

"""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,
)


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



def        _lowerCAmelCase      (						UpperCAmelCase     :							str      ,							UpperCAmelCase     :							List[str]=100      ,							UpperCAmelCase     :							Optional[int]=" "				):


					'''simple docstring'''






					UpperCamelCase__		:    List[str]         =text.split(UpperCAmelCase				)
					return [character.join(text[i : i + n]				).strip() for i in range(0      ,							len(UpperCAmelCase				)      ,							UpperCAmelCase				)]



def        _lowerCAmelCase      (						UpperCAmelCase     :							dict				):


					'''simple docstring'''






					UpperCamelCase__  ,			UpperCamelCase__		:    int         =[], []
					for title, text in zip(documents['''title''']      ,							documents['''text''']				):
										if text is not None:
															for passage in split_text(UpperCAmelCase				):
																				titles.append(title if title is not None else ''''''				)
																				texts.append(UpperCAmelCase				)
					return {"title": titles, "text": texts}



def        _lowerCAmelCase      (						UpperCAmelCase     :							dict      ,							UpperCAmelCase     :							DPRContextEncoder      ,							UpperCAmelCase     :							DPRContextEncoderTokenizerFast				):


					'''simple docstring'''






					UpperCamelCase__		:    Dict         =ctx_tokenizer(
					    documents['''title''']      ,							documents['''text''']      ,							truncation=UpperCAmelCase      ,							padding='''longest'''      ,							return_tensors='''pt'''				)['''input_ids''']
					UpperCamelCase__		:    Any         =ctx_encoder(input_ids.to(device=UpperCAmelCase				)      ,							return_dict=UpperCAmelCase				).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}



def        _lowerCAmelCase      (						UpperCAmelCase     :							"RagExampleArguments"      ,							UpperCAmelCase     :							"ProcessingArguments"      ,							UpperCAmelCase     :							"IndexHnswArguments"      ,							):


					'''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
					UpperCamelCase__		:    int         =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__		:    Optional[int]         =dataset.map(UpperCAmelCase      ,							batched=UpperCAmelCase      ,							num_proc=processing_args.num_proc				)

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

					# And finally save your dataset
					UpperCamelCase__		:    Dict         =os.path.join(rag_example_args.output_dir      ,							'''my_knowledge_dataset'''				)
					dataset.save_to_disk(UpperCAmelCase				)
					# 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[str]         =faiss.IndexHNSWFlat(index_hnsw_args.d      ,							index_hnsw_args.m      ,							faiss.METRIC_INNER_PRODUCT				)
					dataset.add_faiss_index('''embeddings'''      ,							custom_index=UpperCAmelCase				)

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

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

					# Easy way to load the model
					UpperCamelCase__		:    int         =RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name      ,							index_name='''custom'''      ,							indexed_dataset=UpperCAmelCase				)
					UpperCamelCase__		:    Union[str, Any]         =RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,							retriever=UpperCAmelCase				)
					UpperCamelCase__		:    List[str]         =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__		:    Dict         =rag_example_args.question or '''What does Moses\' rod turn into ?'''
					UpperCamelCase__		:    Any         =tokenizer.question_encoder(UpperCAmelCase      ,							return_tensors='''pt'''				)['''input_ids''']
					UpperCamelCase__		:    List[Any]         =model.generate(UpperCAmelCase				)
					UpperCamelCase__		:    Optional[int]         =tokenizer.batch_decode(UpperCAmelCase      ,							skip_special_tokens=UpperCAmelCase				)[0]
					logger.info('''Q: ''' + question				)
					logger.info('''A: ''' + generated_string				)

@dataclass
class     __a   :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						field(
			    default=str(Path(snake_case__    ).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=snake_case__,    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=snake_case__,    metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'},    )



@dataclass
class     __a   :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						field(
			    default=snake_case__,    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     __a   :

			"""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)

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





"""simple docstring"""





import math as m



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]=0.001				):


					'''simple docstring'''






					return (f(a + h				) - f(a - h				)) / (2 * h)



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]=0      ,							UpperCAmelCase     :							Optional[int]=100      ,							UpperCAmelCase     :							Union[str, Any]=0.0001      ,							UpperCAmelCase     :							Dict=1E-6      ,							UpperCAmelCase     :							Any=False				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =xa  # set the initial guess
					UpperCamelCase__		:    List[Any]         =[a]
					UpperCamelCase__		:    Optional[Any]         =abs(f(UpperCAmelCase				)				)
					UpperCamelCase__		:    List[str]         =lambda UpperCAmelCase				: calc_derivative(UpperCAmelCase      ,							UpperCAmelCase      ,							h=UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
					for _ in range(UpperCAmelCase				):
										if fa(UpperCAmelCase				) == 0:
															raise ValueError('''No converging solution found'''				)
										UpperCamelCase__		:    Any         =a - f(UpperCAmelCase				) / fa(UpperCAmelCase				)  # Calculate the next estimate
										if logsteps:
															steps.append(UpperCAmelCase				)
										if error < maxerror:
															break
					else:
										raise ValueError('''Iteration limit reached, no converging solution found'''				)
					if logsteps:
										# If logstep is true, then log intermediate steps
										return a, error, steps
					return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				_SCREAMING_SNAKE_CASE       :  str        = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Any        = newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel("""step""")
				plt.ylabel("""error""")
				plt.show()
				print(F'''solution = {{{solution:f}}}, error = {{{error:f}}}''')


