





'''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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCAmelCase__	:					List[str]	     =       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCAmelCase__	:					Union[str, Any]	     =       "cuda" if torch.cuda.is_available() else "cpu"


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase=100,    _UpperCAmelCase=" "   ):
		__UpperCAmelCase     :				int					   =			text.split(A__   )
		return [character.join(text[i : i + n]   ).strip() for i in range(0,    len(A__   ),    A__   )]


def 		__UpperCamelCase		(       _UpperCAmelCase   ):
		__UpperCAmelCase   ,       __UpperCAmelCase     :				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		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				List[str]					   =			ctx_tokenizer(
		    documents["title"],    documents["text"],    truncation=A__,    padding="longest",    return_tensors="pt"   )["input_ids"]
		__UpperCAmelCase     :				Optional[int]					   =			ctx_encoder(input_ids.to(device=A__   ),    return_dict=A__   ).pooler_output
		return {"embeddings": embeddings.detach().cpu().numpy()}


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase,    ):
		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
		__UpperCAmelCase     :				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
		__UpperCAmelCase     :				str					   =			dataset.map(A__,    batched=A__,    num_proc=processing_args.num_proc   )

		# And compute the embeddings
		__UpperCAmelCase     :				Union[str, Any]					   =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=A__   )
		__UpperCAmelCase     :				Dict					   =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
		__UpperCAmelCase     :				List[str]					   =			Features(
		    {"text": Value("string"   ), "title": Value("string"   ), "embeddings": Sequence(Value("float32"   )   )}   )  # optional, save as float32 instead of float64 to save space
		__UpperCAmelCase     :				str					   =			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
		__UpperCAmelCase     :				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
		__UpperCAmelCase     :				List[Any]					   =			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
		__UpperCAmelCase     :				Dict					   =			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

		######################################
		logger.info("Step 3 - Load RAG"   )
		######################################

		# Easy way to load the model
		__UpperCAmelCase     :				Any					   =			RagRetriever.from_pretrained(
		    rag_example_args.rag_model_name,    index_name="custom",    indexed_dataset=A__   )
		__UpperCAmelCase     :				List[str]					   =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name,    retriever=A__   )
		__UpperCAmelCase     :				Optional[int]					   =			RagTokenizer.from_pretrained(rag_example_args.rag_model_name   )

		# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
		# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

		######################################
		logger.info("Step 4 - Have fun"   )
		######################################

		__UpperCAmelCase     :				Optional[Any]					   =			rag_example_args.question or "What does Moses' rod turn into ?"
		__UpperCAmelCase     :				str					   =			tokenizer.question_encoder(A__,    return_tensors="pt"   )["input_ids"]
		__UpperCAmelCase     :				Dict					   =			model.generate(A__   )
		__UpperCAmelCase     :				Tuple					   =			tokenizer.batch_decode(A__,    skip_special_tokens=A__   )[0]
		logger.info("Q: " + question   )
		logger.info("A: " + generated_string   )


@dataclass
class 			SCREAMING_SNAKE_CASE__	:







					"""simple docstring"""






					SCREAMING_SNAKE_CASE				=     field(
					    default=str(Path(A_						).parent / '''test_data''' / '''my_knowledge_dataset.csv'''						)				,metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}				,)
					SCREAMING_SNAKE_CASE				=     field(
					    default=A_				,metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}				,)
					SCREAMING_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\''''}				,)
					SCREAMING_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\''''
					        )
					    }				,)
					SCREAMING_SNAKE_CASE				=     field(
					    default=A_				,metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}				,)






@dataclass
class 			SCREAMING_SNAKE_CASE__	:







					"""simple docstring"""






					SCREAMING_SNAKE_CASE				=     field(
					    default=A_				,metadata={
					        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
					    }				,)
					SCREAMING_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__	:







					"""simple docstring"""






					SCREAMING_SNAKE_CASE				=     field(
					    default=768				,metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}				,)
					SCREAMING_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__	:					Tuple	     =       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
			lowerCAmelCase__     ,       lowerCAmelCase__     ,       lowerCAmelCase__	:					Union[str, Any]	     =       parser.parse_args_into_dataclasses()
			with TemporaryDirectory() as tmp_dir:
						lowerCAmelCase__	:					Dict	     =       rag_example_args.output_dir or tmp_dir
						main(rag_example_args, processing_args, index_hnsw_args)






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class      lowerCamelCase		(  A_							, unittest.TestCase  ):
     UpperCAmelCase__				:							Tuple           =		KandinskyPriorPipeline
     UpperCAmelCase__				:							List[str]           =		["prompt"]
     UpperCAmelCase__				:							List[str]           =		["prompt", "negative_prompt"]
     UpperCAmelCase__				:							List[str]           =		[
         "num_images_per_prompt",
         "generator",
         "num_inference_steps",
         "latents",
         "negative_prompt",
         "guidance_scale",
         "output_type",
         "return_dict",
     ]
     UpperCAmelCase__				:							str           =		False





