


'''simple docstring'''






import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device


def 					_snake_case   (     A       )						->    Any:
							lowerCAmelCase__          =	{}
							for name in unet.attn_processors.keys():
														lowerCAmelCase__          =	None if name.endswith('''attn1.processor'''       ) else unet.config.cross_attention_dim
														if name.startswith('''mid_block'''       ):
																					lowerCAmelCase__          =	unet.config.block_out_channels[-1]
														elif name.startswith('''up_blocks'''       ):
																					lowerCAmelCase__          =	int(name[len('''up_blocks.'''       )]       )
																					lowerCAmelCase__          =	list(reversed(unet.config.block_out_channels       )       )[block_id]
														elif name.startswith('''down_blocks'''       ):
																					lowerCAmelCase__          =	int(name[len('''down_blocks.'''       )]       )
																					lowerCAmelCase__          =	unet.config.block_out_channels[block_id]
														lowerCAmelCase__          =	(
														    LoRAAttnProcessor2_0 if hasattr(A ,    '''scaled_dot_product_attention'''       ) else LoRAAttnProcessor
														)
														lowerCAmelCase__          =	lora_attn_processor_class(
														    hidden_size=A ,    cross_attention_dim=A       )
							lowerCAmelCase__          =	AttnProcsLayers(A       )
							return lora_attn_procs, unet_lora_layers


def 					_snake_case   (     A       )						->    List[str]:
							lowerCAmelCase__          =	{}
							lowerCAmelCase__          =	(
							    LoRAAttnProcessor2_0 if hasattr(A ,    '''scaled_dot_product_attention'''       ) else LoRAAttnProcessor
							)
							for name, module in text_encoder.named_modules():
														if name.endswith(A       ):
																					lowerCAmelCase__          =	lora_attn_processor_class(
																					    hidden_size=module.out_proj.out_features ,    cross_attention_dim=A       )
							return text_lora_attn_procs


def 					_snake_case   (     A       )						->    Optional[Any]:
							lowerCAmelCase__          =	create_text_encoder_lora_attn_procs(A       )
							lowerCAmelCase__          =	AttnProcsLayers(A       )
							return text_encoder_lora_layers


def 					_snake_case   (     A ,    A=False       )						->    Dict:
							for _, attn_proc in text_lora_attn_procs.items():
														# set up.weights
														for layer_name, layer_module in attn_proc.named_modules():
																					if layer_name.endswith('''_lora'''       ):
																												lowerCAmelCase__          =	(
																												    torch.randn_like(layer_module.up.weight       )
																												    if randn_weight
																												    else torch.zeros_like(layer_module.up.weight       )
																												)
																												lowerCAmelCase__          =	torch.nn.Parameter(A       )






class 		a__				(							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														torch.manual_seed(0   )
														lowerCAmelCase__          =	UNetaDConditionModel(
														    block_out_channels=(32, 64)					,    layers_per_block=2					,    sample_size=32					,    in_channels=4					,    out_channels=4					,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,    cross_attention_dim=32					,    )
														lowerCAmelCase__          =	DDIMScheduler(
														    beta_start=0.00_085					,    beta_end=0.012					,    beta_schedule='''scaled_linear'''					,    clip_sample=lowerCamelCase_					,    set_alpha_to_one=lowerCamelCase_					,    steps_offset=1					,    )
														torch.manual_seed(0   )
														lowerCAmelCase__          =	AutoencoderKL(
														    block_out_channels=[32, 64]					,    in_channels=3					,    out_channels=3					,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,    latent_channels=4					,    )
														lowerCAmelCase__          =	CLIPTextConfig(
														    bos_token_id=0					,    eos_token_id=2					,    hidden_size=32					,    intermediate_size=37					,    layer_norm_eps=1e-05					,    num_attention_heads=4					,    num_hidden_layers=5					,    pad_token_id=1					,    vocab_size=10_00					,    )
														lowerCAmelCase__          =	CLIPTextModel(lowerCamelCase_   )
														lowerCAmelCase__          =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

														lowerCAmelCase__   ,					lowerCAmelCase__          =	create_unet_lora_layers(lowerCamelCase_   )
														lowerCAmelCase__          =	create_text_encoder_lora_layers(lowerCamelCase_   )

														lowerCAmelCase__          =	{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														lowerCAmelCase__          =	{
														    '''unet_lora_layers''': unet_lora_layers,
														    '''text_encoder_lora_layers''': text_encoder_lora_layers,
														    '''unet_lora_attn_procs''': unet_lora_attn_procs,
														}
														return pipeline_components, lora_components







