

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

lowerCAmelCase_       	=	logging.getLogger(__name__)

lowerCAmelCase_       	=	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''',
)

lowerCAmelCase_       	=	parser.parse_args()

if args.tokenizer_name:
					lowerCAmelCase_       	=	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_       	=	args.per_device_eval_batch_size

lowerCAmelCase_       	=	(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCAmelCase_       	=	True

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

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

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

					with builder.create_builder_config() as config:
										lowerCAmelCase_       	=	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_       	=	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_       	=	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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	np.asarray(inputs['''input_ids''']  ,		dtype=np.intaa       )
						snake_case_   =	np.asarray(inputs['''attention_mask''']  ,		dtype=np.intaa       )
						snake_case_   =	np.asarray(inputs['''token_type_ids''']  ,		dtype=np.intaa       )

						# Copy inputs
						cuda.memcpy_htod_async(d_inputs[0]  ,		input_ids.ravel()  ,		SCREAMING_SNAKE_CASE__       )
						cuda.memcpy_htod_async(d_inputs[1]  ,		attention_mask.ravel()  ,		SCREAMING_SNAKE_CASE__       )
						cuda.memcpy_htod_async(d_inputs[2]  ,		token_type_ids.ravel()  ,		SCREAMING_SNAKE_CASE__       )
						# start time
						snake_case_   =	time.time()
						# Run inference
						context.execute_async(
						    bindings=[int(SCREAMING_SNAKE_CASE__       ) for d_inp in d_inputs] + [int(SCREAMING_SNAKE_CASE__       ), int(SCREAMING_SNAKE_CASE__       )]  ,		stream_handle=stream.handle       )
						# Transfer predictions back from GPU
						cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						# Synchronize the stream and take time
						stream.synchronize()
						# end time
						snake_case_   =	time.time()
						snake_case_   =	end_time - start_time
						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.
lowerCAmelCase_       	=	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_       	=	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_       	=	raw_datasets['''validation'''].column_names

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

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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						# 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
						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.
						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=SCREAMING_SNAKE_CASE__  ,		stride=args.doc_stride  ,		return_overflowing_tokens=SCREAMING_SNAKE_CASE__  ,		return_offsets_mapping=SCREAMING_SNAKE_CASE__  ,		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.
						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.
						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).
												snake_case_   =	tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE__       )
												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.
												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.
												snake_case_   =	[
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]       )
												]

						return tokenized_examples


lowerCAmelCase_       	=	raw_datasets['''validation''']
# Validation Feature Creation
lowerCAmelCase_       	=	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_       	=	default_data_collator

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


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

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


lowerCAmelCase_       	=	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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
											return trt.volume(engine.get_binding_shape(SCREAMING_SNAKE_CASE__       )       ) * engine.get_binding_dtype(SCREAMING_SNAKE_CASE__       ).itemsize

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

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

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

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

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

										# necessary to pad predictions and labels for being gathered
										lowerCAmelCase_       	=	accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
										lowerCAmelCase_       	=	accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

										lowerCAmelCase_       	=	(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
										lowerCAmelCase_       	=	logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

					lowerCAmelCase_       	=	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)

lowerCAmelCase_       	=	post_processing_function(eval_examples, eval_dataset, all_preds)
lowerCAmelCase_       	=	metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)