
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


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

__magic_name__            =  logging.getLogger(__name__)

__magic_name__            =  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''',
)

__magic_name__            =  parser.parse_args()

if args.tokenizer_name:
     __magic_name__            =  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)

__magic_name__            =  args.per_device_eval_batch_size

__magic_name__            =  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__magic_name__            =  True

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

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

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

     with builder.create_builder_config() as config:
          __magic_name__            =  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)
          __magic_name__            =  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)
          __magic_name__            =  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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				np.asarray(inputs["input_ids"]    ,		dtype=np.intaa							)
    snake_case__						=				np.asarray(inputs["attention_mask"]    ,		dtype=np.intaa							)
    snake_case__						=				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
    snake_case__						=				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
    snake_case__						=				time.time()
    snake_case__						=				end_time - start_time
    snake_case__						=				(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.
__magic_name__            =  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.
     __magic_name__            =  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.

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

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

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

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    # 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
    snake_case__						=				[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.
    snake_case__						=				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.
    snake_case__						=				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.
    snake_case__						=				[]

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

    return tokenized_examples


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

__magic_name__            =  default_data_collator

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





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase="eval"							):
    # Post-processing: we match the start logits and end logits to answers in the original context.
    snake_case__						=				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:
        snake_case__						=				[
            {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
        ]
    else:
        snake_case__						=				[{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


__magic_name__            =  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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         return trt.volume(engine.get_binding_shape(__lowerCAmelCase							)							) * engine.get_binding_dtype(__lowerCAmelCase							).itemsize

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

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

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

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

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

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

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

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

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

     __magic_name__            =  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)

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





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





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



