencepiece
@require_tokenizers
class 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]    ,					device=lowerCamelCase    ,					dtype=torch.float    ,					)
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]    ,					lowerCamelCase    ,					atol=1e-4						)						)



