"""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=3_84,
    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_28,
    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(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			np.asarray(inputs["""input_ids"""]			,  dtype=np.intaa							)
							A__       :     Dict								=			np.asarray(inputs["""attention_mask"""]			,  dtype=np.intaa							)
							A__       :     Any								=			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
							A__       :     List[str]								=			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
							A__       :     int								=			time.time()
							A__       :     Union[str, Any]								=			end_time - start_time
							A__       :     List[str]								=			(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(		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
							A__       :     Tuple								=			[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__       :     Union[str, Any]								=			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.
							A__       :     List[Any]								=			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__       :     List[str]								=			[]

							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__       :     Tuple								=			tokenized_examples.sequence_ids(lowerCAmelCase							)
														A__       :     int								=			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__       :     int								=			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__       :     Optional[int]								=			[
														    (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(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase="eval"							):
							# Post-processing: we match the start logits and end logits to answers in the original context.
							A__       :     Dict								=			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:
														A__       :     Dict								=			[
														    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
														]
							else:
														A__       :     str								=			[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


_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(		lowerCAmelCase							):
											return trt.volume(engine.get_binding_shape(lowerCAmelCase							)							) * engine.get_binding_dtype(lowerCAmelCase							).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=-1_00)
								_UpperCamelCase								=		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

								_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=-1_00)

				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 * 10_00 / niter))
				logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 10_00))



				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"""



from collections.abc import Sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase							)							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			0.0
							for coeff in reversed(lowerCAmelCase							):
														A__       :     Optional[int]								=			result * x + coeff
							return result


if __name__ == "__main__":
				_UpperCamelCase								=		(0.0, 0.0, 5.0, 9.3, 7.0)
				_UpperCamelCase								=		10.0
				print(evaluate_poly(poly, x))
				print(horner(poly, x))





