


'''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


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

__snake_case							   =  logging.getLogger(__name__)

__snake_case							   =  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''',
)

__snake_case							   =  parser.parse_args()

if args.tokenizer_name:
		__snake_case							   =  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)

__snake_case							   =  args.per_device_eval_batch_size

__snake_case							   =  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__snake_case							   =  True

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

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

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

		with builder.create_builder_config() as config:
				__snake_case							   =  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)
				__snake_case							   =  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)
				__snake_case							   =  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   (    __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :Dict							      =				np.asarray(inputs['''input_ids''']				,      dtype=np.intaa     )
					UpperCamelCase__     :Any							      =				np.asarray(inputs['''attention_mask''']				,      dtype=np.intaa     )
					UpperCamelCase__     :Dict							      =				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
					UpperCamelCase__     :List[str]							      =				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
					UpperCamelCase__     :Any							      =				time.time()
					UpperCamelCase__     :Any							      =				end_time - start_time
					UpperCamelCase__     :Tuple							      =				(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.
__snake_case							   =  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.
		__snake_case							   =  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.

__snake_case							   =  raw_datasets['''validation'''].column_names

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

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

__snake_case							   =  min(args.max_seq_length, tokenizer.model_max_length)


def     a   (    __a     )						->			Optional[Any]:



					'''simple docstring'''






					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__     :Tuple							      =				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.
					UpperCamelCase__     :List[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__     :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).
										UpperCamelCase__     :Any							      =				tokenized_examples.sequence_ids(__a     )
										UpperCamelCase__     :Dict							      =				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__     :List[str]							      =				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__     :List[str]							      =				[
										    (o if sequence_ids[k] == context_index else None)
										    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]     )
										]

					return tokenized_examples


__snake_case							   =  raw_datasets['''validation''']
# Validation Feature Creation
__snake_case							   =  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''',
)

__snake_case							   =  default_data_collator

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


def     a   (    __a				,      __a				,      __a				,      __a="eval"     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				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:
										UpperCamelCase__     :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__     :Tuple							      =				[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
					return EvalPrediction(predictions=__a				,      label_ids=__a     )


__snake_case							   =  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   (    __a     )						->			Any:



							'''simple docstring'''






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

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

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

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

		__snake_case							   =  0.0
		__snake_case							   =  0
		__snake_case							   =  timeit.default_timer()

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

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

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

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

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

		__snake_case							   =  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)

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







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




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps