


'''simple docstring'''






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class 	a__       (							a__       ,       a__       ,       unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     int					=   AutoencoderKL
							lowercase__    :     List[Any]					=   "sample"
							lowercase__    :     str					=   1e-2







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			4
														lowerCAmelCase__      =			3
														lowerCAmelCase__      =			(32, 32)

														lowerCAmelCase__      =			floats_tensor((batch_size, num_channels) + sizes     ).to(lowerCamelCase_     )

														return {"sample": image}







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														return (3, 32, 32)







							@property
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														return (3, 32, 32)







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			{
														    '''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__      =			self.dummy_input
														return init_dict, inputs_dict







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														pass







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														pass







							@unittest.skipIf(torch_device == '''mps'''      ,					'''Gradient checkpointing skipped on MPS'''     )
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														# enable deterministic behavior for gradient checkpointing
														lowerCAmelCase__     ,      lowerCAmelCase__      =			self.prepare_init_args_and_inputs_for_common()
														lowerCAmelCase__      =			self.model_class(**lowerCamelCase_     )
														model.to(lowerCamelCase_     )

														assert not model.is_gradient_checkpointing and model.training

														lowerCAmelCase__      =			model(**lowerCamelCase_     ).sample
														# run the backwards pass on the model. For backwards pass, for simplicity purpose,
														# we won't calculate the loss and rather backprop on out.sum()
														model.zero_grad()

														lowerCAmelCase__      =			torch.randn_like(lowerCamelCase_     )
														lowerCAmelCase__      =			(out - labels).mean()
														loss.backward()

														# re-instantiate the model now enabling gradient checkpointing
														lowerCAmelCase__      =			self.model_class(**lowerCamelCase_     )
														# clone model
														model_a.load_state_dict(model.state_dict()     )
														model_a.to(lowerCamelCase_     )
														model_a.enable_gradient_checkpointing()

														assert model_a.is_gradient_checkpointing and model_a.training

														lowerCAmelCase__      =			model_a(**lowerCamelCase_     ).sample
														# run the backwards pass on the model. For backwards pass, for simplicity purpose,
														# we won't calculate the loss and rather backprop on out.sum()
														model_a.zero_grad()
														lowerCAmelCase__      =			(out_a - labels).mean()
														loss_a.backward()

														# compare the output and parameters gradients
														self.assertTrue((loss - loss_a).abs() < 1e-5     )
														lowerCAmelCase__      =			dict(model.named_parameters()     )
														lowerCAmelCase__      =			dict(model_a.named_parameters()     )
														for name, param in named_params.items():
																					self.assertTrue(torch_all_close(param.grad.data      ,					named_params_a[name].grad.data      ,					atol=5e-5     )     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''      ,					output_loading_info=lowerCamelCase_     )
														self.assertIsNotNone(lowerCamelCase_     )
														self.assertEqual(len(loading_info['''missing_keys''']     )      ,					0     )

														model.to(lowerCamelCase_     )
														lowerCAmelCase__      =			model(**self.dummy_input     )

														assert image is not None, "Make sure output is not None"







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														lowerCAmelCase__      =			AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''     )
														lowerCAmelCase__      =			model.to(lowerCamelCase_     )
														model.eval()

														if torch_device == "mps":
																					lowerCAmelCase__      =			torch.manual_seed(0     )
														else:
																					lowerCAmelCase__      =			torch.Generator(device=lowerCamelCase_     ).manual_seed(0     )

														lowerCAmelCase__      =			torch.randn(
														    1      ,					model.config.in_channels      ,					model.config.sample_size      ,					model.config.sample_size      ,					generator=torch.manual_seed(0     )      ,					)
														lowerCAmelCase__      =			image.to(lowerCamelCase_     )
														with torch.no_grad():
																					lowerCAmelCase__      =			model(lowerCamelCase_      ,					sample_posterior=lowerCamelCase_      ,					generator=lowerCamelCase_     ).sample

														lowerCAmelCase__      =			output[0, -1, -3:, -3:].flatten().cpu()

														# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
														# the expected output slices are not the same for CPU and GPU.
														if torch_device == "mps":
																					lowerCAmelCase__      =			torch.tensor(
																					    [
																					        -4.00_78e-01,
																					        -3.83_23e-04,
																					        -1.26_81e-01,
																					        -1.14_62e-01,
																					        2.00_95e-01,
																					        1.08_93e-01,
																					        -8.82_47e-02,
																					        -3.03_61e-01,
																					        -9.86_44e-03,
																					    ]     )
														elif torch_device == "cpu":
																					lowerCAmelCase__      =			torch.tensor(
																					    [-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026]     )
														else:
																					lowerCAmelCase__      =			torch.tensor(
																					    [-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485]     )

														self.assertTrue(torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					rtol=1e-2     )     )




