



import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__snake_case						:Union[str, Any]      		=     trt.Logger(trt.Logger.WARNING)
__snake_case						:int      		=     absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__snake_case						:Optional[Any]      		=     logging.getLogger(__name__)

__snake_case						:List[str]      		=     argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '''--onnx_model_path''',
    default=None,
    type=str,
    required=True,
    help='''Path to ONNX model: ''',
)

parser.add_argument(
    '''--output_dir''',
    default=None,
    type=str,
    required=True,
    help='''The output directory where the model checkpoints and predictions will be written.''',
)

# Other parameters

parser.add_argument(
    '''--tokenizer_name''',
    default='''''',
    type=str,
    required=True,
    help='''Pretrained tokenizer name or path if not the same as model_name''',
)

parser.add_argument(
    '''--version_2_with_negative''',
    action='''store_true''',
    help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
    '''--null_score_diff_threshold''',
    type=float,
    default=0.0,
    help='''If null_score - best_non_null is greater than the threshold predict null.''',
)

parser.add_argument(
    '''--max_seq_length''',
    default=384,
    type=int,
    help=(
        '''The maximum total input sequence length after WordPiece tokenization. Sequences '''
        '''longer than this will be truncated, and sequences shorter than this will be padded.'''
    ),
)
parser.add_argument(
    '''--doc_stride''',
    default=128,
    type=int,
    help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)

parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')

parser.add_argument(
    '''--n_best_size''',
    default=20,
    type=int,
    help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
    '''--max_answer_length''',
    default=30,
    type=int,
    help=(
        '''The maximum length of an answer that can be generated. This is needed because the start '''
        '''and end predictions are not conditioned on one another.'''
    ),
)

parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')

parser.add_argument(
    '''--dataset_name''',
    type=str,
    default=None,
    required=True,
    help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--dataset_config_name''',
    type=str,
    default=None,
    help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
    '''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
    '''--fp16''',
    action='''store_true''',
    help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
    '''--int8''',
    action='''store_true''',
    help='''Whether to use INT8''',
)

__snake_case						:Any      		=     parser.parse_args()

if args.tokenizer_name:
 __snake_case						:List[Any]      		=     AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
 raise ValueError(
     '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
     '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
 )

logger.info('''Training/evaluation parameters %s''', args)

__snake_case						:str      		=     args.per_device_eval_batch_size

__snake_case						:Union[str, Any]      		=     (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__snake_case						:Dict      		=     True

__snake_case						:List[str]      		=     '''temp_engine/bert-fp32.engine'''
if args.fpaa:
 __snake_case						:Any      		=     '''temp_engine/bert-fp16.engine'''
if args.inta:
 __snake_case						:Tuple      		=     '''temp_engine/bert-int8.engine'''

# import ONNX file
if not os.path.exists('''temp_engine'''):
 os.makedirs('''temp_engine''')

__snake_case						:Any      		=     1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
 with open(args.onnx_model_path, '''rb''') as model:
  if not parser.parse(model.read()):
   for error in range(parser.num_errors):
    print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
 __snake_case						:Any      		=     [network.get_input(i) for i in range(network.num_inputs)]
 __snake_case						:Optional[int]      		=     [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

 with builder.create_builder_config() as config:
  __snake_case						:Dict      		=     1 << 50
  if STRICT_TYPES:
   config.set_flag(trt.BuilderFlag.STRICT_TYPES)
  if args.fpaa:
   config.set_flag(trt.BuilderFlag.FPaa)
  if args.inta:
   config.set_flag(trt.BuilderFlag.INTa)
  __snake_case						:List[str]      		=     builder.create_optimization_profile()
  config.add_optimization_profile(profile)
  for i in range(len(input_names)):
   profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
  __snake_case						:List[Any]      		=     builder.build_engine(network, config)

  # serialize_engine and store in file (can be directly loaded and deserialized):
  with open(engine_name, '''wb''') as f:
   f.write(engine.serialize())





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				np.asarray(inputs['''input_ids''']   , dtype=np.intaa				)
    __a						       =				np.asarray(inputs['''attention_mask''']   , dtype=np.intaa				)
    __a						       =				np.asarray(inputs['''token_type_ids''']   , dtype=np.intaa				)

