




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:     Tuple									= trt.Logger(trt.Logger.WARNING)
SCREAMING_SNAKE_CASE:     Optional[Any]									= absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

SCREAMING_SNAKE_CASE:     Optional[Any]									= logging.getLogger(__name__)

SCREAMING_SNAKE_CASE:     List[str]									= 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=3_8_4,
    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=1_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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:     Optional[Any]									= parser.parse_args()

if args.tokenizer_name:
						SCREAMING_SNAKE_CASE:     Dict									= 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:     Optional[Any]									= args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE:     Optional[int]									= (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE:     List[str]									= True

SCREAMING_SNAKE_CASE:     Any									= 'temp_engine/bert-fp32.engine'
if args.fpaa:
						SCREAMING_SNAKE_CASE:     Dict									= 'temp_engine/bert-fp16.engine'
if args.inta:
						SCREAMING_SNAKE_CASE:     str									= 'temp_engine/bert-int8.engine'

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

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

						with builder.create_builder_config() as config:
												SCREAMING_SNAKE_CASE:     int									= 1 << 5_0
												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:     List[Any]									= 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:     Any									= 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    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							np.asarray(inputs['input_ids']    ,						dtype=np.intaa )
		SCREAMING_SNAKE_CASE_					      =							np.asarray(inputs['attention_mask']    ,						dtype=np.intaa )
		SCREAMING_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
		SCREAMING_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
		SCREAMING_SNAKE_CASE_					      =							time.time()
		SCREAMING_SNAKE_CASE_					      =							end_time - start_time
		SCREAMING_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.
SCREAMING_SNAKE_CASE:     Dict									= 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:     List[Any]									= 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:     Optional[Any]									= raw_datasets['validation'].column_names

SCREAMING_SNAKE_CASE:     Union[str, Any]									= 'question' if 'question' in column_names else column_names[0]
SCREAMING_SNAKE_CASE:     List[Any]									= 'context' if 'context' in column_names else column_names[1]
SCREAMING_SNAKE_CASE:     List[str]									= 'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE:     str									= 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:     Tuple									= min(args.max_seq_length, tokenizer.model_max_length)


def 					_a							(					lowerCAmelCase )->	Union[str, Any]:
		# 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
		SCREAMING_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.
		SCREAMING_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.
		SCREAMING_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.
		SCREAMING_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).
				SCREAMING_SNAKE_CASE_					      =							tokenized_examples.sequence_ids(lowerCAmelCase )
				SCREAMING_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.
				SCREAMING_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.
				SCREAMING_SNAKE_CASE_					      =							[
				    (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:     List[str]									= raw_datasets['validation']
# Validation Feature Creation
SCREAMING_SNAKE_CASE:     List[str]									= 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:     Any									= default_data_collator

SCREAMING_SNAKE_CASE:     Any									= eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
SCREAMING_SNAKE_CASE:     List[Any]									= DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase="eval" )->	Optional[Any]:
		# Post-processing: we match the start logits and end logits to answers in the original context.
		SCREAMING_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:
				SCREAMING_SNAKE_CASE_					      =							[
				    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
				]
		else:
				SCREAMING_SNAKE_CASE_					      =							[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


SCREAMING_SNAKE_CASE:     List[str]									= 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 )->	Optional[int]:
								return trt.volume(engine.get_binding_shape(lowerCAmelCase ) ) * engine.get_binding_dtype(lowerCAmelCase ).itemsize

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

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

						# Create a stream in which to copy inputs/outputs and run inference.
						SCREAMING_SNAKE_CASE:     Optional[int]									= 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:     Union[str, Any]									= 0.0
						SCREAMING_SNAKE_CASE:     List[str]									= 0
						SCREAMING_SNAKE_CASE:     List[str]									= timeit.default_timer()

						SCREAMING_SNAKE_CASE:     int									= None
						for step, batch in enumerate(eval_dataloader):
												SCREAMING_SNAKE_CASE:     Optional[Any]									= model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
												total_time += infer_time
												niter += 1

												SCREAMING_SNAKE_CASE:     str									= outputs
												SCREAMING_SNAKE_CASE:     int									= torch.tensor(start_logits)
												SCREAMING_SNAKE_CASE:     Tuple									= torch.tensor(end_logits)

												# necessary to pad predictions and labels for being gathered
												SCREAMING_SNAKE_CASE:     Any									= accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
												SCREAMING_SNAKE_CASE:     int									= accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

												SCREAMING_SNAKE_CASE:     Optional[Any]									= (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
												SCREAMING_SNAKE_CASE:     Tuple									= logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

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

						SCREAMING_SNAKE_CASE:     Union[str, Any]									= 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_0_0_0 / niter))
						logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1_0_0_0))




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

SCREAMING_SNAKE_CASE:     int									= post_processing_function(eval_examples, eval_dataset, all_preds)
SCREAMING_SNAKE_CASE:     List[str]									= metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




logger.info(f"""Evaluation metrics: {eval_metric}""")


'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  UpperCAmelCase    				=						(boundary[1] - boundary[0]) / steps
  UpperCAmelCase    				=						boundary[0]
  UpperCAmelCase    				=						boundary[1]
  UpperCAmelCase    				=						make_points(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						0.0
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  for i in x_i:
    # print(i)
    y += h * f(SCREAMING_SNAKE_CASE    )
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  return y


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						0.0  # Lower bound of integration
  UpperCAmelCase    				=						1.0  # Upper bound of integration
  UpperCAmelCase    				=						10.0  # define number of steps or resolution
  UpperCAmelCase    				=						[a, b]  # define boundary of integration
  UpperCAmelCase    				=						method_a(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  print(f'''y = {y}'''    )


if __name__ == "__main__":
  main()


