



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__     =	logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase__     =	"""cuda""" if torch.cuda.is_available() else """cpu"""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     str			,       _SCREAMING_SNAKE_CASE    :     str=100			,       _SCREAMING_SNAKE_CASE    :     Any=" "      ):
				"""simple docstring"""







				__a					       =						text.split(_SCREAMING_SNAKE_CASE      )
				return [character.join(text[i : i + n]      ).strip() for i in range(0			,       len(_SCREAMING_SNAKE_CASE      )			,       _SCREAMING_SNAKE_CASE      )]

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     dict      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     dict			,       _SCREAMING_SNAKE_CASE    :     DPRContextEncoder			,       _SCREAMING_SNAKE_CASE    :     DPRContextEncoderTokenizerFast      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     "RagExampleArguments"			,       _SCREAMING_SNAKE_CASE    :     "ProcessingArguments"			,       _SCREAMING_SNAKE_CASE    :     "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
				__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(_SCREAMING_SNAKE_CASE			,       batched=_SCREAMING_SNAKE_CASE			,       num_proc=processing_args.num_proc      )

				# And compute the embeddings
				__a					       =						DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      ).to(device=_SCREAMING_SNAKE_CASE      )
				__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(_SCREAMING_SNAKE_CASE			,       ctx_encoder=_SCREAMING_SNAKE_CASE			,       ctx_tokenizer=_SCREAMING_SNAKE_CASE      )			,       batched=_SCREAMING_SNAKE_CASE			,       batch_size=processing_args.batch_size			,       features=_SCREAMING_SNAKE_CASE			,       )

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

				# And save the index
				__a					       =						os.path.join(rag_example_args.output_dir			,       """my_knowledge_dataset_hnsw_index.faiss"""      )
				dataset.get_index("""embeddings"""      ).save(_SCREAMING_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					       =						RagRetriever.from_pretrained(
				    rag_example_args.rag_model_name			,       index_name="""custom"""			,       indexed_dataset=_SCREAMING_SNAKE_CASE      )
				__a					       =						RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,       retriever=_SCREAMING_SNAKE_CASE      )
				__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(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""      )["""input_ids"""]
				__a					       =						model.generate(_SCREAMING_SNAKE_CASE      )
				__a					       =						tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,       skip_special_tokens=_SCREAMING_SNAKE_CASE      )[0]
				logger.info("""Q: """ + question      )
				logger.info("""A: """ + generated_string      )






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    default=str(Path(lowerCamelCase__   ).parent / 'test_data' / 'my_knowledge_dataset.csv'   )     , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}     , )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}     , )
							__lowerCamelCase		:		str      =field(
							    default='facebook/rag-sequence-nq'     , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}     , )
							__lowerCamelCase		:		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\''
							        )
							    }     , )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}     , )






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		Optional[int]      =field(
							    default=lowerCamelCase__     , metadata={
							        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
							    }     , )
							__lowerCamelCase		:		int      =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     :
							__lowerCamelCase		:		int      =field(
							    default=768     , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}     , )
							__lowerCamelCase		:		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)

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





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict=None			,       _SCREAMING_SNAKE_CASE    :     List[str]=None      ):
				"""simple docstring"""







				return field(default_factory=lambda: default			,       metadata=_SCREAMING_SNAKE_CASE      )






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    metadata={'help': 'The csv file to plot.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Disable logarithmic scale when plotting'}     , )
							__lowerCamelCase		:		bool      =field(
							    default=lowerCamelCase__     , metadata={
							        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
							    }     , )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}     , )
							__lowerCamelCase		:		Optional[List[str]]      =list_field(
							    default=lowerCamelCase__     , metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}   )



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any      ):
				"""simple docstring"""







				try:
								int(_SCREAMING_SNAKE_CASE      )
								return True
				except ValueError:
								return False

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple      ):
				"""simple docstring"""







				try:
								float(_SCREAMING_SNAKE_CASE      )
								return True
				except ValueError:
								return False






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Optional[int] ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						args
											__a					       =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

											with open(self.args.csv_file ,			newline=""""""		) as csv_file:
															__a					       =						csv.DictReader(__lowercase		)
															for row in reader:
																			__a					       =						row["""model"""]
																			self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]		)		)
																			self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]		)		)
																			if can_convert_to_int(row["""result"""]		):
																							# value is not None
																							__a					       =						int(row["""result"""]		)
																			elif can_convert_to_float(row["""result"""]		):
																							# value is not None
																							__a					       =						float(row["""result"""]		)







							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											__a       ,   __a					       =						plt.subplots()
											__a					       =						"""Time usage""" if self.args.is_time else """Memory usage"""
											__a					       =						title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

											if not self.args.no_log_scale:
															# set logarithm scales
															ax.set_xscale("""log"""		)
															ax.set_yscale("""log"""		)

											for axis in [ax.xaxis, ax.yaxis]:
															axis.set_major_formatter(ScalarFormatter()		)

											for model_name_idx, model_name in enumerate(self.result_dict.keys()		):
															__a					       =						sorted(set(self.result_dict[model_name]["""bsz"""]		)		)
															__a					       =						sorted(set(self.result_dict[model_name]["""seq_len"""]		)		)
															__a					       =						self.result_dict[model_name]["""result"""]

															((__a)       ,   (__a))					       =						(
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															__a					       =						(
															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
															)

															for inner_loop_value in inner_loop_array:
																			if self.args.plot_along_batch:
																							__a					       =						np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,			dtype=__lowercase ,			)
																			else:
																							__a					       =						np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,			dtype=np.floataa ,			)

																			((__a)       ,   (__a))					       =						(
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			__a					       =						np.asarray(__lowercase ,			__lowercase		)[: len(__lowercase		)]
																			plt.scatter(
																			    __lowercase ,			__lowercase ,			label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
																			plt.plot(__lowercase ,			__lowercase ,			"""--"""		)

															title_str += F" {label_model_name} vs."

											__a					       =						title_str[:-4]
											__a					       =						"""Time in s""" if self.args.is_time else """Memory in MB"""

											# plot
											plt.title(__lowercase		)
											plt.xlabel(__lowercase		)
											plt.ylabel(__lowercase		)
											plt.legend()

											if self.args.figure_png_file is not None:
															plt.savefig(self.args.figure_png_file		)
											else:
															plt.show()



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser(_SCREAMING_SNAKE_CASE      )
				__a					       =						parser.parse_args_into_dataclasses()[0]
				__a					       =						Plot(args=_SCREAMING_SNAKE_CASE      )
				plot.plot()


if __name__ == "__main__":
						main()
