


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:
							__magic_name__       					=      None

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







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


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





@require_distributed_retrieval
class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Tuple      =       tempfile.mkdtemp()
							lowerCamelCase_			:							Tuple      =       8

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

							lowerCamelCase_			:							Dict      =       os.path.join(self.tmpdirname				,							"bart_tokenizer"     )
							os.makedirs(a_				,							exist_ok=a_     )
							lowerCamelCase_			:							str      =       os.path.join(a_				,							BART_VOCAB_FILES_NAMES["vocab_file"]     )
							lowerCamelCase_			:							List[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 					_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     ):
							lowerCamelCase_			:							Any      =       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				,							a_				,							a_=1_2345     ):
							lowerCamelCase_			:							Union[str, Any]      =       self.get_dummy_dataset()
							lowerCamelCase_			:							List[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:
								lowerCamelCase_			:							List[Any]      =       dataset
								lowerCamelCase_			:							Optional[Any]      =       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 					_UpperCamelCase			(   self				,							a_     ):
							# 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=a_     )
							lowerCamelCase_			:							Dict      =       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							)
							lowerCamelCase_			:							Any      =       ray.remote(a_     )
							lowerCamelCase_			:							Dict      =       [remote_cls.remote() for _ in range(1     )]
							with patch("transformers.models.rag.retrieval_rag.load_dataset"     ) as mock_load_dataset:
								lowerCamelCase_			:							Dict      =       self.get_dummy_dataset()
								lowerCamelCase_			:							Dict      =       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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_=1_2345     ):
							lowerCamelCase_			:							List[Any]      =       self.get_dummy_dataset()
							lowerCamelCase_			:							str      =       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							index_name="custom"				,							)
							if from_disk:
								lowerCamelCase_			:							Dict      =       os.path.join(self.tmpdirname				,							"dataset"     )
								lowerCamelCase_			:							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
								lowerCamelCase_			:							List[Any]      =       RagPyTorchDistributedRetriever(
								    a_				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							)
							else:
								lowerCamelCase_			:							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 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							# 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=a_     )
							lowerCamelCase_			:							Optional[Any]      =       self.get_dummy_dataset()
							lowerCamelCase_			:							Union[str, Any]      =       RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							index_name="custom"				,							)
							lowerCamelCase_			:							Dict      =       ray.remote(a_     )
							lowerCamelCase_			:							Dict      =       [remote_cls.remote() for _ in range(1     )]
							if from_disk:
								lowerCamelCase_			:							Tuple      =       os.path.join(self.tmpdirname				,							"dataset"     )
								lowerCamelCase_			:							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
								lowerCamelCase_			:							Optional[int]      =       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:
								lowerCamelCase_			:							List[str]      =       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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_     ):
							lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							Dict      =       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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       1
							lowerCamelCase_			:							Union[str, 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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Any      =       1
							lowerCamelCase_			:							Union[str, 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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       1
							lowerCamelCase_			:							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_pytorch_retriever(init_retrieval=a_				,							from_disk=a_     )				,							a_				,							a_				,							)




						@require_ray
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Optional[int]      =       1
							lowerCamelCase_			:							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_ray_distributed_retriever(init_retrieval=a_     )				,							a_				,							a_     )
							ray.shutdown()




						@require_ray
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       1
							lowerCamelCase_			:							Union[str, 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 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[Any]      =       1
							lowerCamelCase_			:							Union[str, 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=a_				,							from_disk=a_     )				,							a_				,							a_     )
							ray.shutdown()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__       					=      {'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''LevitFeatureExtractor''']
							__magic_name__       					=      ['''LevitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LevitForImageClassification''',
							    '''LevitForImageClassificationWithTeacher''',
							    '''LevitModel''',
							    '''LevitPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_levit import LevitFeatureExtractor
														from .image_processing_levit import LevitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_levit import (
														    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LevitForImageClassification,
														    LevitForImageClassificationWithTeacher,
														    LevitModel,
														    LevitPreTrainedModel,
														)
else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