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	10
														lowerCAmelCase__          =	4
														lowerCAmelCase__          =	(32, 32)

														lowerCAmelCase__          =	torch.manual_seed(0   )
														lowerCAmelCase__          =	floats_tensor((batch_size, num_channels) + sizes   )
														lowerCAmelCase__          =	torch.randint(1					,    lowerCamelCase_					,    size=(batch_size, sequence_length)					,    generator=lowerCamelCase_   )

														lowerCAmelCase__          =	{
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''output_type''': '''numpy''',
														}

														return noise, input_ids, pipeline_inputs

														# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														lowerCAmelCase__          =	77

														lowerCAmelCase__          =	torch.randint(2					,    56					,    size=(1, max_seq_length)					,    generator=torch.manual_seed(0   )   )

														lowerCAmelCase__          =	{}
														lowerCAmelCase__          =	inputs
														return prepared_inputs







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Optional[int]:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														LoraLoaderMixin.save_lora_weights(
														    save_directory=lowerCamelCase_					,    unet_lora_layers=lora_components['''unet_lora_layers''']					,    text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']					,    )
														self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_					,    '''pytorch_lora_weights.bin'''   )   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )
														lowerCAmelCase__          =	sd_pipe.to(lowerCamelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_inputs()

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	original_images[0, -3:, -3:, -1]

														with tempfile.TemporaryDirectory() as tmpdirname:
																					LoraLoaderMixin.save_lora_weights(
																					    save_directory=lowerCamelCase_					,    unet_lora_layers=lora_components['''unet_lora_layers''']					,    text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']					,    )
																					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_					,    '''pytorch_lora_weights.bin'''   )   )   )
																					sd_pipe.load_lora_weights(lowerCamelCase_   )

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	lora_images[0, -3:, -3:, -1]

														# Outputs shouldn't match.
														self.assertFalse(torch.allclose(torch.from_numpy(lowerCamelCase_   )					,    torch.from_numpy(lowerCamelCase_   )   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )
														lowerCAmelCase__          =	sd_pipe.to(lowerCamelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_inputs()

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	original_images[0, -3:, -3:, -1]

														with tempfile.TemporaryDirectory() as tmpdirname:
																					LoraLoaderMixin.save_lora_weights(
																					    save_directory=lowerCamelCase_					,    unet_lora_layers=lora_components['''unet_lora_layers''']					,    text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']					,    safe_serialization=lowerCamelCase_					,    )
																					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_					,    '''pytorch_lora_weights.safetensors'''   )   )   )
																					sd_pipe.load_lora_weights(lowerCamelCase_   )

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	lora_images[0, -3:, -3:, -1]

														# Outputs shouldn't match.
														self.assertFalse(torch.allclose(torch.from_numpy(lowerCamelCase_   )					,    torch.from_numpy(lowerCamelCase_   )   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														lowerCAmelCase__          =	lora_components['''unet_lora_attn_procs''']
														lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )
														lowerCAmelCase__          =	sd_pipe.to(lowerCamelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_inputs()

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	original_images[0, -3:, -3:, -1]

														with tempfile.TemporaryDirectory() as tmpdirname:
																					lowerCAmelCase__          =	sd_pipe.unet
																					unet.set_attn_processor(lowerCamelCase_   )
																					unet.save_attn_procs(lowerCamelCase_   )
																					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_					,    '''pytorch_lora_weights.bin'''   )   )   )
																					sd_pipe.load_lora_weights(lowerCamelCase_   )

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	lora_images[0, -3:, -3:, -1]

														# Outputs shouldn't match.
														self.assertFalse(torch.allclose(torch.from_numpy(lowerCamelCase_   )					,    torch.from_numpy(lowerCamelCase_   )   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )

														lowerCAmelCase__          =	self.get_dummy_tokens()

														# inference without lora
														lowerCAmelCase__          =	pipe.text_encoder(**lowerCamelCase_   )[0]
														assert outputs_without_lora.shape == (1, 77, 32)

