






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

A        =			logging.getLogger(__name__)

A        =			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',
)

A        =			parser.parse_args()

if args.tokenizer_name:
				A        =			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        =			args.per_device_eval_batch_size

A        =			(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A        =			True

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

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

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

				with builder.create_builder_config() as config:
								A        =			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)
								A        =			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        =			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(			lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		Tuple    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   np.asarray(inputs['input_ids']    ,    dtype=np.intaa      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   np.asarray(inputs['attention_mask']    ,    dtype=np.intaa      )
				SCREAMING_SNAKE_CASE_   :	Union[str, 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
				SCREAMING_SNAKE_CASE_   :	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
				SCREAMING_SNAKE_CASE_   :	Any													=   time.time()
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   end_time - start_time
				SCREAMING_SNAKE_CASE_   :	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        =			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        =			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        =			raw_datasets['validation'].column_names

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

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   [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_   :	Optional[int]													=   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_   :	Union[str, 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.
				SCREAMING_SNAKE_CASE_   :	Dict													=   []

				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_   :	Optional[Any]													=   tokenized_examples.sequence_ids(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Optional[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.
								SCREAMING_SNAKE_CASE_   :	Optional[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.
								SCREAMING_SNAKE_CASE_   :	Tuple													=   [
								    (o if sequence_ids[k] == context_index else None)
								    for k, o in enumerate(tokenized_examples['offset_mapping'][i]      )
								]

				return tokenized_examples


A        =			raw_datasets['validation']
# Validation Feature Creation
A        =			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        =			default_data_collator

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Dict="eval"      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	List[str]													=   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_   :	Optional[Any]													=   [
								    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
								]
				else:
								SCREAMING_SNAKE_CASE_   :	str													=   [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


A        =			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(			lowerCAmelCase__				:		Any      ):





								'''simple docstring'''







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

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

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

				# Create a stream in which to copy inputs/outputs and run inference.
				A        =			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        =			0.0
				A        =			0
				A        =			timeit.default_timer()

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

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

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

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

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

				A        =			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)

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


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







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()