
"""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        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = AutoencoderKL
		lowercase__         = 'sample'
		lowercase__         = 1E-2



		@property
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    4
							_UpperCamelCase									=    3
							_UpperCamelCase									=    (32, 32)

							_UpperCamelCase									=    floats_tensor((batch_size, num_channels) + sizes).to(__a)

							return {"sample": image}



		@property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							return (3, 32, 32)



		@property
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return (3, 32, 32)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''block_out_channels''': [32, 64],
							    '''in_channels''': 3,
							    '''out_channels''': 3,
							    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
							    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
							    '''latent_channels''': 4,
							}
							_UpperCamelCase									=    self.dummy_input
							return init_dict, inputs_dict



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							pass



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






							'''simple docstring'''
							pass



		@unittest.skipIf(torch_device == '''mps'''							,						'''Gradient checkpointing skipped on MPS''')
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							# enable deterministic behavior for gradient checkpointing
							_UpperCamelCase					,    _UpperCamelCase									=    self.prepare_init_args_and_inputs_for_common()
							_UpperCamelCase									=    self.model_class(**__a)
							model.to(__a)

							assert not model.is_gradient_checkpointing and model.training

							_UpperCamelCase									=    model(**__a).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()

							_UpperCamelCase									=    torch.randn_like(__a)
							_UpperCamelCase									=    (out - labels).mean()
							loss.backward()

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

							assert model_a.is_gradient_checkpointing and model_a.training

							_UpperCamelCase									=    model_a(**__a).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()
							_UpperCamelCase									=    (out_a - labels).mean()
							loss_a.backward()

							# compare the output and parameters gradients
							self.assertTrue((loss - loss_a).abs() < 1e-5)
							_UpperCamelCase									=    dict(model.named_parameters())
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''							,						output_loading_info=__a)
							self.assertIsNotNone(__a)
							self.assertEqual(len(loading_info['''missing_keys'''])							,						0)

							model.to(__a)
							_UpperCamelCase									=    model(**self.dummy_input)

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




		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''')
							_UpperCamelCase									=    model.to(__a)
							model.eval()

							if torch_device == "mps":
												_UpperCamelCase									=    torch.manual_seed(0)
							else:
												_UpperCamelCase									=    torch.Generator(device=__a).manual_seed(0)

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

							_UpperCamelCase									=    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":
												_UpperCamelCase									=    torch.tensor(
												    [
												        -4.0_078e-01,
												        -3.8_323e-04,
												        -1.2_681e-01,
												        -1.1_462e-01,
												        2.0_095e-01,
												        1.0_893e-01,
												        -8.8_247e-02,
												        -3.0_361e-01,
												        -9.8_644e-03,
												    ])
							elif torch_device == "cpu":
												_UpperCamelCase									=    torch.tensor(
												    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026])
							else:
												_UpperCamelCase									=    torch.tensor(
												    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485])

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






@slow
class        _UpperCAmelCase(       unittest.TestCase      ):



		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							return F'''gaussian_noise_s={seed}_shape={"_".join([str(__a) for s in shape])}.npy'''



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 		UpperCAmelCase				(   self							,						__a=0							,						__a=(4, 3, 5_12, 5_12)							,						__a=False)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    torch.floataa if fpaa else torch.floataa
							_UpperCamelCase									=    torch.from_numpy(load_hf_numpy(self.get_file_format(__a							,						__a))).to(__a).to(__a)
							return image



		def 		UpperCAmelCase				(   self							,						__a="CompVis/stable-diffusion-v1-4"							,						__a=False)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    '''fp16''' if fpaa else None
							_UpperCamelCase									=    torch.floataa if fpaa else torch.floataa

							_UpperCamelCase									=    AutoencoderKL.from_pretrained(
							    __a							,						subfolder='''vae'''							,						torch_dtype=__a							,						revision=__a							,						)
							model.to(__a).eval()

							return model



		def 		UpperCAmelCase				(   self							,						__a=0)			->			Optional[Any]:






							'''simple docstring'''
							if torch_device == "mps":
												return torch.manual_seed(__a)
							return torch.Generator(device=__a).manual_seed(__a)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ])
		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a)
							_UpperCamelCase									=    self.get_generator(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(__a							,						generator=__a							,						sample_posterior=__a).sample

							assert sample.shape == image.shape

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

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



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
		        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
		        # fmt: on
		    ])
		@require_torch_gpu
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model(fpaa=__a)
							_UpperCamelCase									=    self.get_sd_image(__a							,						fpaa=__a)
							_UpperCamelCase									=    self.get_generator(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(__a							,						generator=__a							,						sample_posterior=__a).sample

							assert sample.shape == image.shape

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

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



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ])
		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model(__a).sample

							assert sample.shape == image.shape

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

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



		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
		        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
		        # fmt: on
		    ])
		@require_torch_gpu
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64))

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

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

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

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



		@parameterized.expand(
		    [
		        # fmt: off
		        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
		        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
		        # fmt: on
		    ])
		@require_torch_gpu
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model(fpaa=__a)
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64)							,						fpaa=__a)

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

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

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

							assert torch_all_close(__a							,						__a							,						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 		UpperCAmelCase				(   self							,						__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model(fpaa=__a)
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64)							,						fpaa=__a)

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

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

							assert torch_all_close(__a							,						__a							,						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 		UpperCAmelCase				(   self							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a							,						shape=(3, 4, 64, 64))

							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
												_UpperCamelCase									=    model.decode(__a).sample