														# create lora_attn_procs with zeroed out up.weights
														lowerCAmelCase__          =	create_text_encoder_lora_attn_procs(pipe.text_encoder   )
														set_lora_up_weights(lowerCamelCase_					,    randn_weight=lowerCamelCase_   )

														# monkey patch
														pipe._modify_text_encoder(lowerCamelCase_   )

														# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
														del text_attn_procs
														gc.collect()

														# inference with lora
														lowerCAmelCase__          =	pipe.text_encoder(**lowerCamelCase_   )[0]
														assert outputs_with_lora.shape == (1, 77, 32)

														assert torch.allclose(
														    lowerCamelCase_					,    lowerCamelCase_   ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

														# create lora_attn_procs with randn up.weights
														lowerCAmelCase__          =	create_text_encoder_lora_attn_procs(pipe.text_encoder   )
														set_lora_up_weights(lowerCamelCase_					,    randn_weight=lowerCamelCase_   )

														# monkey patch
														pipe._modify_text_encoder(lowerCamelCase_   )

														# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
														del text_attn_procs
														gc.collect()

														# inference with lora
														lowerCAmelCase__          =	pipe.text_encoder(**lowerCamelCase_   )[0]
														assert outputs_with_lora.shape == (1, 77, 32)

														assert not torch.allclose(
														    lowerCamelCase_					,    lowerCamelCase_   ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )

														lowerCAmelCase__          =	self.get_dummy_tokens()

														# inference without lora
														lowerCAmelCase__          =	pipe.text_encoder(**lowerCamelCase_   )[0]
														assert outputs_without_lora.shape == (1, 77, 32)

														# create lora_attn_procs with randn up.weights
														lowerCAmelCase__          =	create_text_encoder_lora_attn_procs(pipe.text_encoder   )
														set_lora_up_weights(lowerCamelCase_					,    randn_weight=lowerCamelCase_   )

														# monkey patch
														pipe._modify_text_encoder(lowerCamelCase_   )

														# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
														del text_attn_procs
														gc.collect()

														# inference with lora
														lowerCAmelCase__          =	pipe.text_encoder(**lowerCamelCase_   )[0]
														assert outputs_with_lora.shape == (1, 77, 32)

														assert not torch.allclose(
														    lowerCamelCase_					,    lowerCamelCase_   ), "lora outputs should be different to without lora outputs"

														# remove monkey patch
														pipe._remove_text_encoder_monkey_patch()

														# inference with removed lora
														lowerCAmelCase__          =	pipe.text_encoder(**lowerCamelCase_   )[0]
														assert outputs_without_lora_removed.shape == (1, 77, 32)

														assert torch.allclose(
														    lowerCamelCase_					,    lowerCamelCase_   ), "remove lora monkey patch should restore the original outputs"







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )
														lowerCAmelCase__          =	sd_pipe.to(lowerCamelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_inputs()

														with tempfile.TemporaryDirectory() as tmpdirname:
																					LoraLoaderMixin.save_lora_weights(
																					    save_directory=lowerCamelCase_					,    unet_lora_layers=lora_components['''unet_lora_layers''']					,    text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']					,    )
																					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_					,    '''pytorch_lora_weights.bin'''   )   )   )
																					sd_pipe.load_lora_weights(lowerCamelCase_   )

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	lora_images[0, -3:, -3:, -1]

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_					,    cross_attention_kwargs={'''scale''': 0.5}   ).images
														lowerCAmelCase__          =	lora_images_with_scale[0, -3:, -3:, -1]

														# Outputs shouldn't match.
														self.assertFalse(
														    torch.allclose(torch.from_numpy(lowerCamelCase_   )					,    torch.from_numpy(lowerCamelCase_   )   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														with tempfile.TemporaryDirectory() as tmpdirname:
																					self.create_lora_weight_file(lowerCamelCase_   )

																					lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
																					lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )
																					lowerCAmelCase__          =	sd_pipe.to(lowerCamelCase_   )
																					sd_pipe.set_progress_bar_config(disable=lowerCamelCase_   )

