


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


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

_lowerCAmelCase:						int   				=       logging.getLogger(__name__)

_lowerCAmelCase:						Tuple   				=       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',
)

_lowerCAmelCase:						Any   				=       parser.parse_args()

if args.tokenizer_name:
	_lowerCAmelCase:						Any   				=       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)

_lowerCAmelCase:						Dict   				=       args.per_device_eval_batch_size

_lowerCAmelCase:						str   				=       (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCAmelCase:						Optional[int]   				=       True

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

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

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

	with builder.create_builder_config() as config:
		_lowerCAmelCase:						Union[str, Any]   				=       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)
		_lowerCAmelCase:						Union[str, 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)
		_lowerCAmelCase:						str   				=       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   _lowercase(					__a				:						Tuple    ,     __a				:						Optional[int]    ,     __a				:						Dict    ,     __a				:						Optional[Any]    ,     __a				:						Tuple    ,     __a				:						Union[str, Any]    ,     __a				:						int    ,     __a				:						Any     ):
							a__      =np.asarray(inputs['input_ids']    ,     dtype=np.intaa     )
							a__      =np.asarray(inputs['attention_mask']    ,     dtype=np.intaa     )
							a__      =np.asarray(inputs['token_type_ids']    ,     dtype=np.intaa     )

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

_lowerCAmelCase:						Tuple   				=       raw_datasets['validation'].column_names

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

# Padding side determines if we do (question|context) or (context|question).
_lowerCAmelCase:						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}."""
	)

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






def   _lowercase(					__a				:						List[str]     ):
							# 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__      =[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__      =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=__a    ,     stride=args.doc_stride    ,     return_overflowing_tokens=__a    ,     return_offsets_mapping=__a    ,     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__      =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__      =[]

							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__      =tokenized_examples.sequence_ids(__a     )
														a__      =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__      =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__      =[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples['offset_mapping'][i]     )
														]

							return tokenized_examples


_lowerCAmelCase:						List[Any]   				=       raw_datasets['validation']
# Validation Feature Creation
_lowerCAmelCase:						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',
)

_lowerCAmelCase:						List[str]   				=       default_data_collator

_lowerCAmelCase:						Dict   				=       eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_lowerCAmelCase:						Tuple   				=       DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)






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

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


_lowerCAmelCase:						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   _lowercase(					__a				:						Tuple     ):
								return trt.volume(engine.get_binding_shape(__a     )     ) * engine.get_binding_dtype(__a     ).itemsize

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

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

	# Create a stream in which to copy inputs/outputs and run inference.
	_lowerCAmelCase:						str   				=       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}""")

	_lowerCAmelCase:						Any   				=       0.0
	_lowerCAmelCase:						str   				=       0
	_lowerCAmelCase:						Dict   				=       timeit.default_timer()

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

		_lowerCAmelCase				,						_lowerCAmelCase:						str   				=       outputs
		_lowerCAmelCase:						List[Any]   				=       torch.tensor(start_logits)
		_lowerCAmelCase:						str   				=       torch.tensor(end_logits)

		# necessary to pad predictions and labels for being gathered
		_lowerCAmelCase:						Dict   				=       accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
		_lowerCAmelCase:						List[Any]   				=       accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

		_lowerCAmelCase:						Optional[int]   				=       (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
		_lowerCAmelCase:						List[Any]   				=       logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

	if all_preds is not None:
		_lowerCAmelCase:						Union[str, Any]   				=       nested_truncate(all_preds, len(eval_dataset))

	_lowerCAmelCase:						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_000 / niter))
	logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_000))







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

_lowerCAmelCase:						Tuple   				=       post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCAmelCase:						Union[str, Any]   				=       metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







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





from __future__ import annotations

from typing import Any




class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> None:
											a__      =num_of_nodes
											a__      =[]
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:
											self.m_edges.append([u_node, v_node, weight])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:

											if self.m_component[u_node] == u_node:
																		return u_node
											return self.find_component(self.m_component[u_node])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:

											if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																									a__      =self.find_component(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:

											if component_size[u_node] <= component_size[v_node]:
																		a__      =v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(lowercase_)

											elif component_size[u_node] >= component_size[v_node]:
																		a__      =self.find_component(lowercase_)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(lowercase_)





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =[]
											a__      =0

											a__      =[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes):
																		self.m_component.update({node: node})
																		component_size.append(1)

											a__      =self.m_num_of_nodes

											while num_of_components > 1:
																		for edge in self.m_edges:
																									a__    ,     a__    ,     a__      =edge

																									a__      =self.m_component[u]
																									a__      =self.m_component[v]

																									if u_component != v_component:

																																for component in (u_component, v_component):
																																							if (
																																							    minimum_weight_edge[component] == -1
																																							    or minimum_weight_edge[component][2] > w
																																							):
																																														a__      =[u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(lowercase_       ,  lowercase_):
																																a__    ,     a__    ,     a__      =edge

																																a__      =self.m_component[u]
																																a__      =self.m_component[v]

																																if u_component != v_component:
																																							mst_weight += w
																																							self.union(lowercase_       ,  lowercase_       ,  lowercase_)
																																							print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
																																							num_of_components -= 1

																		a__      =[-1] * self.m_num_of_nodes
											print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()

