

'''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


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

UpperCamelCase_    						=       logging.getLogger(__name__)

UpperCamelCase_    						=       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""",
)

UpperCamelCase_    						=       parser.parse_args()

if args.tokenizer_name:
							UpperCamelCase_    						=       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)

UpperCamelCase_    						=       args.per_device_eval_batch_size

UpperCamelCase_    						=       (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCamelCase_    						=       True

UpperCamelCase_    						=       """temp_engine/bert-fp32.engine"""
if args.fpaa:
							UpperCamelCase_    						=       """temp_engine/bert-fp16.engine"""
if args.inta:
							UpperCamelCase_    						=       """temp_engine/bert-int8.engine"""

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

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

							with builder.create_builder_config() as config:
														UpperCamelCase_    						=       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)
														UpperCamelCase_    						=       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)
														UpperCamelCase_    						=       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 				_lowerCAmelCase			(   __magic_name__						:     Optional[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     List[str]   , __magic_name__						:     Any   , __magic_name__						:     List[str]   , __magic_name__						:     int   , __magic_name__						:     Any   , __magic_name__						:     int						)				->  int:
						lowercase :							Optional[Any]    		=np.asarray(inputs['''input_ids''']   , dtype=np.intaa						)
						lowercase :							Optional[int]    		=np.asarray(inputs['''attention_mask''']   , dtype=np.intaa						)
						lowercase :							Any    		=np.asarray(inputs['''token_type_ids''']   , dtype=np.intaa						)

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

UpperCamelCase_    						=       raw_datasets["""validation"""].column_names

UpperCamelCase_    						=       """question""" if """question""" in column_names else column_names[0]
UpperCamelCase_    						=       """context""" if """context""" in column_names else column_names[1]
UpperCamelCase_    						=       """answers""" if """answers""" in column_names else column_names[2]

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

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






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[int]						)				->  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 :							Union[str, 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.
						lowercase :							int    		=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=__magic_name__   , stride=args.doc_stride   , return_overflowing_tokens=__magic_name__   , return_offsets_mapping=__magic_name__   , 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 :							Union[str, 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.
						lowercase :							Tuple    		=[]

						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 :							Dict    		=tokenized_examples.sequence_ids(__magic_name__						)
												lowercase :							Optional[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.
												lowercase :							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.
												lowercase :							Optional[int]    		=[
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]						)
												]

						return tokenized_examples


UpperCamelCase_    						=       raw_datasets["""validation"""]
# Validation Feature Creation
UpperCamelCase_    						=       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""",
)

UpperCamelCase_    						=       default_data_collator

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






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

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


UpperCamelCase_    						=       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 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  Optional[int]:
													return trt.volume(engine.get_binding_shape(__magic_name__						)						) * engine.get_binding_dtype(__magic_name__						).itemsize

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

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

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

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

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

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

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

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

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

							UpperCamelCase_    						=       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)

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







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












'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

