






"""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						:						Union[str, Any]					   =       logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE						:						Optional[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





@dataclass
class       snake_case_       :



	"""simple docstring"""

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





@dataclass
class       snake_case_       :



	"""simple docstring"""

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





@dataclass
class       snake_case_       :



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							int   			=					field(
	    default=7_68    ,							metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}    ,							)
	SCREAMING_SNAKE_CASE_:							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)

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










"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE						:						Dict					   =       '''Create a default config file for Accelerate with only a few flags set.'''



def  __lowerCamelCase					(					lowerCAmelCase__="no"       ,lowerCAmelCase__ = default_json_config_file       ,lowerCAmelCase__ = False				):
				A__							 =       Path(lowerCAmelCase__				)
				path.parent.mkdir(parents=lowerCAmelCase__       ,exist_ok=lowerCAmelCase__				)
				if path.exists():
								print(
								    f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
								return False
				A__							 =       mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
				A__							 =       {
				    'compute_environment': 'LOCAL_MACHINE',
				    'mixed_precision': mixed_precision,
				}
				if torch.cuda.is_available():
								A__							 =       torch.cuda.device_count()
								A__							 =       num_gpus
								A__							 =       False
								if num_gpus > 1:
												A__							 =       'MULTI_GPU'
								else:
												A__							 =       'NO'
				elif is_xpu_available() and use_xpu:
								A__							 =       torch.xpu.device_count()
								A__							 =       num_xpus
								A__							 =       False
								if num_xpus > 1:
												A__							 =       'MULTI_XPU'
								else:
												A__							 =       'NO'
				elif is_npu_available():
								A__							 =       torch.npu.device_count()
								A__							 =       num_npus
								A__							 =       False
								if num_npus > 1:
												A__							 =       'MULTI_NPU'
								else:
												A__							 =       'NO'
				else:
								A__							 =       0
								A__							 =       True
								A__							 =       1
								A__							 =       'NO'
				A__							 =       ClusterConfig(**lowerCAmelCase__				)
				config.to_json_file(lowerCAmelCase__				)
				return path



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       parser.add_parser('default'       ,parents=lowerCAmelCase__       ,help=lowerCAmelCase__       ,formatter_class=lowerCAmelCase__				)
				parser.add_argument(
				    '--config_file'       ,default=lowerCAmelCase__       ,help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )       ,dest='save_location'       ,)

				parser.add_argument(
				    '--mixed_precision'       ,choices=['no', 'fp16', 'bf16']       ,type=lowerCAmelCase__       ,help='Whether or not to use mixed precision training. '
				    'Choose between FP16 and BF16 (bfloat16) training. '
				    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,default='no'       ,)
				parser.set_defaults(func=lowerCAmelCase__				)
				return parser







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       write_basic_config(args.mixed_precision       ,args.save_location				)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''				)