@slow
class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							int:
														return F"""gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_     ) for s in shape]     )}.npy"""







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=0      ,					lowerCamelCase_=(4, 3, 5_12, 5_12)      ,					lowerCamelCase_=False     ) ->							List[Any]:
														lowerCAmelCase__      =			torch.floataa if fpaa else torch.floataa
														lowerCAmelCase__      =			torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_      ,					lowerCamelCase_     )     )     ).to(lowerCamelCase_     ).to(lowerCamelCase_     )
														return image







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_="CompVis/stable-diffusion-v1-4"      ,					lowerCamelCase_=False     ) ->							str:
														lowerCAmelCase__      =			'''fp16''' if fpaa else None
														lowerCAmelCase__      =			torch.floataa if fpaa else torch.floataa

														lowerCAmelCase__      =			AutoencoderKL.from_pretrained(
														    lowerCamelCase_      ,					subfolder='''vae'''      ,					torch_dtype=lowerCamelCase_      ,					revision=lowerCamelCase_      ,					)
														model.to(lowerCamelCase_     ).eval()

														return model







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=0     ) ->							List[Any]:
														if torch_device == "mps":
																					return torch.manual_seed(lowerCamelCase_     )
														return torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
							        [47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
							        # fmt: on
							    ]     )
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple:
														lowerCAmelCase__      =			self.get_sd_vae_model()
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_     )
														lowerCAmelCase__      =			self.get_generator(lowerCamelCase_     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model(lowerCamelCase_      ,					generator=lowerCamelCase_      ,					sample_posterior=lowerCamelCase_     ).sample

														assert sample.shape == image.shape

														lowerCAmelCase__      =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
														lowerCAmelCase__      =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice     )

														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=3e-3     )







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
							        [47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
							        # fmt: on
							    ]     )
							@require_torch_gpu
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			self.get_sd_vae_model(fpaa=lowerCamelCase_     )
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					fpaa=lowerCamelCase_     )
														lowerCAmelCase__      =			self.get_generator(lowerCamelCase_     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model(lowerCamelCase_      ,					generator=lowerCamelCase_      ,					sample_posterior=lowerCamelCase_     ).sample

														assert sample.shape == image.shape

														lowerCAmelCase__      =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
														lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-2     )







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
							        [47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
							        # fmt: on
							    ]     )
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			self.get_sd_vae_model()
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model(lowerCamelCase_     ).sample

														assert sample.shape == image.shape

														lowerCAmelCase__      =			sample[-1, -2:, -2:, :2].flatten().float().cpu()
														lowerCAmelCase__      =			torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice     )

														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=3e-3     )







							@parameterized.expand(
							    [
							        # fmt: off
							        [13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
							        [37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
							        # fmt: on
							    ]     )
							@require_torch_gpu
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Optional[int]:
														lowerCAmelCase__      =			self.get_sd_vae_model()
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

														assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

														lowerCAmelCase__      =			sample[-1, -2:, :2, -2:].flatten().cpu()
														lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-3     )







							@parameterized.expand(
							    [
							        # fmt: off
							        [27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
							        [16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
							        # fmt: on
							    ]     )
							@require_torch_gpu
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Dict:
														lowerCAmelCase__      =			self.get_sd_vae_model(fpaa=lowerCamelCase_     )
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)      ,					fpaa=lowerCamelCase_     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

														assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

														lowerCAmelCase__      =			sample[-1, -2:, :2, -2:].flatten().float().cpu()
														lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=5e-3     )







							@parameterized.expand([(13,), (16,), (27,)]     )
							@require_torch_gpu
							@unittest.skipIf(not is_xformers_available()      ,					reason='''xformers is not required when using PyTorch 2.0.'''     )
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							int:
														lowerCAmelCase__      =			self.get_sd_vae_model(fpaa=lowerCamelCase_     )
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)      ,					fpaa=lowerCamelCase_     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

														assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-1     )







							@parameterized.expand([(13,), (16,), (37,)]     )
							@require_torch_gpu
							@unittest.skipIf(not is_xformers_available()      ,					reason='''xformers is not required when using PyTorch 2.0.'''     )
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Any:
														lowerCAmelCase__      =			self.get_sd_vae_model()
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_      ,					shape=(3, 4, 64, 64)     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

														model.enable_xformers_memory_efficient_attention()
														with torch.no_grad():
																					lowerCAmelCase__      =			model.decode(lowerCamelCase_     ).sample

														assert list(sample.shape     ) == [3, 3, 5_12, 5_12]

														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=1e-2     )







