




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


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


def 						lowerCamelCase__    (		_A     ,	_A=100     ,	_A=" "							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_    ,      snake_case_		    =     [], []
	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 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	):







	'''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
	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
	snake_case_		    =     dataset.map(_A     ,	batched=_A     ,	num_proc=processing_args.num_proc							)

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







						'''simple docstring'''





						lowerCAmelCase_     		=   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\''''}       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default='''facebook/rag-sequence-nq'''       ,   metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}       ,   )
						lowerCAmelCase_     		=   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_     		=   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 		UpperCAmelCase   :







						'''simple docstring'''





						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={
						        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
						    }       ,   )
						lowerCAmelCase_     		=   field(
						    default=16       ,   metadata={
						        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
						    }       ,   )







@dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





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

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






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowercase__				:     Optional[int] 		=						[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

lowercase__				:     List[Any] 		=						[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

lowercase__				:     Optional[Any] 		=						[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				lowercase__				:     str 		=						f'''down_blocks.{i}.resnets.{j}.'''
				lowercase__				:     Union[str, Any] 		=						f'''input_blocks.{3*i + j + 1}.0.'''
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						lowercase__				:     Tuple 		=						f'''down_blocks.{i}.attentions.{j}.'''
						lowercase__				:     Dict 		=						f'''input_blocks.{3*i + j + 1}.1.'''
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				lowercase__				:     List[Any] 		=						f'''up_blocks.{i}.resnets.{j}.'''
				lowercase__				:     int 		=						f'''output_blocks.{3*i + j}.0.'''
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						lowercase__				:     List[str] 		=						f'''up_blocks.{i}.attentions.{j}.'''
						lowercase__				:     Tuple 		=						f'''output_blocks.{3*i + j}.1.'''
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				lowercase__				:     List[str] 		=						f'''down_blocks.{i}.downsamplers.0.conv.'''
				lowercase__				:     Any 		=						f'''input_blocks.{3*(i+1)}.0.op.'''
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				lowercase__				:     Optional[int] 		=						f'''up_blocks.{i}.upsamplers.0.'''
				lowercase__				:     int 		=						f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowercase__				:     Union[str, Any] 		=						"mid_block.attentions.0."
lowercase__				:     List[Any] 		=						"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		lowercase__				:     Tuple 		=						f'''mid_block.resnets.{j}.'''
		lowercase__				:     List[str] 		=						f'''middle_block.{2*j}.'''
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		snake_case_		    =     sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				snake_case_		    =     v.replace(_A     ,	_A							)
			snake_case_		    =     v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			snake_case_		    =     v.replace(_A     ,	_A							)
		snake_case_		    =     v
	snake_case_		    =     {v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowercase__				:     Dict 		=						[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				lowercase__				:     Any 		=						f'''encoder.down_blocks.{i}.resnets.{j}.'''
				lowercase__				:     List[Any] 		=						f'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				lowercase__				:     Optional[int] 		=						f'''down_blocks.{i}.downsamplers.0.'''
				lowercase__				:     Tuple 		=						f'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				lowercase__				:     List[str] 		=						f'''up_blocks.{i}.upsamplers.0.'''
				lowercase__				:     Optional[int] 		=						f'''up.{3-i}.upsample.'''
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				lowercase__				:     int 		=						f'''decoder.up_blocks.{i}.resnets.{j}.'''
				lowercase__				:     Union[str, Any] 		=						f'''decoder.up.{3-i}.block.{j}.'''
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		lowercase__				:     Dict 		=						f'''mid_block.resnets.{i}.'''
		lowercase__				:     int 		=						f'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowercase__				:     Optional[Any] 		=						[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return w.reshape(*w.shape     ,	1     ,	1							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			snake_case_		    =     v.replace(_A     ,	_A							)
		snake_case_		    =     v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				snake_case_		    =     v.replace(_A     ,	_A							)
			snake_case_		    =     v
	snake_case_		    =     {v: vae_state_dict[k] for k, v in mapping.items()}
	snake_case_		    =     ["q", "k", "v", "proj_out"]
	for k, v in new_state_dict.items():
		for weight_name in weights_to_convert:
			if f"mid.attn_1.{weight_name}.weight" in k:
				print(f"Reshaping {k} for SD format"							)
				snake_case_		    =     reshape_weight_for_sd(_A							)
	return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowercase__				:     int 		=						[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
lowercase__				:     Dict 		=						{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase__				:     Tuple 		=						re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowercase__				:     Dict 		=						{"q": 0, "k": 1, "v": 2}