    # Copy inputs
    cuda.memcpy_htod_async(d_inputs[0]   , input_ids.ravel()   , _UpperCAmelCase				)
    cuda.memcpy_htod_async(d_inputs[1]   , attention_mask.ravel()   , _UpperCAmelCase				)
    cuda.memcpy_htod_async(d_inputs[2]   , token_type_ids.ravel()   , _UpperCAmelCase				)
    # start time
    __a						       =				time.time()
    # Run inference
    context.execute_async(
        bindings=[int(_UpperCAmelCase				) for d_inp in d_inputs] + [int(_UpperCAmelCase				), int(_UpperCAmelCase				)]   , stream_handle=stream.handle				)
    # Transfer predictions back from GPU
    cuda.memcpy_dtoh_async(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)
    cuda.memcpy_dtoh_async(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)
    # Synchronize the stream and take time
    stream.synchronize()
    # end time
    __a						       =				time.time()
    __a						       =				end_time - start_time
    __a						       =				(h_outputa, h_outputa)
    # print(outputs)
    return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__snake_case						:List[Any]      		=     Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
 datasets.utils.logging.set_verbosity_warning()
 transformers.utils.logging.set_verbosity_info()
else:
 datasets.utils.logging.set_verbosity_error()
 transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
 set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
 # Downloading and loading a dataset from the hub.
 __snake_case						:Dict      		=     load_dataset(args.dataset_name, args.dataset_config_name)
else:
 raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__snake_case						:Optional[int]      		=     raw_datasets['''validation'''].column_names

__snake_case						:Tuple      		=     '''question''' if '''question''' in column_names else column_names[0]
__snake_case						:List[Any]      		=     '''context''' if '''context''' in column_names else column_names[1]
__snake_case						:Dict      		=     '''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__snake_case						:Optional[Any]      		=     tokenizer.padding_side == '''right'''

if args.max_seq_length > tokenizer.model_max_length:
 logger.warning(
     f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
     f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
 )

__snake_case						:int      		=     min(args.max_seq_length, tokenizer.model_max_length)





def      __snake_case	(			_UpperCAmelCase				):
    # Some of the questions have lots of whitespace on the left, which is not useful and will make the
    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
    # left whitespace
    __a						       =				[q.lstrip() for q in examples[question_column_name]]

    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    __a						       =				tokenizer(
        examples[question_column_name if pad_on_right else context_column_name]   , examples[context_column_name if pad_on_right else question_column_name]   , truncation='''only_second''' if pad_on_right else '''only_first'''   , max_length=_UpperCAmelCase   , stride=args.doc_stride   , return_overflowing_tokens=_UpperCAmelCase   , return_offsets_mapping=_UpperCAmelCase   , padding='''max_length'''   , )

    # Since one example might give us several features if it has a long context, we need a map from a feature to
    # its corresponding example. This key gives us just that.
    __a						       =				tokenized_examples.pop('''overflow_to_sample_mapping'''				)

    # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
    # corresponding example_id and we will store the offset mappings.
    __a						       =				[]

    for i in range(len(tokenized_examples['''input_ids''']				)				):
        # Grab the sequence corresponding to that example (to know what is the context and what is the question).
        __a						       =				tokenized_examples.sequence_ids(_UpperCAmelCase				)
        __a						       =				1 if pad_on_right else 0

        # One example can give several spans, this is the index of the example containing this span of text.
        __a						       =				sample_mapping[i]
        tokenized_examples["example_id"].append(examples['''id'''][sample_index]				)

        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
        # position is part of the context or not.
        __a						       =				[
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]				)
        ]

    return tokenized_examples


__snake_case						:Optional[Any]      		=     raw_datasets['''validation''']
# Validation Feature Creation
__snake_case						:Tuple      		=     eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='''Running tokenizer on validation dataset''',
)

__snake_case						:Optional[Any]      		=     default_data_collator

__snake_case						:Union[str, Any]      		=     eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
__snake_case						:List[Any]      		=     DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase="eval"				):
    # Post-processing: we match the start logits and end logits to answers in the original context.
    __a						       =				postprocess_qa_predictions(
        examples=_UpperCAmelCase   , features=_UpperCAmelCase   , predictions=_UpperCAmelCase   , version_2_with_negative=args.version_2_with_negative   , n_best_size=args.n_best_size   , max_answer_length=args.max_answer_length   , null_score_diff_threshold=args.null_score_diff_threshold   , output_dir=args.output_dir   , prefix=_UpperCAmelCase   , )
    # Format the result to the format the metric expects.
    if args.version_2_with_negative:
        __a						       =				[
            {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
        ]
    else:
        __a						       =				[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

    __a						       =				[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
    return EvalPrediction(predictions=_UpperCAmelCase   , label_ids=_UpperCAmelCase				)


