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   :				int     			=						logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCamelCase   :				Optional[int]     			=						'''cuda''' if torch.cuda.is_available() else '''cpu'''





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Optional[int]=1_0_0 ,	UpperCAmelCase__       :							str=" "					):
					SCREAMING_SNAKE_CASE           =    text.split(UpperCAmelCase__					)
					return [character.join(text[i : i + n]					).strip() for i in range(0 ,	len(UpperCAmelCase__					) ,	UpperCAmelCase__					)]





def 		__lowerCamelCase							(UpperCAmelCase__       :							dict					):
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    [], []
					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					):
					SCREAMING_SNAKE_CASE           =    ctx_tokenizer(
					    documents["title"] ,	documents["text"] ,	truncation=UpperCAmelCase__ ,	padding="longest" ,	return_tensors="pt"					)["input_ids"]
					SCREAMING_SNAKE_CASE           =    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" ,	):
					######################################
					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           =    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           =    dataset.map(UpperCAmelCase__ ,	batched=UpperCAmelCase__ ,	num_proc=processing_args.num_proc					)

					# And compute the embeddings
					SCREAMING_SNAKE_CASE           =    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
					SCREAMING_SNAKE_CASE           =    Features(
					    {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
					SCREAMING_SNAKE_CASE           =    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
					SCREAMING_SNAKE_CASE           =    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
					SCREAMING_SNAKE_CASE           =    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
					SCREAMING_SNAKE_CASE           =    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


@dataclass
class   lowercase      :
							lowercase__  :    str									=							field(
							    default=str(Path(a       ).parent / """test_run""" / """dummy-kb""" / """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=str(Path(a       ).parent / """test_run""" / """dummy-kb"""       )			,    metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}			,    )



@dataclass
class   lowercase      :
							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   lowercase      :
							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)

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





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCamelCase   :				Optional[Any]     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict					):
					try:
										with open(UpperCAmelCase__ ,	"rb"					) as flax_state_f:
															SCREAMING_SNAKE_CASE           =    from_bytes(UpperCAmelCase__ ,	flax_state_f.read()					)
					except UnpicklingError as e:
										try:
															with open(UpperCAmelCase__					) as f:
																				if f.read().startswith("version"					):
																									raise OSError(
																									    "You seem to have cloned a repository without having git-lfs installed. Please"
																									    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
																									    " folder you cloned."					)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. "					)

					return load_flax_weights_in_pytorch_model(UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
										    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
										    " instructions."					)
										raise

					# check if we have bf16 weights
					SCREAMING_SNAKE_CASE           =    flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase__					: x.dtype == jnp.bfloataa ,	UpperCAmelCase__					)					).values()
					if any(UpperCAmelCase__					):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
										    "before loading those in PyTorch model."					)
										SCREAMING_SNAKE_CASE           =    jax.tree_util.tree_map(
										    lambda UpperCAmelCase__					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    ""

					SCREAMING_SNAKE_CASE           =    flatten_dict(UpperCAmelCase__ ,	sep="."					)
					SCREAMING_SNAKE_CASE           =    pt_model.state_dict()

					# keep track of unexpected & missing keys
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    set(pt_model_dict.keys()					)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										SCREAMING_SNAKE_CASE           =    flax_key_tuple.split("."					)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    jnp.transpose(UpperCAmelCase__ ,	(3, 2, 0, 1)					)
										elif flax_key_tuple_array[-1] == "kernel":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]
															SCREAMING_SNAKE_CASE           =    flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															SCREAMING_SNAKE_CASE           =    flax_key_tuple_array[:-1] + ["weight"]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(UpperCAmelCase__					):
																				SCREAMING_SNAKE_CASE           =    (
																				    flax_key_tuple_string.replace("_0" ,	".0"					)
																				    .replace("_1" ,	".1"					)
																				    .replace("_2" ,	".2"					)
																				    .replace("_3" ,	".3"					)
																				    .replace("_4" ,	".4"					)
																				    .replace("_5" ,	".5"					)
																				    .replace("_6" ,	".6"					)
																				    .replace("_7" ,	".7"					)
																				    .replace("_8" ,	".8"					)
																				    .replace("_9" ,	".9"					)
																				)

										SCREAMING_SNAKE_CASE           =    ".".join(UpperCAmelCase__					)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
																				    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."					)
															else:
																				# add weight to pytorch dict
																				SCREAMING_SNAKE_CASE           =    np.asarray(UpperCAmelCase__					) if not isinstance(UpperCAmelCase__ ,	np.ndarray					) else flax_tensor
																				SCREAMING_SNAKE_CASE           =    torch.from_numpy(UpperCAmelCase__					)
																				# remove from missing keys
																				missing_keys.remove(UpperCAmelCase__					)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(UpperCAmelCase__					)

					pt_model.load_state_dict(UpperCAmelCase__					)

					# re-transform missing_keys to list
					SCREAMING_SNAKE_CASE           =    list(UpperCAmelCase__					)

					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    "Some weights of the Flax model were not used when initializing the PyTorch model"
										    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
										    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
										    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
										    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
										    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
										    " FlaxBertForSequenceClassification model)."					)
					if len(UpperCAmelCase__					) > 0:
										logger.warning(
										    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
										    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
										    " use it for predictions and inference."					)

					return pt_model




