
"""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


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

SCREAMING_SNAKE_CASE_    					=						logging.getLogger(__name__)

SCREAMING_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''',
)

SCREAMING_SNAKE_CASE_    					=						parser.parse_args()

if args.tokenizer_name:
   SCREAMING_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)

SCREAMING_SNAKE_CASE_    					=						args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE_    					=						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE_    					=						True

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

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

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

   with builder.create_builder_config() as config:
      SCREAMING_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)
      SCREAMING_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)
      SCREAMING_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     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					Any ,				lowerCAmelCase				:					int ) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					np.asarray(inputs['input_ids'] ,				dtype=np.intaa )
    UpperCAmelCase									=					np.asarray(inputs['attention_mask'] ,				dtype=np.intaa )
    UpperCAmelCase									=					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									=					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									=					time.time()
    UpperCAmelCase									=					end_time - start_time
    UpperCAmelCase									=					(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.
SCREAMING_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.
   SCREAMING_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.

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

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

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

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




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ) ->      int:






    """simple docstring"""




    UpperCAmelCase									=					[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									=					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									=					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									=					[]

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

    return tokenized_examples


SCREAMING_SNAKE_CASE_    					=						raw_datasets['''validation''']
# Validation Feature Creation
SCREAMING_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''',
)

SCREAMING_SNAKE_CASE_    					=						default_data_collator

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




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Tuple ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					int="eval" ) ->      str:






    """simple docstring"""




    UpperCAmelCase									=					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									=					[
            {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
        ]
    else:
        UpperCAmelCase									=					[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


SCREAMING_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     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      Any:






       """simple docstring"""




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

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

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

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

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

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

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

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

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

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

   SCREAMING_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 * 1000 / niter))
   logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))







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

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







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





"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[str, Any]							      =	"encoder-decoder"
      __SCREAMING_SNAKE_CASE   :							Any							      =	True




      def __init__(							self   ,       **lowercase_							)						->					str:
          super().__init__(**lowercase_							)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          UpperCAmelCase									=					kwargs.pop('encoder'							)
          UpperCAmelCase									=					encoder_config.pop('model_type'							)
          UpperCAmelCase									=					kwargs.pop('decoder'							)
          UpperCAmelCase									=					decoder_config.pop('model_type'							)

          from ..auto.configuration_auto import AutoConfig

          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					AutoConfig.for_model(lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					True




      @classmethod
      def     a_      (							cls   ,       lowercase_   ,       lowercase_   ,       **lowercase_							)						->					PretrainedConfig:
          logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
          UpperCAmelCase									=					True
          UpperCAmelCase									=					True

          return cls(encoder=encoder_config.to_dict()   ,       decoder=decoder_config.to_dict()   ,       **lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					copy.deepcopy(self.__dict__							)
          UpperCAmelCase									=					self.encoder.to_dict()
          UpperCAmelCase									=					self.decoder.to_dict()
          UpperCAmelCase									=					self.__class__.model_type
          return output