																					# check if vanilla attention processors are used
																					for _, module in sd_pipe.unet.named_modules():
																												if isinstance(lowerCamelCase_					,    lowerCamelCase_   ):
																																			self.assertIsInstance(module.processor					,    (AttnProcessor, AttnProcessor2_0)   )

            # load LoRA weight file
																					sd_pipe.load_lora_weights(lowerCamelCase_   )

																					# check if lora attention processors are used
																					for _, module in sd_pipe.unet.named_modules():
																												if isinstance(lowerCamelCase_					,    lowerCamelCase_   ):
																																			lowerCAmelCase__          =	(
																																			    LoRAAttnProcessor2_0 if hasattr(lowerCamelCase_					,    '''scaled_dot_product_attention'''   ) else LoRAAttnProcessor
																																			)
																																			self.assertIsInstance(module.processor					,    lowerCamelCase_   )







							@unittest.skipIf(torch_device != '''cuda'''					,    '''This test is supposed to run on GPU'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														with tempfile.TemporaryDirectory() as tmpdirname:
																					self.create_lora_weight_file(lowerCamelCase_   )

																					lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
																					lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )
																					lowerCAmelCase__          =	sd_pipe.to(lowerCamelCase_   )
																					sd_pipe.set_progress_bar_config(disable=lowerCamelCase_   )

																					# enable XFormers
																					sd_pipe.enable_xformers_memory_efficient_attention()

																					# check if xFormers attention processors are used
																					for _, module in sd_pipe.unet.named_modules():
																												if isinstance(lowerCamelCase_					,    lowerCamelCase_   ):
																																			self.assertIsInstance(module.processor					,    lowerCamelCase_   )

            # load LoRA weight file
																					sd_pipe.load_lora_weights(lowerCamelCase_   )

																					# check if lora attention processors are used
																					for _, module in sd_pipe.unet.named_modules():
																												if isinstance(lowerCamelCase_					,    lowerCamelCase_   ):
																																			self.assertIsInstance(module.processor					,    lowerCamelCase_   )







							@unittest.skipIf(torch_device != '''cuda'''					,    '''This test is supposed to run on GPU'''   )
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_components()
														lowerCAmelCase__          =	StableDiffusionPipeline(**lowerCamelCase_   )
														lowerCAmelCase__          =	sd_pipe.to(lowerCamelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase_   )

														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	self.get_dummy_inputs()

														# enable XFormers
														sd_pipe.enable_xformers_memory_efficient_attention()

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	original_images[0, -3:, -3:, -1]

														with tempfile.TemporaryDirectory() as tmpdirname:
																					LoraLoaderMixin.save_lora_weights(
																					    save_directory=lowerCamelCase_					,    unet_lora_layers=lora_components['''unet_lora_layers''']					,    text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']					,    )
																					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_					,    '''pytorch_lora_weights.bin'''   )   )   )
																					sd_pipe.load_lora_weights(lowerCamelCase_   )

														lowerCAmelCase__          =	sd_pipe(**lowerCamelCase_   ).images
														lowerCAmelCase__          =	lora_images[0, -3:, -3:, -1]

														# Outputs shouldn't match.
														self.assertFalse(torch.allclose(torch.from_numpy(lowerCamelCase_   )					,    torch.from_numpy(lowerCamelCase_   )   )   )



'''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    				=							None

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


def 					_snake_case   (     A       )						->    Any:
							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														lowerCAmelCase__          =	unittest.skip('''test requires Datasets, Faiss, psutil'''       )(A       )
							return test_case






@require_distributed_retrieval
class 		a__				(							a__  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	tempfile.mkdtemp()
														lowerCAmelCase__          =	8

