


'''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


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

__snake_case       		=logging.getLogger(__name__)

__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""",
)

__snake_case       		=parser.parse_args()

if args.tokenizer_name:
       __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)

__snake_case       		=args.per_device_eval_batch_size

__snake_case       		=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__snake_case       		=True

__snake_case       		='''temp_engine/bert-fp32.engine'''
if args.fpaa:
       __snake_case       		='''temp_engine/bert-fp16.engine'''
if args.inta:
       __snake_case       		='''temp_engine/bert-int8.engine'''

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

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

       with builder.create_builder_config() as config:
              __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)
              __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)
              __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       a_   (					lowerCamelCase      :	str      ,					lowerCamelCase      :	List[str]      ,					lowerCamelCase      :	Any      ,					lowerCamelCase      :	Union[str, Any]      ,					lowerCamelCase      :	Optional[Any]      ,					lowerCamelCase      :	Tuple      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	Optional[int]							):
   lowerCAmelCase	=     np.asarray(inputs['input_ids']      ,					dtype=np.intaa							)
   lowerCAmelCase	=     np.asarray(inputs['attention_mask']      ,					dtype=np.intaa							)
   lowerCAmelCase	=     np.asarray(inputs['token_type_ids']      ,					dtype=np.intaa							)

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

__snake_case       		=raw_datasets['''validation'''].column_names

__snake_case       		='''question''' if '''question''' in column_names else column_names[0]
__snake_case       		='''context''' if '''context''' in column_names else column_names[1]
__snake_case       		='''answers''' if '''answers''' in column_names else column_names[2]

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

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





def       a_   (					lowerCamelCase      :	int							):
   lowerCAmelCase	=     [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.
   lowerCAmelCase	=     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=_UpperCamelCase      ,					stride=args.doc_stride      ,					return_overflowing_tokens=_UpperCamelCase      ,					return_offsets_mapping=_UpperCamelCase      ,					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.
   lowerCAmelCase	=     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.
   lowerCAmelCase	=     []

   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).
      lowerCAmelCase	=     tokenized_examples.sequence_ids(_UpperCamelCase							)
      lowerCAmelCase	=     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.
      lowerCAmelCase	=     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.
      lowerCAmelCase	=     [
          (o if sequence_ids[k] == context_index else None)
          for k, o in enumerate(tokenized_examples['offset_mapping'][i]							)
      ]

   return tokenized_examples


__snake_case       		=raw_datasets['''validation''']
# Validation Feature Creation
__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""",
)

__snake_case       		=default_data_collator

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





def       a_   (					lowerCamelCase      :	Any      ,					lowerCamelCase      :	List[Any]      ,					lowerCamelCase      :	List[str]      ,					lowerCamelCase      :	Optional[Any]="eval"							):
   lowerCAmelCase	=     postprocess_qa_predictions(
       examples=_UpperCamelCase      ,					features=_UpperCamelCase      ,					predictions=_UpperCamelCase      ,					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=_UpperCamelCase      ,					)
   # Format the result to the format the metric expects.
   if args.version_2_with_negative:
      lowerCAmelCase	=     [
          {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
      ]
   else:
      lowerCAmelCase	=     [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


__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       a_   (					lowerCamelCase      :	Union[str, Any]							):
          return trt.volume(engine.get_binding_shape(_UpperCamelCase							)							) * engine.get_binding_dtype(_UpperCamelCase							).itemsize

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

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

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

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

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

              __snake_case       		=outputs
              __snake_case       		=torch.tensor(start_logits)
              __snake_case       		=torch.tensor(end_logits)

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

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

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

       __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 * 1_000 / niter))
       logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1_000))




       logger.info("""Total Number of Inference =  %d""", niter)

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




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













import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__magic_name__   :  Optional[int]        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  r'\w+[.]\d+'
      UpperCamelCase					       =  re.findall(_UpperCamelCase       ,							_UpperCamelCase)
      for pat in pats:
            UpperCamelCase					       =  key.replace(_UpperCamelCase       ,							'_'.join(pat.split('.')))
      return key
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
      if (
          any('norm' in str_ for str_ in pt_tuple_key)
          and (pt_tuple_key[-1] == "bias")
          and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
          and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
      ):
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor
      elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('embedding',)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
            UpperCamelCase					       =  pt_tensor.transpose(2       ,							3       ,							1       ,							0)
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight":
            UpperCamelCase					       =  pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('weight',)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('bias',)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=42) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  {k: v.numpy() for k, v in pt_state_dict.items()}

      # Step 2: Since the model is stateless, get random Flax params
      UpperCamelCase					       =  flax_model.init_weights(PRNGKey(_UpperCamelCase))

      UpperCamelCase					       =  flatten_dict(_UpperCamelCase)
      UpperCamelCase					       =  {}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            UpperCamelCase					       =  rename_key(_UpperCamelCase)
            UpperCamelCase					       =  tuple(renamed_pt_key.split('.'))

            # Correctly rename weight parameters
            UpperCamelCase       ,  UpperCamelCase					       =  rename_key_and_reshape_tensor(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                            F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.')

        # also add unexpected weight so that warning is thrown
            UpperCamelCase					       =  jnp.asarray(_UpperCamelCase)

      return unflatten_dict(_UpperCamelCase)



