

import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    Optional[int]:
				_UpperCAmelCase    :						List[str]							=      ASTConfig()

				if "10-10" in model_name:
								pass
				elif "speech-commands" in model_name:
								_UpperCAmelCase    :						Tuple							=      128
				elif "12-12" in model_name:
								_UpperCAmelCase    :						List[str]							=      12
								_UpperCAmelCase    :						Optional[int]							=      12
				elif "14-14" in model_name:
								_UpperCAmelCase    :						Optional[Any]							=      14
								_UpperCAmelCase    :						List[Any]							=      14
				elif "16-16" in model_name:
								_UpperCAmelCase    :						Any							=      16
								_UpperCAmelCase    :						Dict							=      16
				else:
								raise ValueError("Model not supported"	)

				_UpperCAmelCase    :						Tuple							=      "huggingface/label-files"
				if "speech-commands" in model_name:
								_UpperCAmelCase    :						Dict							=      35
								_UpperCAmelCase    :						Any							=      "speech-commands-v2-id2label.json"
				else:
								_UpperCAmelCase    :						Optional[Any]							=      527
								_UpperCAmelCase    :						List[str]							=      "audioset-id2label.json"

				_UpperCAmelCase    :						Union[str, Any]							=      json.load(open(hf_hub_download(lowerCAmelCase					, lowerCAmelCase					, repo_type="dataset"	)					, "r"	)	)
				_UpperCAmelCase    :						List[Any]							=      {int(lowerCAmelCase	): v for k, v in idalabel.items()}
				_UpperCAmelCase    :						List[Any]							=      idalabel
				_UpperCAmelCase    :						str							=      {v: k for k, v in idalabel.items()}

				return config

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    int:
				if "module.v" in name:
								_UpperCAmelCase    :						Dict							=      name.replace("module.v"					, "audio_spectrogram_transformer"	)
				if "cls_token" in name:
								_UpperCAmelCase    :						str							=      name.replace("cls_token"					, "embeddings.cls_token"	)
				if "dist_token" in name:
								_UpperCAmelCase    :						List[Any]							=      name.replace("dist_token"					, "embeddings.distillation_token"	)
				if "pos_embed" in name:
								_UpperCAmelCase    :						Optional[int]							=      name.replace("pos_embed"					, "embeddings.position_embeddings"	)
				if "patch_embed.proj" in name:
								_UpperCAmelCase    :						str							=      name.replace("patch_embed.proj"					, "embeddings.patch_embeddings.projection"	)
				# transformer blocks
				if "blocks" in name:
								_UpperCAmelCase    :						List[str]							=      name.replace("blocks"					, "encoder.layer"	)
				if "attn.proj" in name:
								_UpperCAmelCase    :						Optional[Any]							=      name.replace("attn.proj"					, "attention.output.dense"	)
				if "attn" in name:
								_UpperCAmelCase    :						Optional[Any]							=      name.replace("attn"					, "attention.self"	)
				if "norm1" in name:
								_UpperCAmelCase    :						Union[str, Any]							=      name.replace("norm1"					, "layernorm_before"	)
				if "norm2" in name:
								_UpperCAmelCase    :						int							=      name.replace("norm2"					, "layernorm_after"	)
				if "mlp.fc1" in name:
								_UpperCAmelCase    :						Tuple							=      name.replace("mlp.fc1"					, "intermediate.dense"	)
				if "mlp.fc2" in name:
								_UpperCAmelCase    :						Optional[int]							=      name.replace("mlp.fc2"					, "output.dense"	)
				# final layernorm
				if "audio_spectrogram_transformer.norm" in name:
								_UpperCAmelCase    :						List[str]							=      name.replace("audio_spectrogram_transformer.norm"					, "audio_spectrogram_transformer.layernorm"	)
				# classifier head
				if "module.mlp_head.0" in name:
								_UpperCAmelCase    :						Optional[Any]							=      name.replace("module.mlp_head.0"					, "classifier.layernorm"	)
				if "module.mlp_head.1" in name:
								_UpperCAmelCase    :						Optional[Any]							=      name.replace("module.mlp_head.1"					, "classifier.dense"	)

				return name

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[Any]					, lowerCAmelCase:					Any	)				->    List[Any]:
				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase    :						Tuple							=      orig_state_dict.pop(lowerCAmelCase	)

								if "qkv" in key:
												_UpperCAmelCase    :						Union[str, Any]							=      key.split("."	)
												_UpperCAmelCase    :						Union[str, Any]							=      int(key_split[3]	)
												_UpperCAmelCase    :						List[Any]							=      config.hidden_size
												if "weight" in key:
																_UpperCAmelCase    :						Optional[Any]							=      val[:dim, :]
																_UpperCAmelCase    :						int							=      val[dim : dim * 2, :]
																_UpperCAmelCase    :						Dict							=      val[-dim:, :]
												else:
																_UpperCAmelCase    :						int							=      val[:dim]
																_UpperCAmelCase    :						Union[str, Any]							=      val[dim : dim * 2]
																_UpperCAmelCase    :						List[Any]							=      val[-dim:]
								else:
												_UpperCAmelCase    :						List[Any]							=      val

				return orig_state_dict

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]	)				->    int:
				_UpperCAmelCase    :						List[str]							=      [
				    "module.v.head.weight",
				    "module.v.head.bias",
				    "module.v.head_dist.weight",
				    "module.v.head_dist.bias",
				]
				for k in ignore_keys:
								state_dict.pop(lowerCAmelCase					, lowerCAmelCase	)

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					Union[str, Any]					, lowerCAmelCase:					Tuple=False	)				->    Dict:
				_UpperCAmelCase    :						List[str]							=      get_audio_spectrogram_transformer_config(lowerCAmelCase	)

				_UpperCAmelCase    :						Tuple							=      {
				    "ast-finetuned-audioset-10-10-0.4593": (
				        "https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
				    ),
				    "ast-finetuned-audioset-10-10-0.450": (
				        "https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
				    ),
				    "ast-finetuned-audioset-10-10-0.448": (
				        "https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
				    ),
				    "ast-finetuned-audioset-10-10-0.448-v2": (
				        "https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
				    ),
				    "ast-finetuned-audioset-12-12-0.447": (
				        "https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
				    ),
				    "ast-finetuned-audioset-14-14-0.443": (
				        "https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
				    ),
				    "ast-finetuned-audioset-16-16-0.442": (
				        "https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
				    ),
				    "ast-finetuned-speech-commands-v2": (
				        "https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
				    ),
				}

				# load original state_dict
				_UpperCAmelCase    :						Union[str, Any]							=      model_name_to_url[model_name]
				_UpperCAmelCase    :						Optional[int]							=      torch.hub.load_state_dict_from_url(lowerCAmelCase					, map_location="cpu"	)
				# remove some keys
				remove_keys(lowerCAmelCase	)
				# rename some keys
				_UpperCAmelCase    :						List[Any]							=      convert_state_dict(lowerCAmelCase					, lowerCAmelCase	)

				# load 🤗 model
				_UpperCAmelCase    :						str							=      ASTForAudioClassification(lowerCAmelCase	)
				model.eval()

				model.load_state_dict(lowerCAmelCase	)

				# verify outputs on dummy input
				# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
				_UpperCAmelCase    :						Union[str, Any]							=      -4.2_677_393 if "speech-commands" not in model_name else -6.845_978
				_UpperCAmelCase    :						Union[str, Any]							=      4.5_689_974 if "speech-commands" not in model_name else 5.5_654_526
				_UpperCAmelCase    :						List[str]							=      1024 if "speech-commands" not in model_name else 128
				_UpperCAmelCase    :						Dict							=      ASTFeatureExtractor(mean=lowerCAmelCase					, std=lowerCAmelCase					, max_length=lowerCAmelCase	)

				if "speech-commands" in model_name:
								_UpperCAmelCase    :						Any							=      load_dataset("speech_commands"					, "v0.02"					, split="validation"	)
								_UpperCAmelCase    :						int							=      dataset[0]["audio"]["array"]
				else:
								_UpperCAmelCase    :						Optional[Any]							=      hf_hub_download(
								    repo_id="nielsr/audio-spectogram-transformer-checkpoint"					, filename="sample_audio.flac"					, repo_type="dataset"					, )

								_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      torchaudio.load(lowerCAmelCase	)
								_UpperCAmelCase    :						Union[str, Any]							=      waveform.squeeze().numpy()

				_UpperCAmelCase    :						List[str]							=      feature_extractor(lowerCAmelCase					, sampling_rate=1_6000					, return_tensors="pt"	)

