

"""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,
)


__A	:       Tuple        =						logging.getLogger(__name__)
torch.set_grad_enabled(False)
__A	:       Optional[Any]        =						'cuda' if torch.cuda.is_available() else 'cpu'
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=100     ,				lowercase__=" " ):
					"""simple docstring"""
					A     =   text.split(lowercase__ )
					return [character.join(text[i : i + n] ).strip() for i in range(0     ,				len(lowercase__ )     ,				lowercase__ )]
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     ,       A     =   [], []
					for title, text in zip(documents["title"]     ,				documents["text"] ):
										if text is not None:
															for passage in split_text(lowercase__ ):
																				titles.append(title if title is not None else "" )
																				texts.append(lowercase__ )
					return {"title": titles, "text": texts}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   ctx_tokenizer(
					    documents["title"]     ,				documents["text"]     ,				truncation=lowercase__     ,				padding="longest"     ,				return_tensors="pt" )["input_ids"]
					A     =   ctx_encoder(input_ids.to(device=lowercase__ )     ,				return_dict=lowercase__ ).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				):
					"""simple docstring"""
					######################################
					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     =   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     =   dataset.map(lowercase__     ,				batched=lowercase__     ,				num_proc=processing_args.num_proc )

					# And compute the embeddings
					A     =   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=lowercase__ )
					A     =   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
					A     =   Features(
					    {"text": Value("string" ), "title": Value("string" ), "embeddings": Sequence(Value("float32" ) )} )  # optional, save as float32 instead of float64 to save space
					A     =   dataset.map(
					    partial(lowercase__     ,				ctx_encoder=lowercase__     ,				ctx_tokenizer=lowercase__ )     ,				batched=lowercase__     ,				batch_size=processing_args.batch_size     ,				features=lowercase__     ,				)

					# And finally save your dataset
					A     =   os.path.join(rag_example_args.output_dir     ,				"my_knowledge_dataset" )
					dataset.save_to_disk(lowercase__ )
					# 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     =   faiss.IndexHNSWFlat(index_hnsw_args.d     ,				index_hnsw_args.m     ,				faiss.METRIC_INNER_PRODUCT )
					dataset.add_faiss_index("embeddings"     ,				custom_index=lowercase__ )

					# And save the index
					A     =   os.path.join(rag_example_args.output_dir     ,				"my_knowledge_dataset_hnsw_index.faiss" )
					dataset.get_index("embeddings" ).save(lowercase__ )
					# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

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

					# Easy way to load the model
					A     =   RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name     ,				index_name="custom"     ,				indexed_dataset=lowercase__ )
					A     =   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name     ,				retriever=lowercase__ )
					A     =   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" )
					######################################

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





@dataclass
class      __UpperCamelCase					:
		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      __UpperCamelCase					:
		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      __UpperCamelCase					:
		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)

	__A	:       List[str]        =						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	__A    ,       __A    ,       __A	:       Dict        =						parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		__A	:       int        =						rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)












"""simple docstring"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






       def 				__A			( self  ):
              for timesteps in [10, 50, 1_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=a__  )






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






       def 				__A			( self  ):
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=a__  )






       def 				__A			( self  ):
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=a__  )






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