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

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




		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
		        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
		        # fmt: on
		    ])
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_sd_vae_model()
							_UpperCamelCase									=    self.get_sd_image(__a)
							_UpperCamelCase									=    self.get_generator(__a)

							with torch.no_grad():
												_UpperCamelCase									=    model.encode(__a).latent_dist
												_UpperCamelCase									=    dist.sample(generator=__a)

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

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

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














'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


__lowerCAmelCase									=   logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowerCAmelCase									=   'cuda' if torch.cuda.is_available() else 'cpu'





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Optional[Any]=1_00    ,		__A				: int=" "   ):
				a_		:	Optional[int]									=   text.split(__A   )
				return [character.join(text[i : i + n]   ).strip() for i in range(0    ,		len(__A   )    ,		__A   )]





def 		_UpperCAmelCase (				__A				: dict   ):
				a_					,      a_		:	List[Any]									=   [], []
				for title, text in zip(documents['''title''']    ,		documents['''text''']   ):
								if text is not None:
												for passage in split_text(__A   ):
																titles.append(title if title is not None else ''''''   )
																texts.append(__A   )
				return {"title": titles, "text": texts}





def 		_UpperCAmelCase (				__A				: dict    ,		__A				: DPRContextEncoder    ,		__A				: DPRContextEncoderTokenizerFast   ):
				a_		:	int									=   ctx_tokenizer(
				    documents['''title''']    ,		documents['''text''']    ,		truncation=__A    ,		padding='''longest'''    ,		return_tensors='''pt'''   )['''input_ids''']
				a_		:	List[Any]									=   ctx_encoder(input_ids.to(device=__A   )    ,		return_dict=__A   ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}





def 		_UpperCAmelCase (				__A				: "RagExampleArguments"    ,		__A				: "ProcessingArguments"    ,		__A				: "IndexHnswArguments"    ,		):
				######################################
				logger.info('''Step 1 - Create the dataset'''   )
				######################################

				# The dataset needed for RAG must have three columns:
				# - title (string): title of the document
				# - text (string): text of a passage of the document
				# - embeddings (array of dimension d): DPR representation of the passage
				# Let's say you have documents in tab-separated csv files with columns "title" and "text"
				assert os.path.isfile(rag_example_args.csv_path   ), "Please provide a valid path to a csv file"

				# You can load a Dataset object this way
				a_		:	List[str]									=   load_dataset(
				    '''csv'''    ,		data_files=[rag_example_args.csv_path]    ,		split='''train'''    ,		delimiter='''\t'''    ,		column_names=['''title''', '''text''']   )

				# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

				# Then split the documents into passages of 100 words
				a_		:	Dict									=   dataset.map(__A    ,		batched=__A    ,		num_proc=processing_args.num_proc   )

				# And compute the embeddings
				a_		:	str									=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__A   )
				a_		:	Tuple									=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
				a_		:	Union[str, Any]									=   Features(
				    {'''text''': Value('''string'''   ), '''title''': Value('''string'''   ), '''embeddings''': Sequence(Value('''float32'''   )   )}   )  # optional, save as float32 instead of float64 to save space
				a_		:	Union[str, Any]									=   dataset.map(
				    partial(__A    ,		ctx_encoder=__A    ,		ctx_tokenizer=__A   )    ,		batched=__A    ,		batch_size=processing_args.batch_size    ,		features=__A    ,		)

				# And finally save your dataset
				a_		:	Tuple									=   os.path.join(rag_example_args.output_dir    ,		'''my_knowledge_dataset'''   )
				dataset.save_to_disk(__A   )
				# from datasets import load_from_disk
				# dataset = load_from_disk(passages_path)  # to reload the dataset

				######################################
				logger.info('''Step 2 - Index the dataset'''   )
				######################################

				# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
				a_		:	Dict									=   faiss.IndexHNSWFlat(index_hnsw_args.d    ,		index_hnsw_args.m    ,		faiss.METRIC_INNER_PRODUCT   )
				dataset.add_faiss_index('''embeddings'''    ,		custom_index=__A   )

				# And save the index
				a_		:	Optional[Any]									=   os.path.join(rag_example_args.output_dir    ,		'''my_knowledge_dataset_hnsw_index.faiss'''   )
				dataset.get_index('''embeddings'''   ).save(__A   )
				# dataset.load_faiss_index("embeddings", index_path)  # to reload the index





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=str(Path(SCREAMING_SNAKE_CASE_							).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"							)						, metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}						, )
			snake_case__	=					field(
			    default="facebook/rag-sequence-nq"						, metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}						, )
			snake_case__	=					field(
			    default="facebook/dpr-ctx_encoder-multiset-base"						, metadata={
			        "help": (
			            "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
			            " 'facebook/dpr-ctx_encoder-multiset-base'"
			        )
			    }						, )
			snake_case__	=					field(
			    default=str(Path(SCREAMING_SNAKE_CASE_							).parent / "test_run" / "dummy-kb"							)						, metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}						, )





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={
			        "help": "The number of processes to use to split the documents into passages. Default is single process."
			    }						, )
			snake_case__	=					field(
			    default=16						, metadata={
			        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
			    }						, )





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=768						, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}						, )
			snake_case__	=					field(
			    default=128						, metadata={
			        "help": (
			            "The number of bi-directional links created for every new element during the HNSW index construction."
			        )
			    }						, )


if __name__ == "__main__":
		logging.basicConfig(level=logging.WARNING)
		logger.setLevel(logging.INFO)

		__lowerCAmelCase									=   HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
		__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase									=   parser.parse_args_into_dataclasses()
		with TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase									=   rag_example_args.output_dir or tmp_dir
				main(rag_example_args, processing_args, index_hnsw_args)