							@parameterized.expand(
							    [
							        # fmt: off
							        [33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
							        [47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
							        # fmt: on
							    ]     )
							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							List[Any]:
														lowerCAmelCase__      =			self.get_sd_vae_model()
														lowerCAmelCase__      =			self.get_sd_image(lowerCamelCase_     )
														lowerCAmelCase__      =			self.get_generator(lowerCamelCase_     )

														with torch.no_grad():
																					lowerCAmelCase__      =			model.encode(lowerCamelCase_     ).latent_dist
																					lowerCAmelCase__      =			dist.sample(generator=lowerCamelCase_     )

														assert list(sample.shape     ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

														lowerCAmelCase__      =			sample[0, -1, -3:, -3:].flatten().cpu()
														lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     )

														lowerCAmelCase__      =			3e-3 if torch_device != '''mps''' else 1e-2
														assert torch_all_close(lowerCamelCase_      ,					lowerCamelCase_      ,					atol=lowerCamelCase_     )



'''simple docstring'''






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


__UpperCAmelCase					       =							logging.getLogger(__name__)




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_=None     ) ->							Dict:
														super().__init__(
														    lowerCamelCase_      ,					question_encoder_tokenizer=lowerCamelCase_      ,					generator_tokenizer=lowerCamelCase_      ,					index=lowerCamelCase_      ,					init_retrieval=lowerCamelCase_      ,					)
														lowerCAmelCase__      =			None







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							str:
														logger.info('''initializing retrieval'''     )

														# initializing a separate process group for retrieval as the default
														# nccl backend doesn't support gather/scatter operations while gloo
														# is too slow to replace nccl for the core gpu communication
														if dist.is_initialized():
																					logger.info('''dist initialized'''     )
																					# needs to be set manually
																					lowerCAmelCase__      =			self._infer_socket_ifname()
																					# avoid clash with the NCCL port
																					lowerCAmelCase__      =			str(distributed_port + 1     )
																					lowerCAmelCase__      =			dist.new_group(ranks=lowerCamelCase_      ,					backend='''gloo'''     )

														# initialize retriever only on the main worker
														if not dist.is_initialized() or self._is_main():
																					logger.info('''dist not initialized / main'''     )
																					self.index.init_index()

														# all processes wait untill the retriever is initialized by the main process
														if dist.is_initialized():
																					torch.distributed.barrier(group=self.process_group     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														return dist.get_rank(group=self.process_group     ) == 0







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_=torch.floataa     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			torch.empty(lowerCamelCase_      ,					dtype=lowerCamelCase_     )
														dist.scatter(lowerCamelCase_      ,					src=0      ,					scatter_list=lowerCamelCase_      ,					group=self.process_group     )
														return target_tensor







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			psutil.net_if_addrs()
														# a hacky way to deal with varying network interface names
														lowerCAmelCase__      =			next((addr for addr in addrs if addr.startswith('''e'''     ))      ,					lowerCamelCase_     )
														return ifname







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple[np.ndarray, List[dict]]:

														# single GPU training
														if not dist.is_initialized():
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			self._main_retrieve(lowerCamelCase_      ,					lowerCamelCase_     )
																					return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase_     )

														# distributed training
														lowerCAmelCase__      =			dist.get_world_size(group=self.process_group     )

														# gather logic
														lowerCAmelCase__      =			None
														if self._is_main():
																					lowerCAmelCase__      =			[torch.empty(question_hidden_states.shape      ,					dtype=torch.floataa     ) for _ in range(lowerCamelCase_     )]
														dist.gather(torch.tensor(lowerCamelCase_     )      ,					dst=0      ,					gather_list=lowerCamelCase_      ,					group=self.process_group     )

														# scatter logic
														lowerCAmelCase__      =			question_hidden_states.shape[0]
														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			[]
														if self._is_main():
																					assert len(lowerCamelCase_     ) == world_size
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			self._main_retrieve(torch.cat(lowerCamelCase_     ).numpy()      ,					lowerCamelCase_     )
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			torch.tensor(lowerCamelCase_     ), torch.tensor(lowerCamelCase_     )
																					lowerCAmelCase__      =			self._chunk_tensor(lowerCamelCase_      ,					lowerCamelCase_     )
																					lowerCAmelCase__      =			self._chunk_tensor(lowerCamelCase_      ,					lowerCamelCase_     )
														lowerCAmelCase__      =			self._scattered(lowerCamelCase_      ,					[n_queries, n_docs]      ,					target_type=torch.intaa     )
														lowerCAmelCase__      =			self._scattered(lowerCamelCase_      ,					[n_queries, n_docs, question_hidden_states.shape[1]]     )

														return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(lowerCamelCase_     )