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {}
	snake_case_		    =     {}
	snake_case_		    =     {}
	for k, v in text_enc_dict.items():
		if (
		    k.endswith(".self_attn.q_proj.weight"							)
		    or k.endswith(".self_attn.k_proj.weight"							)
		    or k.endswith(".self_attn.v_proj.weight"							)
		):
			snake_case_		    =     k[: -len(".q_proj.weight"							)]
			snake_case_		    =     k[-len("q_proj.weight"							)]
			if k_pre not in capture_qkv_weight:
				snake_case_		    =     [None, None, None]
			snake_case_		    =     v
			continue

		if (
		    k.endswith(".self_attn.q_proj.bias"							)
		    or k.endswith(".self_attn.k_proj.bias"							)
		    or k.endswith(".self_attn.v_proj.bias"							)
		):
			snake_case_		    =     k[: -len(".q_proj.bias"							)]
			snake_case_		    =     k[-len("q_proj.bias"							)]
			if k_pre not in capture_qkv_bias:
				snake_case_		    =     [None, None, None]
			snake_case_		    =     v
			continue

		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     v

	for k_pre, tensors in capture_qkv_weight.items():
		if None in tensors:
			raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"							)
		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     torch.cat(_A							)

	for k_pre, tensors in capture_qkv_bias.items():
		if None in tensors:
			raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"							)
		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     torch.cat(_A							)

	return new_state_dict


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return text_enc_dict


if __name__ == "__main__":
		lowercase__				:     int 		=						argparse.ArgumentParser()

		parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
		parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
		parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
		parser.add_argument(
		    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
		)

		lowercase__				:     Dict 		=						parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		lowercase__				:     Tuple 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		lowercase__				:     int 		=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		lowercase__				:     Any 		=						osp.join(args.model_path, "text_encoder", "model.safetensors")

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				lowercase__				:     str 		=						load_file(unet_path, device="cpu")
		else:
				lowercase__				:     Optional[Any] 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				lowercase__				:     Any 		=						torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				lowercase__				:     Tuple 		=						load_file(vae_path, device="cpu")
		else:
				lowercase__				:     Any 		=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				lowercase__				:     Dict 		=						torch.load(vae_path, map_location="cpu")

		if osp.exists(text_enc_path):
				lowercase__				:     Union[str, Any] 		=						load_file(text_enc_path, device="cpu")
		else:
				lowercase__				:     Union[str, Any] 		=						osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
				lowercase__				:     Optional[int] 		=						torch.load(text_enc_path, map_location="cpu")

		# Convert the UNet model
		lowercase__				:     Dict 		=						convert_unet_state_dict(unet_state_dict)
		lowercase__				:     Any 		=						{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		lowercase__				:     Dict 		=						convert_vae_state_dict(vae_state_dict)
		lowercase__				:     Union[str, Any] 		=						{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		lowercase__				:     Optional[Any] 		=						"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				lowercase__				:     Any 		=						{"transformer." + k: v for k, v in text_enc_dict.items()}
				lowercase__				:     List[Any] 		=						convert_text_enc_state_dict_vaa(text_enc_dict)
				lowercase__				:     List[str] 		=						{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				lowercase__				:     Tuple 		=						convert_text_enc_state_dict(text_enc_dict)
				lowercase__				:     Any 		=						{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		lowercase__				:     Tuple 		=						{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				lowercase__				:     Any 		=						{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				lowercase__				:     Union[str, Any] 		=						{"state_dict": state_dict}
				torch.save(state_dict, args.checkpoint_path)