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

														lowerCAmelCase__          =	os.path.join(self.tmpdirname					,    '''bart_tokenizer'''   )
														os.makedirs(lowerCamelCase_					,    exist_ok=lowerCamelCase_   )
														lowerCAmelCase__          =	os.path.join(lowerCamelCase_					,    BART_VOCAB_FILES_NAMES['''vocab_file''']   )
														lowerCAmelCase__          =	os.path.join(lowerCamelCase_					,    BART_VOCAB_FILES_NAMES['''merges_file''']   )
														with open(self.vocab_file					,    '''w'''					,    encoding='''utf-8'''   ) as fp:
																					fp.write(json.dumps(lowerCamelCase_   ) + '''\n'''   )
														with open(self.merges_file					,    '''w'''					,    encoding='''utf-8'''   ) as fp:
																					fp.write('''\n'''.join(lowerCamelCase_   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							DPRQuestionEncoderTokenizer:
														return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,    '''dpr_tokenizer'''   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							BartTokenizer:
														return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,    '''bart_tokenizer'''   )   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[int]:
														shutil.rmtree(self.tmpdirname   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														lowerCAmelCase__          =	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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_=1_23_45   )       ->							RagPyTorchDistributedRetriever:
														lowerCAmelCase__          =	self.get_dummy_dataset()
														lowerCAmelCase__          =	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__          =	dataset
																					lowerCAmelCase__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
																					if init_retrieval:
																												retriever.init_retrieval(lowerCamelCase_   )
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							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=lowerCamelCase_   )
														lowerCAmelCase__          =	RagConfig(
														    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    )
														lowerCAmelCase__          =	ray.remote(lowerCamelCase_   )
														lowerCAmelCase__          =	[remote_cls.remote() for _ in range(1   )]
														with patch('''transformers.models.rag.retrieval_rag.load_dataset'''   ) as mock_load_dataset:
																					lowerCAmelCase__          =	self.get_dummy_dataset()
																					lowerCAmelCase__          =	RagRayDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCamelCase_					,    )
																					if init_retrieval:
																												retriever.init_retrieval()
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_=1_23_45   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	self.get_dummy_dataset()
														lowerCAmelCase__          =	RagConfig(
														    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    index_name='''custom'''					,    )
														if from_disk:
																					lowerCAmelCase__          =	os.path.join(self.tmpdirname					,    '''dataset'''   )
																					lowerCAmelCase__          =	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__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
														else:
																					lowerCAmelCase__          =	RagPyTorchDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    index=CustomHFIndex(config.retrieval_vector_size					,    lowerCamelCase_   )					,    )
														if init_retrieval:
																					retriever.init_retrieval(lowerCamelCase_   )
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, 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=lowerCamelCase_   )
														lowerCAmelCase__          =	self.get_dummy_dataset()
														lowerCAmelCase__          =	RagConfig(
														    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    index_name='''custom'''					,    )
														lowerCAmelCase__          =	ray.remote(lowerCamelCase_   )
														lowerCAmelCase__          =	[remote_cls.remote() for _ in range(1   )]
														if from_disk:
																					lowerCAmelCase__          =	os.path.join(self.tmpdirname					,    '''dataset'''   )
																					lowerCAmelCase__          =	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__          =	RagRayDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCamelCase_					,    index=CustomHFIndex.load_from_disk(
																					        vector_size=config.retrieval_vector_size					,    dataset_path=config.passages_path					,    index_path=config.index_path					,    )					,    )
														else:
																					lowerCAmelCase__          =	RagRayDistributedRetriever(
																					    lowerCamelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCamelCase_					,    index=CustomHFIndex(config.retrieval_vector_size					,    lowerCamelCase_   )					,    )
														if init_retrieval:
																					retriever.init_retrieval()
														return retriever







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__          =	retriever.retrieve(lowerCamelCase_					,    n_docs=lowerCamelCase_   )
														self.assertEqual(retrieved_doc_embeds.shape					,    (2, n_docs, self.retrieval_vector_size)   )
														self.assertEqual(len(lowerCamelCase_   )					,    2   )
														self.assertEqual(sorted(doc_dicts[0]   )					,    ['''embeddings''', '''id''', '''text''', '''title''']   )
														self.assertEqual(len(doc_dicts[0]['''id''']   )					,    lowerCamelCase_   )
														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   __SCREAMING_SNAKE_CASE							(		self   )       ->							Optional[Any]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )







							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )
														ray.shutdown()







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							int:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	np.array(
														    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]					,    dtype=np.floataa   )
														with self.assertRaises(lowerCamelCase_   ):
																					self.distributed_retriever_check(
																					    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_					,    )
														ray.shutdown()







							@require_ray
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[str]:
														lowerCAmelCase__          =	1
														lowerCAmelCase__          =	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=lowerCamelCase_					,    from_disk=lowerCamelCase_   )					,    lowerCamelCase_					,    lowerCamelCase_   )
														ray.shutdown()