




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			    =logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE			    ='''cuda''' if torch.cuda.is_available() else '''cpu'''

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

def       a (_lowerCAmelCase  ):
							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       a (_lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase  ):
							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       a (_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
							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 			__magic_name__    :
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				str						   =       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'"}     ,	)
				SCREAMING_SNAKE_CASE__       :				Optional[str]						   =       field(
				    default=__UpperCAmelCase     ,	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(__UpperCAmelCase).parent / "test_run" / "dummy-kb")     ,	metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}     ,	)






@dataclass
class 			__magic_name__    :
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				Optional[int]						   =       field(
				    default=__UpperCAmelCase     ,	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 			__magic_name__    :
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__       :				int						   =       field(
				    default=768     ,	metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}     ,	)
				SCREAMING_SNAKE_CASE__       :				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			    =HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
			__SCREAMING_SNAKE_CASE			    =parser.parse_args_into_dataclasses()
			with TemporaryDirectory() as tmp_dir:
						__SCREAMING_SNAKE_CASE			    =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.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
							import botoa  # noqa: F401







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )

							A_			    =		{
							    "Version": "2012-10-17",
							    "Statement": [
							        {"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
							    ],
							}
							try:
														# create the role, associated with the chosen trust policy
														iam_client.create_role(
														    RoleName=UpperCamelCase_						,  AssumeRolePolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 ) )
														A_			    =		{
														    "Version": "2012-10-17",
														    "Statement": [
														        {
														            "Effect": "Allow",
														            "Action": [
														                "sagemaker:*",
														                "ecr:GetDownloadUrlForLayer",
														                "ecr:BatchGetImage",
														                "ecr:BatchCheckLayerAvailability",
														                "ecr:GetAuthorizationToken",
														                "cloudwatch:PutMetricData",
														                "cloudwatch:GetMetricData",
														                "cloudwatch:GetMetricStatistics",
														                "cloudwatch:ListMetrics",
														                "logs:CreateLogGroup",
														                "logs:CreateLogStream",
														                "logs:DescribeLogStreams",
														                "logs:PutLogEvents",
														                "logs:GetLogEvents",
														                "s3:CreateBucket",
														                "s3:ListBucket",
														                "s3:GetBucketLocation",
														                "s3:GetObject",
														                "s3:PutObject",
														            ],
														            "Resource": "*",
														        }
														    ],
														}
														# attach policy to role
														iam_client.put_role_policy(
														    RoleName=UpperCamelCase_						,  PolicyName=f"{role_name}_policy_permission"						,  PolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 )						,  )
							except iam_client.exceptions.EntityAlreadyExistsException:
														print(f"role {role_name} already exists. Using existing one" )







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )
							return iam_client.get_role(RoleName=UpperCamelCase_ )["Role"]["Arn"]







def       a_				(	):
							A_			    =		_ask_options(
							    "How do you want to authorize?"						,  ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]						,  UpperCamelCase_						,  )
							A_			    =		None
							if credentials_configuration == 0:
														A_			    =		_ask_field("Enter your AWS Profile name: [default] "						,  default="default" )
														A_			    =		aws_profile
							else:
														print(
														    "Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
														    "`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" )
														A_			    =		_ask_field("AWS Access Key ID: " )
														A_			    =		aws_access_key_id

														A_			    =		_ask_field("AWS Secret Access Key: " )
														A_			    =		aws_secret_access_key

							A_			    =		_ask_field("Enter your AWS Region: [us-east-1]"						,  default="us-east-1" )
							A_			    =		aws_region

							A_			    =		_ask_options(
							    "Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"						,  ["Provide IAM Role name", "Create new IAM role using credentials"]						,  UpperCamelCase_						,  )
							if role_management == 0:
														A_			    =		_ask_field("Enter your IAM role name: " )
							else:
														A_			    =		"accelerate_sagemaker_execution_role"
														print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
														_create_iam_role_for_sagemaker(UpperCamelCase_ )

							A_			    =		_ask_field(
							    "Do you want to use custom Docker image? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_custom_docker_image:
														A_			    =		_ask_field("Enter your Docker image: "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() )

							A_			    =		_ask_field(
							    "Do you want to provide SageMaker input channels with data locations? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_inputs_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_field(
							    "Do you want to enable SageMaker metrics? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_metrics_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_options(
							    "What is the distributed mode?"						,  ["No distributed training", "Data parallelism"]						,  _convert_sagemaker_distributed_mode						,  )
							A_			    =		{}
							A_			    =		_ask_field(
							    "Do you wish to optimize your script with torch dynamo?[yes/NO]:"						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							if use_dynamo:
														A_			    =		"dynamo_"
														A_			    =		_ask_options(
														    "Which dynamo backend would you like to use?"						,  [x.lower() for x in DYNAMO_BACKENDS]						,  _convert_dynamo_backend						,  default=2						,  )
														A_			    =		_ask_field(
														    "Do you want to customize the defaults sent to torch.compile? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )

														if use_custom_options:
																					A_			    =		_ask_options(
																					    "Which mode do you want to use?"						,  UpperCamelCase_						,  lambda UpperCamelCase_ : TORCH_DYNAMO_MODES[int(UpperCamelCase_ )]						,  default="default"						,  )
																					A_			    =		_ask_field(
																					    "Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
																					A_			    =		_ask_field(
																					    "Do you want to enable dynamic shape tracing? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		"Which EC2 instance type you want to use for your training?"
							if distributed_type != SageMakerDistributedType.NO:
														A_			    =		_ask_options(
														    UpperCamelCase_						,  UpperCamelCase_						,  lambda UpperCamelCase_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_ )] )
							else:
														eca_instance_query += "? [ml.p3.2xlarge]:"
														A_			    =		_ask_field(UpperCamelCase_						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  default="ml.p3.2xlarge" )

							A_			    =		1
							if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
														A_			    =		_ask_field(
														    "How many machines do you want use? [1]: "						,  UpperCamelCase_						,  default=1						,  )

							A_			    =		_ask_options(
							    "Do you wish to use FP16 or BF16 (mixed precision)?"						,  ["no", "fp16", "bf16", "fp8"]						,  _convert_mixed_precision						,  )

							if use_dynamo and mixed_precision == "no":
														print(
														    "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." )

							return SageMakerConfig(
							    image_uri=UpperCamelCase_						,  compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER						,  distributed_type=UpperCamelCase_						,  use_cpu=UpperCamelCase_						,  dynamo_config=UpperCamelCase_						,  eca_instance_type=UpperCamelCase_						,  profile=UpperCamelCase_						,  region=UpperCamelCase_						,  iam_role_name=UpperCamelCase_						,  mixed_precision=UpperCamelCase_						,  num_machines=UpperCamelCase_						,  sagemaker_inputs_file=UpperCamelCase_						,  sagemaker_metrics_file=UpperCamelCase_						,  )





