




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase						    =  logging.get_logger()

@dataclass
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Tensor     ,  UpperCamelCase :							Tensor				)->Any:
      __SCREAMING_SNAKE_CASE       :    int       =	len(list(m.modules()				)				) == 1 or isinstance(UpperCamelCase     ,  nn.Convad				) or isinstance(UpperCamelCase     ,  nn.BatchNormad				)
      if has_not_submodules:
       self.traced.append(UpperCamelCase				)





     def __call__(       self :							List[str]     ,  UpperCamelCase :							Tensor				)->List[str]:
      for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook				)				)
      self.module(UpperCamelCase				)
      [x.remove() for x in self.handles]
      return self






     @property
     def 	__snake_case    (       self :							List[Any]				)->List[Any]:
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda UpperCamelCase				: len(list(x.state_dict().keys()				)				) > 0     ,  self.traced				)				)






@dataclass
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			42
     lowerCAmelCase					=			42
     lowerCAmelCase					=			0
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )
     lowerCAmelCase					=			field(default_factory=UpperCamelCase )





     def __call__(       self :							Any     ,  UpperCamelCase :							Tensor				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	Tracker(self.dest				)(UpperCamelCase				).parametrized
      __SCREAMING_SNAKE_CASE       :    int       =	Tracker(self.src				)(UpperCamelCase				).parametrized

      __SCREAMING_SNAKE_CASE       :    Dict       =	list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.src_skip     ,  UpperCamelCase				)				)
      __SCREAMING_SNAKE_CASE       :    Any       =	list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.dest_skip     ,  UpperCamelCase				)				)

      if len(UpperCamelCase				) != len(UpperCamelCase				):
       raise Exception(
           F"""Numbers of operations are different. Source module has {len(UpperCamelCase				)} operations while"""
           F""" destination module has {len(UpperCamelCase				)}."""				)

      for dest_m, src_m in zip(UpperCamelCase     ,  UpperCamelCase				):
       dest_m.load_state_dict(src_m.state_dict()				)
       if self.verbose == 1:
        print(F"""Transfered from={src_m} to={dest_m}"""				)






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     ResNetConfig		,	__lowerCamelCase   :     Path		,	__lowerCamelCase   :     bool = True     ):



 """simple docstring"""





 print(F"""Converting {name}..."""     )
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	timm.create_model(__lowerCamelCase		,	pretrained=__lowerCamelCase     ).eval()
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	ResNetForImageClassification(__lowerCamelCase     ).eval()
  __SCREAMING_SNAKE_CASE       :    int       =	ModuleTransfer(src=__lowerCamelCase		,	dest=__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.randn((1, 3, 224, 224)     )
  module_transfer(__lowerCamelCase     )

 assert torch.allclose(from_model(__lowerCamelCase     )		,	our_model(__lowerCamelCase     ).logits     ), "The model logits don't match the original one."

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	F"""resnet{"-".join(name.split("resnet"     )     )}"""
 print(__lowerCamelCase     )

 if push_to_hub:
  our_model.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,	commit_message="Add model"		,	use_temp_dir=__lowerCamelCase		,	)

  # we can use the convnext one
  __SCREAMING_SNAKE_CASE       :    int       =	AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"     )
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / checkpoint_name		,	commit_message="Add image processor"		,	use_temp_dir=__lowerCamelCase		,	)

  print(F"""Pushed {checkpoint_name}"""     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Path		,	__lowerCamelCase   :     str = None		,	__lowerCamelCase   :     bool = True     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"imagenet-1k-id2label.json"
 __SCREAMING_SNAKE_CASE       :    int       =	1000
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	(1, num_labels)

 __SCREAMING_SNAKE_CASE       :    int       =	"huggingface/label-files"
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_labels
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	json.load(open(hf_hub_download(__lowerCamelCase		,	__lowerCamelCase		,	repo_type="dataset"     )		,	"r"     )     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{int(__lowerCamelCase     ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	idalabel
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{v: k for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	partial(__lowerCamelCase		,	num_labels=__lowerCamelCase		,	idalabel=__lowerCamelCase		,	labelaid=__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Tuple       =	{
     "resnet18": ImageNetPreTrainedConfig(
         depths=[2, 2, 2, 2]		,	hidden_sizes=[64, 128, 256, 512]		,	layer_type="basic"     ),
     "resnet26": ImageNetPreTrainedConfig(
         depths=[2, 2, 2, 2]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
     "resnet34": ImageNetPreTrainedConfig(
         depths=[3, 4, 6, 3]		,	hidden_sizes=[64, 128, 256, 512]		,	layer_type="basic"     ),
     "resnet50": ImageNetPreTrainedConfig(
         depths=[3, 4, 6, 3]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
     "resnet101": ImageNetPreTrainedConfig(
         depths=[3, 4, 23, 3]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
     "resnet152": ImageNetPreTrainedConfig(
         depths=[3, 8, 36, 3]		,	hidden_sizes=[256, 512, 1024, 2048]		,	layer_type="bottleneck"     ),
 }

 if model_name:
  convert_weight_and_push(__lowerCamelCase		,	names_to_config[model_name]		,	__lowerCamelCase		,	__lowerCamelCase     )
 else:
  for model_name, config in names_to_config.items():
   convert_weight_and_push(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
 return config, expected_shape


if __name__ == "__main__":
      _lowerCamelCase						    =  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
              """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      _lowerCamelCase						    =  parser.parse_args()
      _lowerCamelCase						    =  args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, 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


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

_lowerCamelCase						    =  logging.getLogger(__name__)

_lowerCamelCase						    =  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""",
)

_lowerCamelCase						    =  parser.parse_args()

if args.tokenizer_name:
      _lowerCamelCase						    =  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)

_lowerCamelCase						    =  args.per_device_eval_batch_size

_lowerCamelCase						    =  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCamelCase						    =  True

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

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

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

      with builder.create_builder_config() as config:
            _lowerCamelCase						    =  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)
            _lowerCamelCase						    =  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)
            _lowerCamelCase						    =  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 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	np.asarray(inputs["input_ids"]		,	dtype=np.intaa     )
 __SCREAMING_SNAKE_CASE       :    Any       =	np.asarray(inputs["attention_mask"]		,	dtype=np.intaa     )
 __SCREAMING_SNAKE_CASE       :    str       =	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       :    str       =	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       :    List[Any]       =	time.time()
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	end_time - start_time
 __SCREAMING_SNAKE_CASE       :    Tuple       =	(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.
_lowerCamelCase						    =  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.
      _lowerCamelCase						    =  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.

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

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

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

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, 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.
 __SCREAMING_SNAKE_CASE       :    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.
 __SCREAMING_SNAKE_CASE       :    Union[str, 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.
 __SCREAMING_SNAKE_CASE       :    List[str]       =	[]

 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       :    Optional[Any]       =	tokenized_examples.sequence_ids(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, 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.
  __SCREAMING_SNAKE_CASE       :    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.
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[
      (o if sequence_ids[k] == context_index else None)
      for k, o in enumerate(tokenized_examples["offset_mapping"][i]     )
  ]

 return tokenized_examples


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

_lowerCamelCase						    =  default_data_collator

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int="eval"     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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       :    Optional[int]       =	[
      {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
  ]
 else:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[{"id": k, "prediction_text": v} for k, v in predictions.items()]

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
 return EvalPrediction(predictions=__lowerCamelCase		,	label_ids=__lowerCamelCase     )


_lowerCamelCase						    =  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 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]     ):



       """simple docstring"""





       return trt.volume(engine.get_binding_shape(__lowerCamelCase     )     ) * engine.get_binding_dtype(__lowerCamelCase     ).itemsize

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

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

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

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

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

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

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

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

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

      _lowerCamelCase						    =  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)

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






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