				# forward pass
				_UpperCAmelCase    :						Optional[Any]							=      model(**lowerCAmelCase	)
				_UpperCAmelCase    :						Optional[int]							=      outputs.logits

				if model_name == "ast-finetuned-audioset-10-10-0.4593":
								_UpperCAmelCase    :						Union[str, Any]							=      torch.tensor([-0.8_760, -7.0_042, -8.6_602]	)
				elif model_name == "ast-finetuned-audioset-10-10-0.450":
								_UpperCAmelCase    :						Tuple							=      torch.tensor([-1.1_986, -7.0_903, -8.2_718]	)
				elif model_name == "ast-finetuned-audioset-10-10-0.448":
								_UpperCAmelCase    :						Tuple							=      torch.tensor([-2.6_128, -8.0_080, -9.4_344]	)
				elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
								_UpperCAmelCase    :						Any							=      torch.tensor([-1.5_080, -7.4_534, -8.8_917]	)
				elif model_name == "ast-finetuned-audioset-12-12-0.447":
								_UpperCAmelCase    :						int							=      torch.tensor([-0.5_050, -6.5_833, -8.0_843]	)
				elif model_name == "ast-finetuned-audioset-14-14-0.443":
								_UpperCAmelCase    :						List[str]							=      torch.tensor([-0.3_826, -7.0_336, -8.2_413]	)
				elif model_name == "ast-finetuned-audioset-16-16-0.442":
								_UpperCAmelCase    :						Union[str, Any]							=      torch.tensor([-1.2_113, -6.9_101, -8.3_470]	)
				elif model_name == "ast-finetuned-speech-commands-v2":
								_UpperCAmelCase    :						str							=      torch.tensor([6.1_589, -8.0_566, -8.7_984]	)
				else:
								raise ValueError("Unknown model name"	)
				if not torch.allclose(logits[0, :3]					, lowerCAmelCase					, atol=1E-4	):
								raise ValueError("Logits don't match"	)
				print("Looks ok!"	)

				if pytorch_dump_folder_path is not None:
								Path(lowerCAmelCase	).mkdir(exist_ok=lowerCAmelCase	)
								print(F'Saving model {model_name} to {pytorch_dump_folder_path}'	)
								model.save_pretrained(lowerCAmelCase	)
								print(F'Saving feature extractor to {pytorch_dump_folder_path}'	)
								feature_extractor.save_pretrained(lowerCAmelCase	)

				if push_to_hub:
								print("Pushing model and feature extractor to the hub..."	)
								model.push_to_hub(F'MIT/{model_name}'	)
								feature_extractor.push_to_hub(F'MIT/{model_name}'	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='ast-finetuned-audioset-10-10-0.4593',
					    type=str,
					    help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)

					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Any					, lowerCAmelCase:					str					, lowerCAmelCase:					str					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					Dict					, lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						Dict							=      np.asarray(inputs["input_ids"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						List[str]							=      np.asarray(inputs["attention_mask"]					, dtype=np.intaa	)
				_UpperCAmelCase    :						Union[str, Any]							=      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
				_UpperCAmelCase    :						Dict							=      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
				_UpperCAmelCase    :						Any							=      time.time()
				_UpperCAmelCase    :						Any							=      end_time - start_time
				_UpperCAmelCase    :						Optional[int]							=      (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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    Any:
				# 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
				_UpperCAmelCase    :						Optional[Any]							=      [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.
				_UpperCAmelCase    :						List[str]							=      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.
				_UpperCAmelCase    :						List[Any]							=      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.
				_UpperCAmelCase    :						Tuple							=      []

				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).
								_UpperCAmelCase    :						Tuple							=      tokenized_examples.sequence_ids(lowerCAmelCase	)
								_UpperCAmelCase    :						Optional[Any]							=      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.
								_UpperCAmelCase    :						List[str]							=      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.
								_UpperCAmelCase    :						Any							=      [
								    (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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					Dict					, lowerCAmelCase:					str					, lowerCAmelCase:					Optional[Any]="eval"	)				->    Union[str, Any]:
				# Post-processing: we match the start logits and end logits to answers in the original context.
				_UpperCAmelCase    :						Tuple							=      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:
								_UpperCAmelCase    :						Optional[int]							=      [
								    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
								]
				else:
								_UpperCAmelCase    :						Optional[Any]							=      [{"id": k, "prediction_text": v} for k, v in predictions.items()]

				_UpperCAmelCase    :						Optional[int]							=      [{"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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str	)				->    List[str]:
									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 * 1000 / niter))
					logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))





					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}''')


