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





def __A						(   a_ :							str		,		a_ :							Optional[Any]=1_00		,		a_ :							Dict=" "     )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= text.split(a_     )
		return [character.join(text[i : i + n]     ).strip() for i in range(0		,		len(a_     )		,		a_     )]





def __A						(   a_ :							dict     )->							dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     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 __A						(   a_ :							dict		,		a_ :							DPRContextEncoder		,		a_ :							DPRContextEncoderTokenizerFast     )->							dict:




		'''simple docstring'''






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





def __A						(   a_ :							"RagExampleArguments"		,		a_ :							"ProcessingArguments"		,		a_ :							"IndexHnswArguments"		,		)->							Optional[int]:




		'''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
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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
		SCREAMING_SNAKE_CASE       :     int				= dataset.map(a_		,		batched=a_		,		num_proc=processing_args.num_proc     )

		# And compute the embeddings
		SCREAMING_SNAKE_CASE       :     List[Any]				= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=a_     )
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     )
		SCREAMING_SNAKE_CASE       :     List[str]				= Features(
		    {'''text''': Value('''string'''     ), '''title''': Value('''string'''     ), '''embeddings''': Sequence(Value('''float32'''     )     )}     )  # optional, save as float32 instead of float64 to save space
		SCREAMING_SNAKE_CASE       :     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
		SCREAMING_SNAKE_CASE       :     Dict				= 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
		SCREAMING_SNAKE_CASE       :     Optional[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
		SCREAMING_SNAKE_CASE       :     Tuple				= 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 				lowercase__:
	UpperCamelCase				=  field(
	    default=str(Path(_UpperCAmelCase   ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""   )		,						metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}		,						)
	UpperCamelCase				=  field(
	    default=_UpperCAmelCase		,						metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}		,						)
	UpperCamelCase				=  field(
	    default="""facebook/rag-sequence-nq"""		,						metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}		,						)
	UpperCamelCase				=  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'"""
	        )
	    }		,						)
	UpperCamelCase				=  field(
	    default=str(Path(_UpperCAmelCase   ).parent / """test_run""" / """dummy-kb"""   )		,						metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}		,						)





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





@dataclass
class 				lowercase__:
	UpperCamelCase				=  field(
	    default=7_68		,						metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}		,						)
	UpperCamelCase				=  field(
	    default=1_28		,						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__ :       List[Any]						     =			parser.parse_args_into_dataclasses()
					with TemporaryDirectory() as tmp_dir:
										lowerCamelCase__ :       Optional[int]						     =			rag_example_args.output_dir or tmp_dir
										main(rag_example_args, processing_args, index_hnsw_args)







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__ :       Optional[int]						     =			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





def __A						(   a_ :							str		,		a_ :							list[str]		,		a_ :							bool = True     )->							tuple[int, int, str]:




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(a_     )
		# Verify that the target contains no genes besides the ones inside genes variable.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		for _ in range(a_     ):
				population.append(''''''.join([random.choice(a_     ) for i in range(len(a_     )     )]     )     )

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(a_     )

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

				# Check if there is a matching evolution.
				SCREAMING_SNAKE_CASE       :     List[Any]				= sorted(a_		,		key=lambda a_     : x[1]		,		reverse=a_     )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    F"\nGeneration: {generation}"
						    F"\nTotal Population:{total_population}"
						    F"\nBest score: {population_score[0][1]}"
						    F"\nBest string: {population_score[0][0]}"     )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= population[: int(N_POPULATION / 3     )]
				population.clear()
				population.extend(a_     )
				# Normalize population score to be between 0 and 1.
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [
				    (item, score / len(a_     )) for item, score in population_score
				]

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(a_     ) > N_POPULATION:
								break


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					lowerCamelCase__ :       int						     =			list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
					)
					lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__ :       Dict						     =			basic(target_str, genes_list)
					print(
					    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)





