

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,
)


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

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

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

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

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

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






@dataclass
class     lowerCamelCase_			:
	_lowercase		:      str    				=							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\''''}	,			)
	_lowercase		:      Optional[str]    				=							field(
	    default=a__	,			metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}	,			)
	_lowercase		:      str    				=							field(
	    default='''facebook/rag-sequence-nq'''	,			metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}	,			)
	_lowercase		:      str    				=							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\''''
	        )
	    }	,			)
	_lowercase		:      Optional[str]    				=							field(
	    default=a__	,			metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,			)






@dataclass
class     lowerCamelCase_			:
	_lowercase		:      Optional[int]    				=							field(
	    default=a__	,			metadata={
	        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
	    }	,			)
	_lowercase		:      int    				=							field(
	    default=16	,			metadata={
	        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
	    }	,			)






@dataclass
class     lowerCamelCase_			:
	_lowercase		:      int    				=							field(
	    default=768	,			metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}	,			)
	_lowercase		:      int    				=							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)

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








import math






class     lowerCamelCase_			:




	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
		__A   : Union[str, Any]								= x
		__A   : Union[str, Any]								= y
		__A   : Optional[int]								= step_size
		__A   : Dict								= function_to_optimize




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		return self.function(self.x				,			self.y       )




	def 					lowerCAmelCase_					(    self	:    int       ):
		__A   : Dict								= self.step_size
		return [
		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
		    for x, y in (
		        (self.x - step_size, self.y - step_size),
		        (self.x - step_size, self.y),
		        (self.x - step_size, self.y + step_size),
		        (self.x, self.y - step_size),
		        (self.x, self.y + step_size),
		        (self.x + step_size, self.y - step_size),
		        (self.x + step_size, self.y),
		        (self.x + step_size, self.y + step_size),
		    )
		]




	def __hash__(    self	:    str       ):
		return hash(str(self       )       )




	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
		if isinstance(__A				,			__A       ):
			return hash(str(self       )       ) == hash(str(__A       )       )
		return False




	def __str__(    self	:    List[Any]       ):
		return F"""x: {self.x} y: {self.y}"""






def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
	__A   : int								= search_prob
	__A   : List[str]								= []  # list to store the current score at each iteration
	__A   : Optional[Any]								= 0
	__A   : Any								= False
	__A   : Dict								= set()
	while not solution_found and iterations < max_iter:
		visited.add(a__					)
		iterations += 1
		__A   : Union[str, Any]								= current_state.score()
		scores.append(a__					)
		__A   : List[Any]								= current_state.get_neighbors()
		__A   : Tuple								= -math.inf
		__A   : str								= math.inf
		__A   : Optional[int]								= None  # to hold the next best neighbor
		for neighbor in neighbors:
			if neighbor in visited:
				continue  # do not want to visit the same state again
			if (
			    neighbor.x > max_x
			    or neighbor.x < min_x
			    or neighbor.y > max_y
			    or neighbor.y < min_y
			):
				continue  # neighbor outside our bounds
			__A   : int								= neighbor.score() - current_score
			if find_max:  # finding max
				# going to direction with greatest ascent
				if change > max_change and change > 0:
					__A   : str								= change
					__A   : Optional[int]								= neighbor
			else:  # finding min
				# to direction with greatest descent
				if change < min_change and change < 0:
					__A   : List[Any]								= change
					__A   : List[str]								= neighbor
		if next_state is not None:
			# we found at least one neighbor which improved the current state
			__A   : Tuple								= next_state
		else:
			# since we have no neighbor that improves the solution we stop the search
			__A   : str								= True

	if visualization:
		from matplotlib import pyplot as plt

		plt.plot(range(a__					) ,a__					)
		plt.xlabel("""Iterations"""					)
		plt.ylabel("""Function values"""					)
		plt.show()

	return current_state


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
				return (x**2) + (y**2)

			# starting the problem with initial coordinates (3, 4)
			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
			print(
			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
			    f"""{local_min.score()}"""
			)

			# starting the problem with initial coordinates (12, 47)
			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Any		   =      hill_climbing(
			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
			)
			print(
			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
			)
			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
				return (3 * x**2) - (6 * y)

			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)







			print(
			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
			    f"""{local_min.score()}"""
			)





