





"""simple docstring"""

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


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

__UpperCAmelCase        =logging.getLogger(__name__)

__UpperCAmelCase        =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""",
)

__UpperCAmelCase        =parser.parse_args()

if args.tokenizer_name:
 __UpperCAmelCase        =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)

__UpperCAmelCase        =args.per_device_eval_batch_size

__UpperCAmelCase        =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__UpperCAmelCase        =True

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

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

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

 with builder.create_builder_config() as config:
  __UpperCAmelCase        =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)
  __UpperCAmelCase        =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)
  __UpperCAmelCase        =builder.build_engine(network, config)

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



def        __a							(			A ,	A ,	A ,	A ,	A ,	A ,	A ,	A )       ->						int:



   '''simple docstring'''



   A__											=					np.asarray(inputs["input_ids"] ,	dtype=np.intaa )
   A__											=					np.asarray(inputs["attention_mask"] ,	dtype=np.intaa )
   A__											=					np.asarray(inputs["token_type_ids"] ,	dtype=np.intaa )

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

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

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

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

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



def        __a							(			A )       ->						str:



   '''simple docstring'''



   A__											=					[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.
   A__											=					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=A ,	stride=args.doc_stride ,	return_overflowing_tokens=A ,	return_offsets_mapping=A ,	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.
   A__											=					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.
   A__											=					[]

   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).
      A__											=					tokenized_examples.sequence_ids(A )
      A__											=					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.
      A__											=					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.
      A__											=					[
          (o if sequence_ids[k] == context_index else None)
          for k, o in enumerate(tokenized_examples["offset_mapping"][i] )
      ]

   return tokenized_examples


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

__UpperCAmelCase        =default_data_collator

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



def        __a							(			A ,	A ,	A ,	A="eval" )       ->						Tuple:



   '''simple docstring'''



   A__											=					postprocess_qa_predictions(
       examples=A ,	features=A ,	predictions=A ,	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=A ,	)
   # Format the result to the format the metric expects.
   if args.version_2_with_negative:
      A__											=					[
          {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
      ]
   else:
      A__											=					[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

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


__UpperCAmelCase        =load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

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



 def        __a							(			A )       ->						Optional[int]:



    '''simple docstring'''



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

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

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

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

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

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

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

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

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

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

 __UpperCAmelCase        =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)

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

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


'''simple docstring'''





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_    						=       object()

# For specifying empty leaf dict `{}`
UpperCamelCase_    						=       object()






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     int						)				->  Optional[int]:
						lowercase :							Optional[Any]    		=tuple((re.compile(x + '''$'''						) for x in qs)						)
						for i in range(len(__magic_name__						) - len(__magic_name__						) + 1						):
												lowercase :							Union[str, Any]    		=[x.match(__magic_name__						) for x, y in zip(__magic_name__   , ks[i:]						)]
												if matches and all(__magic_name__						):
																		return True
						return False






def 				_lowerCAmelCase			(   __magic_name__						:     Dict						)				->  List[str]:
						def replace(__magic_name__						:     Optional[int]   , __magic_name__						:     Optional[Any]						):
												for rule, replacement in rules:
																		if _match(__magic_name__   , __magic_name__						):
																								return replacement
												return val

						return replace






def 				_lowerCAmelCase			(   )				->  int:
						return [
						    # embeddings
						    (("transformer", "wpe", "embedding"), P('''mp'''   , __magic_name__						)),
						    (("transformer", "wte", "embedding"), P('''mp'''   , __magic_name__						)),
						    # atention
						    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("attention", "out_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("attention", "out_proj", "bias"), None),
						    # mlp
						    (("mlp", "c_fc", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("mlp", "c_fc", "bias"), P('''mp'''						)),
						    (("mlp", "c_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("mlp", "c_proj", "bias"), None),
						    # layer norms
						    ((r"ln_\d+", "bias"), None),
						    ((r"\d+", r"ln_\d+", "scale"), None),
						    (("ln_f", "bias"), None),
						    (("ln_f", "scale"), None),
						]







def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  int:
						lowercase :							int    		=_get_partition_rules()
						lowercase :							Tuple    		=_replacement_rules(__magic_name__						)
						lowercase :							Any    		={k: _unmatched for k in flatten_dict(__magic_name__						)}
						lowercase :							Any    		={k: replace(__magic_name__   , __magic_name__						) for k, v in initd.items()}
						assert _unmatched not in result.values(), "Incomplete partition spec."
						return freeze(unflatten_dict(__magic_name__						)						)





