
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


lowercase  : Optional[int]						   =							trt.Logger(trt.Logger.WARNING)
lowercase  : int						   =							absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowercase  : Any						   =							logging.getLogger(__name__)

lowercase  : Any						   =							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""",
)

lowercase  : List[str]						   =							parser.parse_args()

if args.tokenizer_name:
  lowercase  : str						   =							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)

lowercase  : int						   =							args.per_device_eval_batch_size

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

# TRT Engine properties
lowercase  : int						   =							True

lowercase  : Dict						   =							"""temp_engine/bert-fp32.engine"""
if args.fpaa:
  lowercase  : str						   =							"""temp_engine/bert-fp16.engine"""
if args.inta:
  lowercase  : Tuple						   =							"""temp_engine/bert-int8.engine"""

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

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

  with builder.create_builder_config() as config:
    lowercase  : List[Any]						   =							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)
    lowercase  : List[str]						   =							builder.create_optimization_profile()
    config.add_optimization_profile(profile)
    for i in range(len(input_names)):
      profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
    lowercase  : Tuple						   =							builder.build_engine(network, config)

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					List[str]	     =				np.asarray(inputs["""input_ids"""] ,     dtype=np.intaa    )
       lowercase							:					Optional[Any]	     =				np.asarray(inputs["""attention_mask"""] ,     dtype=np.intaa    )
       lowercase							:					List[Any]	     =				np.asarray(inputs["""token_type_ids"""] ,     dtype=np.intaa    )

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

lowercase  : Any						   =							raw_datasets["""validation"""].column_names

lowercase  : Optional[int]						   =							"""question""" if """question""" in column_names else column_names[0]
lowercase  : List[Any]						   =							"""context""" if """context""" in column_names else column_names[1]
lowercase  : Dict						   =							"""answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowercase  : Optional[int]						   =							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}.'''
  )

lowercase  : List[str]						   =							min(args.max_seq_length, tokenizer.model_max_length)







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   int:
       # 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
       lowercase							:					Tuple	     =				[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.
       lowercase							:					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=SCREAMING_SNAKE_CASE__ ,     stride=args.doc_stride ,     return_overflowing_tokens=SCREAMING_SNAKE_CASE__ ,     return_offsets_mapping=SCREAMING_SNAKE_CASE__ ,     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.
       lowercase							:					Tuple	     =				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.
       lowercase							:					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).
              lowercase							:					List[str]	     =				tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE__    )
              lowercase							:					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.
              lowercase							:					Optional[int]	     =				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.
              lowercase							:					Optional[Any]	     =				[
                  (o if sequence_ids[k] == context_index else None)
                  for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]    )
              ]

       return tokenized_examples


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

lowercase  : Dict						   =							default_data_collator

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__="eval"    )			->   Optional[Any]:
       # Post-processing: we match the start logits and end logits to answers in the original context.
       lowercase							:					Tuple	     =				postprocess_qa_predictions(
           examples=SCREAMING_SNAKE_CASE__ ,     features=SCREAMING_SNAKE_CASE__ ,     predictions=SCREAMING_SNAKE_CASE__ ,     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=SCREAMING_SNAKE_CASE__ ,     )
       # Format the result to the format the metric expects.
       if args.version_2_with_negative:
              lowercase							:					List[str]	     =				[
                  {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
              ]
       else:
              lowercase							:					List[Any]	     =				[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

       lowercase							:					int	     =				[{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
       return EvalPrediction(predictions=SCREAMING_SNAKE_CASE__ ,     label_ids=SCREAMING_SNAKE_CASE__    )


lowercase  : Optional[int]						   =							load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

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







  def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
         return trt.volume(engine.get_binding_shape(SCREAMING_SNAKE_CASE__    )    ) * engine.get_binding_dtype(SCREAMING_SNAKE_CASE__    ).itemsize

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

  # Allocate output buffer
  lowercase  : Tuple						   =							cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
  lowercase  : Tuple						   =							cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
  lowercase  : str						   =							cuda.mem_alloc(h_outputa.nbytes)
  lowercase  : Any						   =							cuda.mem_alloc(h_outputa.nbytes)

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

  lowercase  : str						   =							0.0
  lowercase  : List[str]						   =							0
  lowercase  : List[str]						   =							timeit.default_timer()

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

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

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

    lowercase  : Dict						   =							(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
    lowercase  : Optional[int]						   =							logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

  if all_preds is not None:
    lowercase  : Optional[int]						   =							nested_truncate(all_preds, len(eval_dataset))

  lowercase  : Optional[int]						   =							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)

lowercase  : Optional[Any]						   =							post_processing_function(eval_examples, eval_dataset, all_preds)
lowercase  : Any						   =							metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




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


from ...processing_utils import ProcessorMixin

class __snake_case		(				lowerCAmelCase					):
    _a		:						Union[str, Any]=    "WhisperFeatureExtractor"
    _a		:						int=    "WhisperTokenizer"
    def __init__(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           super().__init__(snake_case		,snake_case				)
           lowercase							:					Optional[int]	     =				self.feature_extractor
           lowercase							:					Tuple	     =				False
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=None		,snake_case=None		,snake_case=True				):



           '''simple docstring'''







           return self.tokenizer.get_decoder_prompt_ids(task=snake_case		,language=snake_case		,no_timestamps=snake_case				)
    def __call__(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           if self._in_target_context_manager:
                  return self.current_processor(*snake_case		,**snake_case				)

           lowercase							:					Optional[Any]	     =				kwargs.pop("""audio"""		,snake_case				)
           lowercase							:					str	     =				kwargs.pop("""sampling_rate"""		,snake_case				)
           lowercase							:					Dict	     =				kwargs.pop("""text"""		,snake_case				)
           if len(snake_case				) > 0:
                  lowercase							:					List[Any]	     =				args[0]
                  lowercase							:					Tuple	     =				args[1:]

           if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process."""				)

           if audio is not None:
                  lowercase							:					Any	     =				self.feature_extractor(snake_case		,*snake_case		,sampling_rate=snake_case		,**snake_case				)
           if text is not None:
                  lowercase							:					str	     =				self.tokenizer(snake_case		,**snake_case				)

           if text is None:
                  return inputs

           elif audio is None:
                  return encodings
           else:
                  lowercase							:					List[Any]	     =				encodings["""input_ids"""]
                  return inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.batch_decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           return self.tokenizer.decode(*snake_case		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case="np"				):



           '''simple docstring'''







           return self.tokenizer.get_prompt_ids(snake_case		,return_tensors=snake_case				)