     @property
     def 		UpperCAmelCase(self      :    List[str]   )						->						Union[str, Any]:
         return 3_2





     @property
     def 		UpperCAmelCase(self      :    Any   )						->						Any:
         return 3_2





     @property
     def 		UpperCAmelCase(self      :    int   )						->						Dict:
         return self.time_input_dim





     @property
     def 		UpperCAmelCase(self      :    str   )						->						Tuple:
         return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase(self      :    Dict   )						->						Dict:
         return 1_0_0





     @property
     def 		UpperCAmelCase(self      :    Any   )						->						int:
         snake_case      =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
         return tokenizer





     @property
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						Optional[Any]:
         torch.manual_seed(0   )
         snake_case      =					CLIPTextConfig(
             bos_token_id=0				,    eos_token_id=2				,    hidden_size=self.text_embedder_hidden_size				,    projection_dim=self.text_embedder_hidden_size				,    intermediate_size=3_7				,    layer_norm_eps=1E-05				,    num_attention_heads=4				,    num_hidden_layers=5				,    pad_token_id=1				,    vocab_size=1_0_0_0				,    )
         return CLIPTextModelWithProjection(_A   )





     @property
     def 		UpperCAmelCase(self      :    List[Any]   )						->						Tuple:
         torch.manual_seed(0   )

         snake_case      =					{
             "num_attention_heads": 2,
             "attention_head_dim": 1_2,
             "embedding_dim": self.text_embedder_hidden_size,
             "num_layers": 1,
         }

         snake_case      =					PriorTransformer(**_A   )
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         snake_case      =					nn.Parameter(torch.ones(model.clip_std.shape   )   )
         return model





     @property
     def 		UpperCAmelCase(self      :    str   )						->						Union[str, Any]:
         torch.manual_seed(0   )
         snake_case      =					CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size				,    image_size=2_2_4				,    projection_dim=self.text_embedder_hidden_size				,    intermediate_size=3_7				,    num_attention_heads=4				,    num_channels=3				,    num_hidden_layers=5				,    patch_size=1_4				,    )

         snake_case      =					CLIPVisionModelWithProjection(_A   )
         return model





     @property
     def 		UpperCAmelCase(self      :    List[Any]   )						->						int:
         snake_case      =					CLIPImageProcessor(
             crop_size=2_2_4				,    do_center_crop=_A				,    do_normalize=_A				,    do_resize=_A				,    image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,    image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,    resample=3				,    size=2_2_4				,    )

         return image_processor





     def 		UpperCAmelCase(self      :    int   )						->						Union[str, Any]:
         snake_case      =					self.dummy_prior
         snake_case      =					self.dummy_image_encoder
         snake_case      =					self.dummy_text_encoder
         snake_case      =					self.dummy_tokenizer
         snake_case      =					self.dummy_image_processor

         snake_case      =					UnCLIPScheduler(
             variance_type="fixed_small_log"				,    prediction_type="sample"				,    num_train_timesteps=1_0_0_0				,    clip_sample=_A				,    clip_sample_range=10.0				,    )

         snake_case      =					{
             "prior": prior,
             "image_encoder": image_encoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "scheduler": scheduler,
             "image_processor": image_processor,
         }

         return components





     def 		UpperCAmelCase(self      :    Optional[int]				,    _A      :    Tuple				,    _A      :    str=0   )						->						Union[str, Any]:
         if str(_A   ).startswith("mps"   ):
             snake_case      =					torch.manual_seed(_A   )
         else:
             snake_case      =					torch.Generator(device=_A   ).manual_seed(_A   )
         snake_case      =					{
             "prompt": "horse",
             "generator": generator,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs





     def 		UpperCAmelCase(self      :    List[Any]   )						->						int:
         snake_case      =					"cpu"

         snake_case      =					self.get_dummy_components()

         snake_case      =					self.pipeline_class(**_A   )
         snake_case      =					pipe.to(_A   )

         pipe.set_progress_bar_config(disable=_A   )

         snake_case      =					pipe(**self.get_dummy_inputs(_A   )   )
         snake_case      =					output.image_embeds

         snake_case      =					pipe(
             **self.get_dummy_inputs(_A   )				,    return_dict=_A				,    )[0]

         snake_case      =					image[0, -1_0:]
         snake_case      =					image_from_tuple[0, -1_0:]

         assert image.shape == (1, 3_2)

         snake_case      =					np.array(
             [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2





     @skip_mps
     def 		UpperCAmelCase(self      :    List[Any]   )						->						str:
         snake_case      =					torch_device == "cpu"
         snake_case      =					True
         snake_case      =					False

         self._test_inference_batch_single_identical(
             test_max_difference=_A				,    relax_max_difference=_A				,    test_mean_pixel_difference=_A				,    )





     @skip_mps
     def 		UpperCAmelCase(self      :    Optional[int]   )						->						List[str]:
         snake_case      =					torch_device == "cpu"
         snake_case      =					False

         self._test_attention_slicing_forward_pass(
             test_max_difference=_A				,    test_mean_pixel_difference=_A				,    )

