






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


a		:       Any			     = trt.Logger(trt.Logger.WARNING)
a		:       Dict			     = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

a		:       Union[str, Any]			     = logging.getLogger(__name__)

a		:       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",
)

a		:       str			     = parser.parse_args()

if args.tokenizer_name:
       a		:       Tuple			     = 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)

a		:       Any			     = args.per_device_eval_batch_size

a		:       Optional[Any]			     = (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a		:       List[str]			     = True

a		:       Optional[int]			     = "temp_engine/bert-fp32.engine"
if args.fpaa:
       a		:       Optional[int]			     = "temp_engine/bert-fp16.engine"
if args.inta:
       a		:       Dict			     = "temp_engine/bert-int8.engine"

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

a		:       Optional[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
       a		:       List[str]			     = [network.get_input(i) for i in range(network.num_inputs)]
       a		:       Optional[Any]			     = [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

       with builder.create_builder_config() as config:
              a		:       Optional[int]			     = 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)
              a		:       Optional[int]			     = 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)
              a		:       Union[str, 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 				lowerCamelCase__      (    __lowerCamelCase			:     List[Any]  ,		__lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Optional[int]  ,		__lowerCamelCase			:     Optional[int]  ,		__lowerCamelCase			:     Any  ,		__lowerCamelCase			:     int  ,		__lowerCamelCase			:     Any  ,		__lowerCamelCase			:     Tuple		):
  __UpperCAmelCase							:	Tuple           =  np.asarray(inputs["""input_ids"""]  ,		dtype=np.intaa		)
  __UpperCAmelCase							:	str           =  np.asarray(inputs["""attention_mask"""]  ,		dtype=np.intaa		)
  __UpperCAmelCase							:	List[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							:	Optional[int]           =  time.time()
  __UpperCAmelCase							:	Optional[Any]           =  end_time - start_time
  __UpperCAmelCase							:	Optional[Any]           =  (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.
a		:       Optional[int]			     = 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.
       a		:       Optional[int]			     = 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.

a		:       Optional[int]			     = raw_datasets["validation"].column_names

a		:       str			     = "question" if "question" in column_names else column_names[0]
a		:       Union[str, Any]			     = "context" if "context" in column_names else column_names[1]
a		:       Any			     = "answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
a		:       List[str]			     = 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}."""
       )

a		:       List[Any]			     = min(args.max_seq_length, tokenizer.model_max_length)






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[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							:	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.
  __UpperCAmelCase							:	Dict           =  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							:	int           =  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							:	Optional[int]           =  []

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

  return tokenized_examples


a		:       Tuple			     = raw_datasets["validation"]
# Validation Feature Creation
a		:       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",
)

a		:       int			     = default_data_collator

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






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Union[str, Any]="eval"		):
  # Post-processing: we match the start logits and end logits to answers in the original context.
  __UpperCAmelCase							:	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:
    __UpperCAmelCase							:	Any           =  [
        {"""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							:	int           =  [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=__lowerCamelCase  ,		label_ids=__lowerCamelCase		)


a		:       Tuple			     = 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			:     Dict		):
         return trt.volume(engine.get_binding_shape(__lowerCamelCase		)		) * engine.get_binding_dtype(__lowerCamelCase		).itemsize

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

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

       # Create a stream in which to copy inputs/outputs and run inference.
       a		:       List[Any]			     = 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}""")

       a		:       Optional[Any]			     = 0.0
       a		:       int			     = 0
       a		:       Tuple			     = timeit.default_timer()

       a		:       Optional[int]			     = None
       for step, batch in enumerate(eval_dataloader):
              a     ,a		:       Optional[int]			     = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
              total_time += infer_time
              niter += 1

              a     ,a		:       List[str]			     = outputs
              a		:       List[str]			     = torch.tensor(start_logits)
              a		:       Optional[Any]			     = torch.tensor(end_logits)

              # necessary to pad predictions and labels for being gathered
              a		:       Optional[Any]			     = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
              a		:       Tuple			     = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

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

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

       a		:       str			     = 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)

a		:       str			     = post_processing_function(eval_examples, eval_dataset, all_preds)
a		:       Any			     = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







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










import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__   )
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	field(default='automatic-speech-recognition'							,       metadata={'include_in_asdict_even_if_is_default': True}   )
      a  : ClassVar[Features]							       =	Features({'audio': Audio()}   )
      a  : ClassVar[Features]							       =	Features({'transcription': Value('string'   )}   )
      a  : str							       =	"audio"
      a  : str							       =	"transcription"
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			Optional[int]					)							->      str:
        if self.audio_column not in features:
          raise ValueError(f"""Column {self.audio_column} is not present in features."""					)
        if not isinstance(features[self.audio_column]		,      __lowercase					):
          raise ValueError(f"""Column {self.audio_column} is not an Audio type."""					)
        __UpperCAmelCase							:	int           =  copy.deepcopy(self					)
        __UpperCAmelCase							:	str           =  self.input_schema.copy()
        __UpperCAmelCase							:	List[str]           =  features[self.audio_column]
        __UpperCAmelCase							:	Optional[Any]           =  input_schema
        return task_template




      @property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Dict[str, str]:
        return {self.audio_column: "audio", self.transcription_column: "transcription"}


