

'''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


lowercase_      :				Optional[Any]   					=    trt.Logger(trt.Logger.WARNING)
lowercase_      :				int   					=    absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowercase_      :				Union[str, Any]   					=    logging.getLogger(__name__)

lowercase_      :				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=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''',
)

lowercase_      :				str   					=    parser.parse_args()

if args.tokenizer_name:
						lowercase_      :				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)

lowercase_      :				int   					=    args.per_device_eval_batch_size

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

# TRT Engine properties
lowercase_      :				Optional[Any]   					=    True

lowercase_      :				List[str]   					=    '''temp_engine/bert-fp32.engine'''
if args.fpaa:
						lowercase_      :				Tuple   					=    '''temp_engine/bert-fp16.engine'''
if args.inta:
						lowercase_      :				str   					=    '''temp_engine/bert-int8.engine'''

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

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

						with builder.create_builder_config() as config:
												lowercase_      :				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)
												lowercase_      :				str   					=    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)
												lowercase_      :				List[Any]   					=    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				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      List[Any] ,  lowercase_    :      Optional[int] ,  lowercase_    :      Optional[int] ,  lowercase_    :      int ,  lowercase_    :      Any ,  lowercase_    :      str ,  lowercase_    :      Any						):
						lowercase        =							np.asarray(inputs["""input_ids"""] ,  dtype=np.intaa						)
						lowercase        =							np.asarray(inputs["""attention_mask"""] ,  dtype=np.intaa						)
						lowercase        =							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
						lowercase        =							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
						lowercase        =							time.time()
						lowercase        =							end_time - start_time
						lowercase        =							(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.
lowercase_      :				List[Any]   					=    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.
						lowercase_      :				Tuple   					=    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.

lowercase_      :				Optional[int]   					=    raw_datasets['''validation'''].column_names

lowercase_      :				Tuple   					=    '''question''' if '''question''' in column_names else column_names[0]
lowercase_      :				int   					=    '''context''' if '''context''' in column_names else column_names[1]
lowercase_      :				Dict   					=    '''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowercase_      :				Union[str, 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}.'''
						)

lowercase_      :				str   					=    min(args.max_seq_length, tokenizer.model_max_length)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      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
						lowercase        =							[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.
						lowercase        =							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.
						lowercase        =							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.
						lowercase        =							[]

						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).
												lowercase        =							tokenized_examples.sequence_ids(lowercase_						)
												lowercase        =							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.
												lowercase        =							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.
												lowercase        =							[
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]						)
												]

						return tokenized_examples


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

lowercase_      :				List[str]   					=    default_data_collator

lowercase_      :				Any   					=    eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
lowercase_      :				Optional[int]   					=    DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      Optional[Any] ,  lowercase_    :      List[str] ,  lowercase_    :      Union[str, Any]="eval"						):
						# Post-processing: we match the start logits and end logits to answers in the original context.
						lowercase        =							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:
												lowercase        =							[
												    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
												]
						else:
												lowercase        =							[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


lowercase_      :				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      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple						):
												return trt.volume(engine.get_binding_shape(lowercase_						)						) * engine.get_binding_dtype(lowercase_						).itemsize

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

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

						# Create a stream in which to copy inputs/outputs and run inference.
						lowercase_      :				List[Any]   					=    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}''')

						lowercase_      :				List[Any]   					=    0.0
						lowercase_      :				List[str]   					=    0
						lowercase_      :				Optional[Any]   					=    timeit.default_timer()

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

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

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

												lowercase_      :				Union[str, Any]   					=    (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
												lowercase_      :				Any   					=    logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

						lowercase_      :				List[str]   					=    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)

lowercase_      :				Optional[int]   					=    post_processing_function(eval_examples, eval_dataset, all_preds)
lowercase_      :				str   					=    metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'''Evaluation metrics: {eval_metric}''')







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_      :				Any   					=    logging.get_logger(__name__)

lowercase_      :				str   					=    {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''vit_msn'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-06 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    List[Any]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias


