




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


__SCREAMING_SNAKE_CASE 						=trt.Logger(trt.Logger.WARNING)
__SCREAMING_SNAKE_CASE 						=absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__SCREAMING_SNAKE_CASE 						=logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE 						=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""",
)

__SCREAMING_SNAKE_CASE 						=parser.parse_args()

if args.tokenizer_name:
			__SCREAMING_SNAKE_CASE 						=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)

__SCREAMING_SNAKE_CASE 						=args.per_device_eval_batch_size

__SCREAMING_SNAKE_CASE 						=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__SCREAMING_SNAKE_CASE 						=True

__SCREAMING_SNAKE_CASE 						="""temp_engine/bert-fp32.engine"""
if args.fpaa:
			__SCREAMING_SNAKE_CASE 						="""temp_engine/bert-fp16.engine"""
if args.inta:
			__SCREAMING_SNAKE_CASE 						="""temp_engine/bert-int8.engine"""

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

__SCREAMING_SNAKE_CASE 						=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
			__SCREAMING_SNAKE_CASE 						=[network.get_input(i) for i in range(network.num_inputs)]
			__SCREAMING_SNAKE_CASE 						=[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

			with builder.create_builder_config() as config:
						__SCREAMING_SNAKE_CASE 						=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)
						__SCREAMING_SNAKE_CASE 						=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)
						__SCREAMING_SNAKE_CASE 						=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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    np.asarray(inputs['''input_ids''']  ,    dtype=np.intaa     )
							SCREAMING_SNAKE_CASE_   	=    np.asarray(inputs['''attention_mask''']  ,    dtype=np.intaa     )
							SCREAMING_SNAKE_CASE_   	=    np.asarray(inputs['''token_type_ids''']  ,    dtype=np.intaa     )

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

__SCREAMING_SNAKE_CASE 						=raw_datasets["""validation"""].column_names

__SCREAMING_SNAKE_CASE 						="""question""" if """question""" in column_names else column_names[0]
__SCREAMING_SNAKE_CASE 						="""context""" if """context""" in column_names else column_names[1]
__SCREAMING_SNAKE_CASE 						="""answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__SCREAMING_SNAKE_CASE 						=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}."""
			)

__SCREAMING_SNAKE_CASE 						=min(args.max_seq_length, tokenizer.model_max_length)

def 			a   (_lowerCAmelCase     ):
							# 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
							SCREAMING_SNAKE_CASE_   	=    [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.
							SCREAMING_SNAKE_CASE_   	=    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=_lowerCAmelCase  ,    stride=args.doc_stride  ,    return_overflowing_tokens=_lowerCAmelCase  ,    return_offsets_mapping=_lowerCAmelCase  ,    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.
							SCREAMING_SNAKE_CASE_   	=    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.
							SCREAMING_SNAKE_CASE_   	=    []

							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).
														SCREAMING_SNAKE_CASE_   	=    tokenized_examples.sequence_ids(_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    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.
														SCREAMING_SNAKE_CASE_   	=    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.
														SCREAMING_SNAKE_CASE_   	=    [
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]     )
														]

							return tokenized_examples


__SCREAMING_SNAKE_CASE 						=raw_datasets["""validation"""]
# Validation Feature Creation
__SCREAMING_SNAKE_CASE 						=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""",
)

__SCREAMING_SNAKE_CASE 						=default_data_collator

__SCREAMING_SNAKE_CASE 						=eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
__SCREAMING_SNAKE_CASE 						=DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

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

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


__SCREAMING_SNAKE_CASE 						=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 			a   (_lowerCAmelCase     ):
										return trt.volume(engine.get_binding_shape(_lowerCAmelCase     )     ) * engine.get_binding_dtype(_lowerCAmelCase     ).itemsize

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

			# Allocate output buffer
			__SCREAMING_SNAKE_CASE 						=cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
			__SCREAMING_SNAKE_CASE 						=cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
			__SCREAMING_SNAKE_CASE 						=cuda.mem_alloc(h_outputa.nbytes)
			__SCREAMING_SNAKE_CASE 						=cuda.mem_alloc(h_outputa.nbytes)

			# Create a stream in which to copy inputs/outputs and run inference.
			__SCREAMING_SNAKE_CASE 						=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}""")

			__SCREAMING_SNAKE_CASE 						=0.0
			__SCREAMING_SNAKE_CASE 						=0
			__SCREAMING_SNAKE_CASE 						=timeit.default_timer()

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

						__SCREAMING_SNAKE_CASE						,    __SCREAMING_SNAKE_CASE 						=outputs
						__SCREAMING_SNAKE_CASE 						=torch.tensor(start_logits)
						__SCREAMING_SNAKE_CASE 						=torch.tensor(end_logits)

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

						__SCREAMING_SNAKE_CASE 						=(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
						__SCREAMING_SNAKE_CASE 						=logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

			__SCREAMING_SNAKE_CASE 						=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 * 1_000 / niter))
			logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1_000))






			logger.info("""Total Number of Inference =  %d""", niter)

__SCREAMING_SNAKE_CASE 						=post_processing_function(eval_examples, eval_dataset, all_preds)
__SCREAMING_SNAKE_CASE 						=metric.compute(predictions=prediction.predictions, references=prediction.label_ids)






logger.info(f"""Evaluation metrics: {eval_metric}""")








import re
import string

import numpy as np

import datasets


__SCREAMING_SNAKE_CASE 						="""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

__SCREAMING_SNAKE_CASE 						="""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

__SCREAMING_SNAKE_CASE 						="""
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,  _KWARGS_DESCRIPTION)
class  __magic_name__						(  datasets.Metric):
				'''simple docstring'''







				def     _A							(							self:   Optional[int]		):
											return datasets.MetricInfo(
											    description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
											        {
											            '''predictions''': datasets.Value('''string'''		,					id='''sequence'''		),
											            '''references''': datasets.Value('''string'''		,					id='''sequence'''		),
											        }		)		,					reference_urls=[]		,					)





				def     _A							(							self:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=False		,					_lowerCamelCase:   List[str]=False		,					_lowerCamelCase:   List[str]=False		,					):
											if regexes_to_ignore is not None:
																		for s in regexes_to_ignore:
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in predictions]		)
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in references]		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)

											if ignore_case:
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)

											if ignore_punctuation:
																		SCREAMING_SNAKE_CASE_   	=    string.punctuation.maketrans(''''''		,					''''''		,					string.punctuation		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											if ignore_numbers:
																		SCREAMING_SNAKE_CASE_   	=    string.digits.maketrans(''''''		,					''''''		,					string.digits		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    predictions == references

											return {"exact_match": np.mean(_lowerCamelCase		) * 1_00}


