

"""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:
						lowerCamelCase__			:					Any							  =					None

if is_ray_available():
						import ray  # noqa: E402 # isort:skip
						from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
						lowerCamelCase__			:					Union[str, Any]							  =					None
						lowerCamelCase__			:					str							  =					None
						lowerCamelCase__			:					Any							  =					None
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int]	)			->     Union[str, Any]:
	if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
		_UpperCAmelCase      :							Tuple       =   unittest.skip("""test requires Datasets, Faiss, psutil"""	)(_lowerCAmelCase	)
	return test_case






@require_distributed_retrieval
class       _UpperCAmelCase     (						__a):
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							int       =   tempfile.mkdtemp()
				_UpperCAmelCase      :							Optional[int]       =   8

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

				_UpperCAmelCase      :							Tuple       =   os.path.join(self.tmpdirname ,						"""bart_tokenizer"""       )
				os.makedirs(_A ,						exist_ok=_A       )
				_UpperCAmelCase      :							int       =   os.path.join(_A ,						BART_VOCAB_FILES_NAMES["""vocab_file"""]       )
				_UpperCAmelCase      :							Union[str, Any]       =   os.path.join(_A ,						BART_VOCAB_FILES_NAMES["""merges_file"""]       )
				with open(self.vocab_file ,						"""w""" ,						encoding="""utf-8"""       ) as fp:
					fp.write(json.dumps(_A       ) + """\n"""       )
				with open(self.merges_file ,						"""w""" ,						encoding="""utf-8"""       ) as fp:
					fp.write("""\n""".join(_A       )       )
			def 							__snake_case	(						self       )      ->      DPRQuestionEncoderTokenizer:






				'''simple docstring'''







				return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,						"""dpr_tokenizer"""       )       )
			def 							__snake_case	(						self       )      ->      BartTokenizer:






				'''simple docstring'''







				return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,						"""bart_tokenizer"""       )       )
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				shutil.rmtree(self.tmpdirname       )
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							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 							__snake_case	(						self ,						_A ,						_A=1_23_45       )      ->      RagPyTorchDistributedRetriever:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[Any]       =   self.get_dummy_dataset()
				_UpperCAmelCase      :							Union[str, Any]       =   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      :							List[Any]       =   dataset
					_UpperCAmelCase      :							str       =   RagPyTorchDistributedRetriever(
					    _A ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer() ,						)
					if init_retrieval:
						retriever.init_retrieval(_A       )
				return retriever
			def 							__snake_case	(						self ,						_A       )      ->      RagRayDistributedRetriever:






				'''simple docstring'''







				ray.init(local_mode=_A       )
				_UpperCAmelCase      :							Optional[int]       =   RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size ,						question_encoder=DPRConfig().to_dict() ,						generator=BartConfig().to_dict() ,						)
				_UpperCAmelCase      :							Union[str, Any]       =   ray.remote(_A       )
				_UpperCAmelCase      :							List[str]       =   [remote_cls.remote() for _ in range(1       )]
				with patch("""transformers.models.rag.retrieval_rag.load_dataset"""       ) as mock_load_dataset:
					_UpperCAmelCase      :							Optional[int]       =   self.get_dummy_dataset()
					_UpperCAmelCase      :							Optional[int]       =   RagRayDistributedRetriever(
					    _A ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer() ,						retrieval_workers=_A ,						)
					if init_retrieval:
						retriever.init_retrieval()
				return retriever
			def 							__snake_case	(						self ,						_A ,						_A ,						_A=1_23_45       )      ->      Optional[Any]:






				'''simple docstring'''







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






				'''simple docstring'''







				ray.init(local_mode=_A       )
				_UpperCAmelCase      :							Dict       =   self.get_dummy_dataset()
				_UpperCAmelCase      :							Union[str, Any]       =   RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size ,						question_encoder=DPRConfig().to_dict() ,						generator=BartConfig().to_dict() ,						index_name="""custom""" ,						)
				_UpperCAmelCase      :							List[Any]       =   ray.remote(_A       )
				_UpperCAmelCase      :							Optional[int]       =   [remote_cls.remote() for _ in range(1       )]
				if from_disk:
					_UpperCAmelCase      :							Tuple       =   os.path.join(self.tmpdirname ,						"""dataset"""       )
					_UpperCAmelCase      :							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
					_UpperCAmelCase      :							Optional[Any]       =   RagRayDistributedRetriever(
					    _A ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer() ,						retrieval_workers=_A ,						index=CustomHFIndex.load_from_disk(
					        vector_size=config.retrieval_vector_size ,						dataset_path=config.passages_path ,						index_path=config.index_path ,						) ,						)
				else:
					_UpperCAmelCase      :							Union[str, Any]       =   RagRayDistributedRetriever(
					    _A ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer() ,						retrieval_workers=_A ,						index=CustomHFIndex(config.retrieval_vector_size ,						_A       ) ,						)
				if init_retrieval:
					retriever.init_retrieval()
				return retriever
			def 							__snake_case	(						self ,						_A ,						_A ,						_A       )      ->      None:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   retriever.retrieve(_A ,						n_docs=_A       )
				self.assertEqual(retrieved_doc_embeds.shape ,						(2, n_docs, self.retrieval_vector_size)       )
				self.assertEqual(len(_A       ) ,						2       )
				self.assertEqual(sorted(doc_dicts[0]       ) ,						["""embeddings""", """id""", """text""", """title"""]       )
				self.assertEqual(len(doc_dicts[0]["""id"""]       ) ,						_A       )
				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       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   1
				_UpperCAmelCase      :							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=_A       ) ,						_A ,						_A       )
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   1
				_UpperCAmelCase      :							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=_A ,						from_disk=_A       ) ,						_A ,						_A ,						)
			def 							__snake_case	(						self       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   1
				_UpperCAmelCase      :							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=_A ,						from_disk=_A       ) ,						_A ,						_A ,						)
			@require_ray
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   1
				_UpperCAmelCase      :							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_ray_distributed_retriever(init_retrieval=_A       ) ,						_A ,						_A       )
				ray.shutdown()
			@require_ray
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







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






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   1
				_UpperCAmelCase      :							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_ray_retriever(init_retrieval=_A ,						from_disk=_A       ) ,						_A ,						_A       )
				ray.shutdown()








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			:					int							  =					{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					Optional[int]							  =					[
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						lowerCamelCase__			:					Optional[Any]							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