__snake_case						:Union[str, Any]      		=     load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')

# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
 # setup for TRT inferrence
 for i in range(len(input_names)):
  context.set_binding_shape(i, INPUT_SHAPE)
 assert context.all_binding_shapes_specified





 def      __snake_case	(			_UpperCAmelCase				):
     return trt.volume(engine.get_binding_shape(_UpperCAmelCase				)				) * engine.get_binding_dtype(_UpperCAmelCase				).itemsize

 # Allocate device memory for inputs and outputs.
 __snake_case						:Tuple      		=     [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

 # Allocate output buffer
 __snake_case						:List[Any]      		=     cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
 __snake_case						:Optional[int]      		=     cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
 __snake_case						:str      		=     cuda.mem_alloc(h_outputa.nbytes)
 __snake_case						:Any      		=     cuda.mem_alloc(h_outputa.nbytes)

 # Create a stream in which to copy inputs/outputs and run inference.
 __snake_case						:int      		=     cuda.Stream()

 # Evaluation
 logger.info('''***** Running Evaluation *****''')
 logger.info(f'  Num examples = {len(eval_dataset)}')
 logger.info(f'  Batch size = {args.per_device_eval_batch_size}')

 __snake_case						:int      		=     0.0
 __snake_case						:str      		=     0
 __snake_case						:Optional[Any]      		=     timeit.default_timer()

 __snake_case						:Dict      		=     None
 for step, batch in enumerate(eval_dataloader):
  __snake_case		,__snake_case						:Tuple      		=     model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
  total_time += infer_time
  niter += 1

  __snake_case		,__snake_case						:Tuple      		=     outputs
  __snake_case						:List[Any]      		=     torch.tensor(start_logits)
  __snake_case						:Dict      		=     torch.tensor(end_logits)

  # necessary to pad predictions and labels for being gathered
  __snake_case						:Dict      		=     accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
  __snake_case						:str      		=     accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

  __snake_case						:Union[str, Any]      		=     (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
  __snake_case						:List[str]      		=     logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

 if all_preds is not None:
  __snake_case						:Tuple      		=     nested_truncate(all_preds, len(eval_dataset))

 __snake_case						:Tuple      		=     timeit.default_timer() - start_time
 logger.info('''  Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
 # Inference time from TRT
 logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 1000 / niter))
 logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))
 logger.info('''Total Number of Inference =  %d''', niter)

__snake_case						:str      		=     post_processing_function(eval_examples, eval_dataset, all_preds)
__snake_case						:Any      		=     metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')










import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case						:List[Any]      		=     logging.getLogger(__name__)

class     _A       :



       def __init__(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				False



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           if not self.initialized:
               __a						       =				RagRetriever(
                   __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				True



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           self.retriever.index.init_index()





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return doc_ids, retrieved_doc_embeds


class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None):


           '''simple docstring'''

           if index is not None and index.is_initialized() and len(__SCREAMING_SNAKE_CASE) > 0:
               raise ValueError(
                   '''When using Ray for distributed fine-tuning, '''
                   '''you\'ll need to provide the paths instead, '''
                   '''as the dataset and the index are loaded '''
                   '''separately. More info in examples/rag/use_own_knowledge_dataset.py ''')
           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				retrieval_workers
           if len(self.retrieval_workers) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
                       for worker in self.retrieval_workers
                   ])



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           logger.info('''initializing retrieval''')

           if len(self.retrieval_workers) > 0:
               ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
           else:
               # Non-distributed training. Load index into this same process.
               self.index.init_index()



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           if len(self.retrieval_workers) > 0:
               # Select a random retrieval actor.
               __a						       =				self.retrieval_workers[random.randint(0     ,		len(self.retrieval_workers) - 1)]
               __a       ,   __a						       =				ray.get(random_worker.retrieve.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))
           else:
               __a       ,   __a						       =				self._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase			(		cls							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return super(__SCREAMING_SNAKE_CASE     ,		cls).get_tokenizers(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)





       @classmethod
       def 		_lowerCamelCase			(		cls							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				kwargs.pop('''config'''     ,		__SCREAMING_SNAKE_CASE) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     ,		config=__SCREAMING_SNAKE_CASE)
           __a						       =				rag_tokenizer.question_encoder
           __a						       =				rag_tokenizer.generator
           if indexed_dataset is not None:
               __a						       =				'''custom'''
               __a						       =				CustomHFIndex(config.retrieval_vector_size     ,		__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				cls._build_index(__SCREAMING_SNAKE_CASE)
           return cls(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		retrieval_workers=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		)





