



'''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


a   :			Any					  =    trt.Logger(trt.Logger.WARNING)
a   :			List[str]					  =    absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

a   :			List[Any]					  =    logging.getLogger(__name__)

a   :			List[Any]					  =    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""",
)

a   :			int					  =    parser.parse_args()

if args.tokenizer_name:
							a   :			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)

a   :			str					  =    args.per_device_eval_batch_size

a   :			int					  =    (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a   :			str					  =    True

a   :			Optional[int]					  =    """temp_engine/bert-fp32.engine"""
if args.fpaa:
							a   :			Any					  =    """temp_engine/bert-fp16.engine"""
if args.inta:
							a   :			int					  =    """temp_engine/bert-int8.engine"""

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

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

							with builder.create_builder_config() as config:
														a   :			List[Any]					  =    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)
														a   :			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)
														a   :			Optional[int]					  =    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       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		List[Any]							     =		np.asarray(inputs["""input_ids"""]	,				dtype=np.intaa		)
							UpperCAmelCase     :		List[Any]							     =		np.asarray(inputs["""attention_mask"""]	,				dtype=np.intaa		)
							UpperCAmelCase     :		int							     =		np.asarray(inputs["""token_type_ids"""]	,				dtype=np.intaa		)

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

a   :			Dict					  =    raw_datasets["""validation"""].column_names

a   :			List[Any]					  =    """question""" if """question""" in column_names else column_names[0]
a   :			Dict					  =    """context""" if """context""" in column_names else column_names[1]
a   :			Tuple					  =    """answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
a   :			Optional[Any]					  =    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}.'''
							)

a   :			Tuple					  =    min(args.max_seq_length, tokenizer.model_max_length)



def       __lowerCamelCase  (					_lowercase		)      ->			List[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
							UpperCAmelCase     :		Dict							     =		[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     :		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=_lowercase	,				stride=args.doc_stride	,				return_overflowing_tokens=_lowercase	,				return_offsets_mapping=_lowercase	,				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     :		str							     =		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     :		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).
														UpperCAmelCase     :		Optional[int]							     =		tokenized_examples.sequence_ids(_lowercase		)
														UpperCAmelCase     :		Any							     =		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     :		Dict							     =		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     :		Dict							     =		[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]		)
														]

							return tokenized_examples


a   :			List[str]					  =    raw_datasets["""validation"""]
# Validation Feature Creation
a   :			Any					  =    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""",
)

a   :			Dict					  =    default_data_collator

a   :			Dict					  =    eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
a   :			Optional[int]					  =    DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase="eval"		)      ->			Tuple:
							# Post-processing: we match the start logits and end logits to answers in the original context.
							UpperCAmelCase     :		Dict							     =		postprocess_qa_predictions(
							    examples=_lowercase	,				features=_lowercase	,				predictions=_lowercase	,				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=_lowercase	,				)
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														UpperCAmelCase     :		Union[str, Any]							     =		[
														    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
														]
							else:
														UpperCAmelCase     :		List[Any]							     =		[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


a   :			Dict					  =    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       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
														return trt.volume(engine.get_binding_shape(_lowercase		)		) * engine.get_binding_dtype(_lowercase		).itemsize

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

							# Allocate output buffer
							a   :			int					  =    cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
							a   :			Optional[Any]					  =    cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
							a   :			Optional[Any]					  =    cuda.mem_alloc(h_outputa.nbytes)
							a   :			int					  =    cuda.mem_alloc(h_outputa.nbytes)

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

							a   :			Tuple					  =    0.0
							a   :			Any					  =    0
							a   :			Any					  =    timeit.default_timer()

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

														a  ,      a   :			Any					  =    outputs
														a   :			Union[str, Any]					  =    torch.tensor(start_logits)
														a   :			int					  =    torch.tensor(end_logits)

														# necessary to pad predictions and labels for being gathered
														a   :			List[str]					  =    accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
														a   :			Union[str, Any]					  =    accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

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

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

							a   :			Optional[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)

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


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






'''simple docstring'''




from itertools import count



def       __lowerCamelCase  (					_lowercase = 5_0		)      ->			int:
							UpperCAmelCase     :		Any							     =		[1] * min_block_length

							for n in count(_lowercase		):
														fill_count_functions.append(1		)

														for block_length in range(_lowercase	,				n + 1		):
																					for block_start in range(n - block_length		):
																												fill_count_functions[n] += fill_count_functions[
																												    n - block_start - block_length - 1
																												]

																					fill_count_functions[n] += 1

														if fill_count_functions[n] > 1_0_0_0_0_0_0:
																					break

							return n


if __name__ == "__main__":
							print(F'''{solution() = }''')

