






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

A_       :Dict       =						logging.getLogger(__name__)

A_       :List[str]       =						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_       :str       =						parser.parse_args()

if args.tokenizer_name:
							A_       :Optional[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)

A_       :Tuple       =						args.per_device_eval_batch_size

A_       :List[Any]       =						(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A_       :Union[str, Any]       =						True

A_       :Union[str, Any]       =						'''temp_engine/bert-fp32.engine'''
if args.fpaa:
							A_       :int       =						'''temp_engine/bert-fp16.engine'''
if args.inta:
							A_       :List[str]       =						'''temp_engine/bert-int8.engine'''

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

A_       :Union[str, Any]       =						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_       :Tuple       =						[network.get_input(i) for i in range(network.num_inputs)]
							A_       :Dict       =						[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

							with builder.create_builder_config() as config:
														A_       :Dict       =						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_       :Optional[int]       =						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_       :int       =						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_					)     ->						List[str]:
					__UpperCamelCase      :					int       		=np.asarray(inputs['input_ids']							,dtype=np.intaa					)
					__UpperCamelCase      :					List[Any]       		=np.asarray(inputs['attention_mask']							,dtype=np.intaa					)
					__UpperCamelCase      :					Optional[int]       		=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      :					Any       		=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      :					Tuple       		=time.time()
					__UpperCamelCase      :					Tuple       		=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.
A_       :Optional[int]       =						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_       :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.

A_       :str       =						raw_datasets['''validation'''].column_names

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

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

A_       :int       =						min(args.max_seq_length, tokenizer.model_max_length)






def 							A     (       a_					)     ->						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
					__UpperCamelCase      :					List[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.
					__UpperCamelCase      :					List[str]       		=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      :					Optional[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.
					__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      :					List[str]       		=tokenized_examples.sequence_ids(a_					)
										__UpperCamelCase      :					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.
										__UpperCamelCase      :					Union[str, Any]       		=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


A_       :Union[str, Any]       =						raw_datasets['''validation''']
# Validation Feature Creation
A_       :Union[str, 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''',
)

A_       :List[Any]       =						default_data_collator

A_       :Tuple       =						eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
A_       :Dict       =						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]:
					# Post-processing: we match the start logits and end logits to answers in the original context.
					__UpperCamelCase      :					Optional[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      :					Union[str, Any]       		=[
										    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
										]
					else:
										__UpperCamelCase      :					Union[str, Any]       		=[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


A_       :Any       =						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_					)     ->						List[str]:
												return trt.volume(engine.get_binding_shape(a_					)					) * engine.get_binding_dtype(a_					).itemsize

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

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

							# Create a stream in which to copy inputs/outputs and run inference.
							A_       :Optional[int]       =						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_       :List[str]       =						0.0
							A_       :Dict       =						0
							A_       :Any       =						timeit.default_timer()

							A_       :Union[str, Any]       =						None
							for step, batch in enumerate(eval_dataloader):
														A_  ,A_       :Dict       =						model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
														total_time += infer_time
														niter += 1

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

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

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

							if all_preds is not None:
														A_       :Optional[Any]       =						nested_truncate(all_preds, len(eval_dataset))

							A_       :int       =						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_       :Tuple       =						post_processing_function(eval_examples, eval_dataset, all_preds)
A_       :Optional[int]       =						metric.compute(predictions=prediction.predictions, references=prediction.label_ids)


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




'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase :      Dict					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class 							A__    (       A__  ):
  A__		 =				'deta'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=None							,					_a      :	int=900							,					_a      :	Optional[Any]=2048							,					_a      :	int=6							,					_a      :	Tuple=2048							,					_a      :	Optional[int]=8							,					_a      :	Any=6							,					_a      :	str=1024							,					_a      :	int=8							,					_a      :	int=0.0							,					_a      :	Optional[Any]=True							,					_a      :	Tuple="relu"							,					_a      :	Union[str, Any]=256							,					_a      :	Tuple=0.1							,					_a      :	str=0.0							,					_a      :	Dict=0.0							,					_a      :	Tuple=0.02							,					_a      :	Union[str, Any]=1.0							,					_a      :	Any=True							,					_a      :	Tuple=False							,					_a      :	List[Any]="sine"							,					_a      :	str=5							,					_a      :	List[Any]=4							,					_a      :	str=4							,					_a      :	Union[str, Any]=True							,					_a      :	Optional[int]=300							,					_a      :	Dict=True							,					_a      :	List[Any]=True							,					_a      :	List[Any]=1							,					_a      :	List[str]=5							,					_a      :	int=2							,					_a      :	Dict=1							,					_a      :	str=1							,					_a      :	Optional[Any]=5							,					_a      :	Union[str, Any]=2							,					_a      :	List[str]=0.1							,					_a      :	List[Any]=0.25							,					**_a      :	Union[str, Any]							,					)	->    List[str]:





       '''simple docstring'''






       if backbone_config is None:
            logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
            _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
       else:
            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =backbone_config.pop('model_type' )
                 _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING[backbone_model_type]
                 _SCREAMING_SNAKE_CASE							    =config_class.from_dict(_a )

       _SCREAMING_SNAKE_CASE							    =backbone_config
       _SCREAMING_SNAKE_CASE							    =num_queries
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =d_model
       _SCREAMING_SNAKE_CASE							    =encoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =encoder_layers
       _SCREAMING_SNAKE_CASE							    =encoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =decoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =decoder_layers
       _SCREAMING_SNAKE_CASE							    =decoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std
       _SCREAMING_SNAKE_CASE							    =init_xavier_std
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =auxiliary_loss
       _SCREAMING_SNAKE_CASE							    =position_embedding_type
       # deformable attributes
       _SCREAMING_SNAKE_CASE							    =num_feature_levels
       _SCREAMING_SNAKE_CASE							    =encoder_n_points
       _SCREAMING_SNAKE_CASE							    =decoder_n_points
       _SCREAMING_SNAKE_CASE							    =two_stage
       _SCREAMING_SNAKE_CASE							    =two_stage_num_proposals
       _SCREAMING_SNAKE_CASE							    =with_box_refine
       _SCREAMING_SNAKE_CASE							    =assign_first_stage
       if two_stage is True and with_box_refine is False:
            raise ValueError('If two_stage is True, with_box_refine must be True.' )
       # Hungarian matcher
       _SCREAMING_SNAKE_CASE							    =class_cost
       _SCREAMING_SNAKE_CASE							    =bbox_cost
       _SCREAMING_SNAKE_CASE							    =giou_cost
       # Loss coefficients
       _SCREAMING_SNAKE_CASE							    =mask_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =dice_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =bbox_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =giou_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =eos_coefficient
       _SCREAMING_SNAKE_CASE							    =focal_alpha
       super().__init__(is_encoder_decoder=_a							,					**_a )





  @property
  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       return self.encoder_attention_heads





  @property
  def 						A  (		self      :	List[Any] )	->    int:





       '''simple docstring'''






       return self.d_model





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =copy.deepcopy(self.__dict__ )
       _SCREAMING_SNAKE_CASE							    =self.backbone_config.to_dict()
       _SCREAMING_SNAKE_CASE							    =self.__class__.model_type
       return output
