






"""simple docstring"""







import argparse

import torch

from diffusers import UNetaDConditionModel






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase=None		,					lowerCAmelCase=None		,					lowerCAmelCase=None						):







       '''simple docstring'''
       assert isinstance(lowerCAmelCase		,					lowerCAmelCase						), "Paths should be a list of dicts containing 'old' and 'new' keys."

       # Splits the attention layers into three variables.
       if attention_paths_to_split is not None:
              for path, path_map in attention_paths_to_split.items():
                     UpperCAmelCase					=	old_checkpoint[path]
                     UpperCAmelCase					=	old_tensor.shape[0] // 3

                     UpperCAmelCase					=	(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

                     UpperCAmelCase					=	old_tensor.shape[0] // config["""num_head_channels"""] // 3

                     UpperCAmelCase					=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
                     UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	old_tensor.split(channels // num_heads		,					dim=1						)

                     UpperCAmelCase					=	query.reshape(lowerCAmelCase						)
                     UpperCAmelCase					=	key.reshape(lowerCAmelCase						)
                     UpperCAmelCase					=	value.reshape(lowerCAmelCase						)

       for path in paths:
              UpperCAmelCase					=	path["""new"""]

              # These have already been assigned
              if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                     continue

              if additional_replacements is not None:
                     for replacement in additional_replacements:
                            UpperCAmelCase					=	new_path.replace(replacement["""old"""]		,					replacement["""new"""]						)

        # proj_attn.weight has to be converted from conv 1D to linear
              UpperCAmelCase					=	old_checkpoint[path["""old"""]]
              UpperCAmelCase					=	["""proj_attn.weight"""]
              UpperCAmelCase					=	["""proj_out.weight""", """proj_in.weight"""]
              if any(k in new_path for k in names						):
                     UpperCAmelCase					=	weight[:, :, 0]
              elif any(k in new_path for k in names_a						) and len(weight.shape						) > 2 and ".attentions." not in new_path:
                     UpperCAmelCase					=	weight[:, :, 0]
              else:
                     UpperCAmelCase					=	weight






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=0						):







       '''simple docstring'''
       UpperCAmelCase					=	[]
       for old_item in old_list:
              UpperCAmelCase					=	old_item

              #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
              #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

              #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
              #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

              #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

              mapping.append({"""old""": old_item, """new""": new_item}						)

       return mapping






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=1						):







       '''simple docstring'''
       if n_shave_prefix_segments >= 0:
              return ".".join(path.split("""."""						)[n_shave_prefix_segments:]						)
       else:
              return ".".join(path.split("""."""						)[:n_shave_prefix_segments]						)






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=0						):







       '''simple docstring'''
       UpperCAmelCase					=	[]
       for old_item in old_list:
              mapping.append({"""old""": old_item, """new""": old_item}						)

       return mapping






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=0						):







       '''simple docstring'''
       UpperCAmelCase					=	[]
       for old_item in old_list:
              UpperCAmelCase					=	old_item.replace("""in_layers.0"""		,					"""norm1"""						)
              UpperCAmelCase					=	new_item.replace("""in_layers.2"""		,					"""conv1"""						)

              UpperCAmelCase					=	new_item.replace("""out_layers.0"""		,					"""norm2"""						)
              UpperCAmelCase					=	new_item.replace("""out_layers.3"""		,					"""conv2"""						)

              UpperCAmelCase					=	new_item.replace("""emb_layers.1"""		,					"""time_emb_proj"""						)
              UpperCAmelCase					=	new_item.replace("""skip_connection"""		,					"""conv_shortcut"""						)

              UpperCAmelCase					=	shave_segments(lowerCAmelCase		,					n_shave_prefix_segments=lowerCAmelCase						)

              if "temopral_conv" not in old_item:
                     mapping.append({"""old""": old_item, """new""": new_item}						)

       return mapping






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase=None		,					lowerCAmelCase=False						):







       '''simple docstring'''
       UpperCAmelCase					=	{}
       UpperCAmelCase					=	list(checkpoint.keys()						)

       UpperCAmelCase					=	"""model.diffusion_model."""

       # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
       if sum(k.startswith("""model_ema"""						) for k in keys						) > 100 and extract_ema:
              print(F'''Checkpoint {path} has both EMA and non-EMA weights.'''						)
              print(
                  """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
                  """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""						)
              for key in keys:
                     if key.startswith("""model.diffusion_model"""						):
                            UpperCAmelCase					=	"""model_ema.""" + """""".join(key.split("""."""						)[1:]						)
                            UpperCAmelCase					=	checkpoint.pop(lowerCAmelCase						)
       else:
              if sum(k.startswith("""model_ema"""						) for k in keys						) > 100:
                     print(
                         """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
                         """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""						)

              for key in keys:
                     UpperCAmelCase					=	checkpoint.pop(lowerCAmelCase						)

       UpperCAmelCase					=	{}

       UpperCAmelCase					=	unet_state_dict["""time_embed.0.weight"""]
       UpperCAmelCase					=	unet_state_dict["""time_embed.0.bias"""]
       UpperCAmelCase					=	unet_state_dict["""time_embed.2.weight"""]
       UpperCAmelCase					=	unet_state_dict["""time_embed.2.bias"""]

       if config["class_embed_type"] is None:
              # No parameters to port
              ...
       elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.0.weight"""]
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.0.bias"""]
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.2.weight"""]
              UpperCAmelCase					=	unet_state_dict["""label_emb.0.2.bias"""]
       else:
              raise NotImplementedError(F'''Not implemented `class_embed_type`: {config['class_embed_type']}'''						)

       UpperCAmelCase					=	unet_state_dict["""input_blocks.0.0.weight"""]
       UpperCAmelCase					=	unet_state_dict["""input_blocks.0.0.bias"""]

       UpperCAmelCase					=	[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""						)]
       UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
       assign_to_checkpoint(lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       UpperCAmelCase					=	unet_state_dict["""out.0.weight"""]
       UpperCAmelCase					=	unet_state_dict["""out.0.bias"""]
       UpperCAmelCase					=	unet_state_dict["""out.2.weight"""]
       UpperCAmelCase					=	unet_state_dict["""out.2.bias"""]

       # Retrieves the keys for the input blocks only
       UpperCAmelCase					=	len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """input_blocks""" in layer}						)
       UpperCAmelCase					=	{
           layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
           for layer_id in range(lowerCAmelCase						)
       }

       # Retrieves the keys for the middle blocks only
       UpperCAmelCase					=	len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """middle_block""" in layer}						)
       UpperCAmelCase					=	{
           layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
           for layer_id in range(lowerCAmelCase						)
       }

       # Retrieves the keys for the output blocks only
       UpperCAmelCase					=	len({""".""".join(layer.split("""."""						)[:2]						) for layer in unet_state_dict if """output_blocks""" in layer}						)
       UpperCAmelCase					=	{
           layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
           for layer_id in range(lowerCAmelCase						)
       }

       for i in range(1		,					lowerCAmelCase						):
              UpperCAmelCase					=	(i - 1) // (config["""layers_per_block"""] + 1)
              UpperCAmelCase					=	(i - 1) % (config["""layers_per_block"""] + 1)

              UpperCAmelCase					=	[
                  key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key and F'''input_blocks.{i}.0.op''' not in key
              ]
              UpperCAmelCase					=	[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
              UpperCAmelCase					=	[key for key in input_blocks[i] if F'''input_blocks.{i}.2''' in key]

              if F'''input_blocks.{i}.op.weight''' in unet_state_dict:
                     UpperCAmelCase					=	unet_state_dict.pop(
                         F'''input_blocks.{i}.op.weight'''						)
                     UpperCAmelCase					=	unet_state_dict.pop(
                         F'''input_blocks.{i}.op.bias'''						)

              UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
              UpperCAmelCase					=	{"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
              assign_to_checkpoint(
                  lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

              UpperCAmelCase					=	[key for key in resnets if """temopral_conv""" in key]
              UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
              UpperCAmelCase					=	{
                  """old""": F'''input_blocks.{i}.0.temopral_conv''',
                  """new""": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
              }
              assign_to_checkpoint(
                  lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

              if len(lowerCAmelCase						):
                     UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	{"""old""": F'''input_blocks.{i}.1''', """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

              if len(lowerCAmelCase						):
                     UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	{
                         """old""": F'''input_blocks.{i}.2''',
                         """new""": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                     }
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       UpperCAmelCase					=	middle_blocks[0]
       UpperCAmelCase					=	[key for key in resnet_a if """temopral_conv""" in key]
       UpperCAmelCase					=	middle_blocks[1]
       UpperCAmelCase					=	middle_blocks[2]
       UpperCAmelCase					=	middle_blocks[3]
       UpperCAmelCase					=	[key for key in resnet_a if """temopral_conv""" in key]

       UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					config=lowerCAmelCase		,					additional_replacements=[meta_path]						)

       UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
       UpperCAmelCase					=	{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
       assign_to_checkpoint(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

       for i in range(lowerCAmelCase						):
              UpperCAmelCase					=	i // (config["""layers_per_block"""] + 1)
              UpperCAmelCase					=	i % (config["""layers_per_block"""] + 1)
              UpperCAmelCase					=	[shave_segments(lowerCAmelCase		,					2						) for name in output_blocks[i]]
              UpperCAmelCase					=	{}

              for layer in output_block_layers:
                     UpperCAmelCase			,				UpperCAmelCase					=	layer.split("""."""						)[0], shave_segments(lowerCAmelCase		,					1						)
                     if layer_id in output_block_list:
                            output_block_list[layer_id].append(lowerCAmelCase						)
                     else:
                            UpperCAmelCase					=	[layer_name]

              if len(lowerCAmelCase						) > 1:
                     UpperCAmelCase					=	[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
                     UpperCAmelCase					=	[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
                     UpperCAmelCase					=	[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

                     UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase						)

                     UpperCAmelCase					=	{"""old""": F'''output_blocks.{i}.0''', """new""": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

                     UpperCAmelCase					=	[key for key in resnets if """temopral_conv""" in key]
                     UpperCAmelCase					=	renew_temp_conv_paths(lowerCAmelCase						)
                     UpperCAmelCase					=	{
                         """old""": F'''output_blocks.{i}.0.temopral_conv''',
                         """new""": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
                     }
                     assign_to_checkpoint(
                         lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

                     UpperCAmelCase					=	{k: sorted(lowerCAmelCase						) for k, v in output_block_list.items()}
                     if ["conv.bias", "conv.weight"] in output_block_list.values():
                            UpperCAmelCase					=	list(output_block_list.values()						).index(["""conv.bias""", """conv.weight"""]						)
                            UpperCAmelCase					=	unet_state_dict[
                                F'''output_blocks.{i}.{index}.conv.weight'''
                            ]
                            UpperCAmelCase					=	unet_state_dict[
                                F'''output_blocks.{i}.{index}.conv.bias'''
                            ]

                            # Clear attentions as they have been attributed above.
                            if len(lowerCAmelCase						) == 2:
                                   UpperCAmelCase					=	[]

                     if len(lowerCAmelCase						):
                            UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                            UpperCAmelCase					=	{
                                """old""": F'''output_blocks.{i}.1''',
                                """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                            }
                            assign_to_checkpoint(
                                lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)

                     if len(lowerCAmelCase						):
                            UpperCAmelCase					=	renew_attention_paths(lowerCAmelCase						)
                            UpperCAmelCase					=	{
                                """old""": F'''output_blocks.{i}.2''',
                                """new""": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                            }
                            assign_to_checkpoint(
                                lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					additional_replacements=[meta_path]		,					config=lowerCAmelCase						)
              else:
                     UpperCAmelCase					=	renew_resnet_paths(lowerCAmelCase		,					n_shave_prefix_segments=1						)
                     for path in resnet_0_paths:
                            UpperCAmelCase					=	""".""".join(["""output_blocks""", str(lowerCAmelCase						), path["""old"""]]						)
                            UpperCAmelCase					=	""".""".join(["""up_blocks""", str(lowerCAmelCase						), """resnets""", str(lowerCAmelCase						), path["""new"""]]						)
                            UpperCAmelCase					=	unet_state_dict[old_path]

                     UpperCAmelCase					=	[l for l in output_block_layers if """temopral_conv""" in l]
                     for path in temopral_conv_paths:
                            UpperCAmelCase					=	path.split("""temopral_conv."""						)[-1]
                            UpperCAmelCase					=	""".""".join(["""output_blocks""", str(lowerCAmelCase						), str(lowerCAmelCase						), """temopral_conv""", pruned_path]						)
                            UpperCAmelCase					=	""".""".join(["""up_blocks""", str(lowerCAmelCase						), """temp_convs""", str(lowerCAmelCase						), pruned_path]						)
                            UpperCAmelCase					=	unet_state_dict[old_path]

       return new_checkpoint


if __name__ == "__main__":
 lowerCAmelCase_		:			List[str]				   =					argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )
 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 lowerCAmelCase_		:			List[Any]				   =					parser.parse_args()

 lowerCAmelCase_		:			Any				   =					torch.load(args.checkpoint_path, map_location='''cpu''')
 lowerCAmelCase_		:			Any				   =					UNetaDConditionModel()

 lowerCAmelCase_		:			Optional[int]				   =					convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

 lowerCAmelCase_		:			Any				   =					set(unet.state_dict().keys()) - set(converted_ckpt.keys())
 lowerCAmelCase_		:			Any				   =					set(converted_ckpt.keys()) - set(unet.state_dict().keys())

 assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

 # load state_dict
 unet.load_state_dict(converted_ckpt)

 unet.save_pretrained(args.dump_path)

 # -- finish converting the unet --

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_				:		Union[str, Any]       						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_				:		List[Any]       						=       """cuda""" if torch.cuda.is_available() else """cpu"""
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=100      ,				__lowerCamelCase=" "						):
					__a							=   text.split(__lowerCamelCase						)
					return [character.join(text[i : i + n]						).strip() for i in range(0      ,				len(__lowerCamelCase						)      ,				__lowerCamelCase						)]
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a					,      __a							=   [], []
					for title, text in zip(documents['title']      ,				documents['text']						):
										if text is not None:
															for passage in split_text(__lowerCamelCase						):
																				titles.append(title if title is not None else ''						)
																				texts.append(__lowerCamelCase						)
					return {"title": titles, "text": texts}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   ctx_tokenizer(
					    documents['title']      ,				documents['text']      ,				truncation=__lowerCamelCase      ,				padding='longest'      ,				return_tensors='pt'						)['input_ids']
					__a							=   ctx_encoder(input_ids.to(device=__lowerCamelCase						)      ,				return_dict=__lowerCamelCase						).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				):
					######################################
					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(__lowerCamelCase      ,				batched=__lowerCamelCase      ,				num_proc=processing_args.num_proc						)

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

					# And finally save your dataset
					__a							=   os.path.join(rag_example_args.output_dir      ,				'my_knowledge_dataset'						)
					dataset.save_to_disk(__lowerCamelCase						)
					# 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=__lowerCamelCase						)

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


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






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






@dataclass
class 						a__							:
			A__				:   int								=			field(
			    default=7_68      ,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}      ,						)
			A__				:   int								=			field(
			    default=1_28      ,						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_				:		Tuple       						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_				:		Union[str, Any]       						=       parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		lowerCamelCase_				:		Union[str, Any]       						=       rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)




