


'''simple docstring'''
import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME







def       a_				(	UpperCamelCase_ ):
							A_			    =		[
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							A_    ,					A_			    =		emb.weight.shape
							A_			    =		nn.Linear(UpperCamelCase_						,  UpperCamelCase_						,  bias=UpperCamelCase_ )
							A_			    =		emb.weight.data
							return lin_layer







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=None ):
							A_			    =		{}
							for old_key in state_dict.keys():
														A_			    =		old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												A_			    =		key.replace("moe_layer.experts.0"						,  f"ffn.experts.expert_{expert_idx}" )
																					else:
																												A_			    =		key.replace("moe_layer.experts."						,  "ffn.experts.expert_" )
														if "gate" in key:
																					A_			    =		key.replace(".moe_layer.gate.wg"						,  ".ffn.router.classifier" )
														if "fc2" and "experts" not in key:
																					A_			    =		key.replace(".fc2."						,  ".ffn.fc2." )
														if "fc1" and "experts" not in key:
																					A_			    =		key.replace(".fc1."						,  ".ffn.fc1." )
														if ".encoder_attn." in key:
																					A_			    =		key.replace(".encoder_attn."						,  ".cross_attention." )
														if "encoder_attn_layer_norm" in key:
																					A_			    =		key.replace("encoder_attn_layer_norm"						,  "cross_attention_layer_norm" )
														if "final_layer_norm" in key:
																					A_			    =		key.replace("final_layer_norm"						,  "ff_layer_norm" )
														A_			    =		state_dict[old_key]
							return new_dict







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ = WEIGHTS_NAME ):
							A_			    =		[]
							A_			    =		0
							os.makedirs(UpperCamelCase_						,  exist_ok=UpperCamelCase_ )

							for expert in range(UpperCamelCase_ ):
														A_			    =		switch_checkpoint_path + f"-rank-{expert}.pt"
														if os.path.isfile(UpperCamelCase_ ):
																					A_			    =		torch.load(UpperCamelCase_ )["model"]
																					remove_ignore_keys_(UpperCamelCase_ )
																					A_			    =		rename_fairseq_keys(UpperCamelCase_						,  UpperCamelCase_ )
																					A_			    =		os.path.join(
																					    UpperCamelCase_						,  weights_name.replace(".bin"						,  f"-{len(UpperCamelCase_ )+1:05d}-of-???.bin" ) )
																					torch.save(UpperCamelCase_						,  UpperCamelCase_ )
																					sharded_state_dicts.append(expert_state.keys() )
																					total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
																					    expert_state[list(UpperCamelCase_ )[0]].dtype )

    # Add the last block
							A_			    =		os.path.join(UpperCamelCase_						,  weights_name.replace(".bin"						,  f"-{len(UpperCamelCase_ )+1:05d}-of-???.bin" ) )
							A_			    =		torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
							remove_ignore_keys_(UpperCamelCase_ )
							A_			    =		rename_fairseq_keys(UpperCamelCase_						,  UpperCamelCase_ )
							A_			    =		shared_weights["decoder.embed_tokens.weight"]
							sharded_state_dicts.append(shared_weights.keys() )

							# If we only have the shared weights (dummy model/experts saved on the same file)
							if len(UpperCamelCase_ ) == 1:
														A_			    =		os.path.join(UpperCamelCase_						,  UpperCamelCase_ )
														torch.save(UpperCamelCase_						,  UpperCamelCase_ )
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(UpperCamelCase_						,  UpperCamelCase_ )
							# Otherwise, let's build the index
							A_			    =		{}
							for idx, shard in enumerate(UpperCamelCase_ ):
														A_			    =		weights_name.replace(".bin"						,  f"-{idx+1:05d}-of-{len(UpperCamelCase_ ):05d}.bin" )
														A_			    =		os.path.join(UpperCamelCase_						,  weights_name.replace(".bin"						,  f"-{idx+1:05d}-of-???.bin" ) )
														os.rename(UpperCamelCase_						,  os.path.join(UpperCamelCase_						,  UpperCamelCase_ ) )
														for key in shard:
																					A_			    =		shard_file

    # Add the metadata
							A_			    =		{"total_size": total_size}
							A_			    =		{"metadata": metadata, "weight_map": weight_map}

							with open(os.path.join(UpperCamelCase_						,  UpperCamelCase_ )						,  "w"						,  encoding="utf-8" ) as f:
														A_			    =		json.dumps(UpperCamelCase_						,  indent=2						,  sort_keys=UpperCamelCase_ ) + "\n"
														f.write(UpperCamelCase_ )

							return metadata, index


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				Dict									=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--nllb_moe_checkpoint_path''',
							    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
							    type=str,
							    required=False,
							    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
							)
							parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
							    type=str,
							    required=False,
							    help='''Path to the output pytorch model.''',
							)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		parser.parse_args()
							__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE					:				Optional[int]									=		shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    128,
							    args.dtype,
							)

							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		NllbMoeConfig.from_pretrained(
							    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							__SCREAMING_SNAKE_CASE					:				List[str]									=		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('''Done''')
							model.save_pretrained(args.pytorch_dump_folder_path)









'''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


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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0_0						,  UpperCamelCase_=" " ):
							A_			    =		text.split(UpperCamelCase_ )
							return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(UpperCamelCase_ )						,  UpperCamelCase_ )]







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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  ):
							######################################
							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(UpperCamelCase_						,  batched=UpperCamelCase_						,  num_proc=processing_args.num_proc )

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

							# And finally save your dataset
							A_			    =		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
							A_			    =		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
							A_			    =		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 					__lowerCAmelCase   :

						"""simple docstring"""






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






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






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






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       int   						=field(
						    default=768				,			metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}				,			)
						_UpperCAmelCase		:       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)

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





