





"""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


a		:				Union[str, Any]  			=     trt.Logger(trt.Logger.WARNING)
a		:				Any  			=     absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

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

a		:				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''',
)

a		:				Optional[int]  			=     parser.parse_args()

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

		with builder.create_builder_config() as config:
				a		:				Tuple  			=     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		:				Optional[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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Any					, _lowercase		:					List[str]					, _lowercase		:					Dict					, _lowercase		:					int					, _lowercase		:					Optional[int]					, _lowercase		:					List[str]					, _lowercase		:					int      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	int			=       np.asarray(inputs["input_ids"]					, dtype=np.intaa      )
	a   :	Union[str, Any]			=       np.asarray(inputs["attention_mask"]					, dtype=np.intaa      )
	a   :	Tuple			=       np.asarray(inputs["token_type_ids"]					, dtype=np.intaa      )

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

a		:				Union[str, Any]  			=     '''question''' if '''question''' in column_names else column_names[0]
a		:				Optional[int]  			=     '''context''' if '''context''' in column_names else column_names[1]
a		:				int  			=     '''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
a		:				int  			=     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		:				Union[str, Any]  			=     min(args.max_seq_length, tokenizer.model_max_length)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Any      )				->Optional[Any]:






	'''simple docstring'''






	a   :	int			=       [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.
	a   :	Any			=       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=_lowerCAmelCase					, stride=args.doc_stride					, return_overflowing_tokens=_lowerCAmelCase					, return_offsets_mapping=_lowerCAmelCase					, 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.
	a   :	Optional[int]			=       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.
	a   :	int			=       []

	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).
		a   :	Dict			=       tokenized_examples.sequence_ids(_lowerCAmelCase      )
		a   :	str			=       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.
		a   :	Tuple			=       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.
		a   :	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		:				Optional[Any]  			=     raw_datasets['''validation''']
# Validation Feature Creation
a		:				str  			=     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		:				Dict  			=     default_data_collator

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Any					, _lowercase		:					Union[str, Any]					, _lowercase		:					Union[str, Any]="eval"      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	int			=       postprocess_qa_predictions(
	    examples=_lowerCAmelCase					, features=_lowerCAmelCase					, predictions=_lowerCAmelCase					, 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=_lowerCAmelCase					, )
	# Format the result to the format the metric expects.
	if args.version_2_with_negative:
		a   :	Any			=       [
		    {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
		]
	else:
		a   :	List[Any]			=       [{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


a		:				Optional[int]  			=     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		:					str      )				->Tuple:






			'''simple docstring'''






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

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

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

		# Create a stream in which to copy inputs/outputs and run inference.
		a		:				Tuple  			=     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		:				str  			=     0
		a		:				Optional[int]  			=     timeit.default_timer()

		a		:				Optional[int]  			=     None
		for step, batch in enumerate(eval_dataloader):
				a			,				a		:				Optional[Any]  			=     model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
				total_time += infer_time
				niter += 1

				a			,				a		:				str  			=     outputs
				a		:				int  			=     torch.tensor(start_logits)
				a		:				int  			=     torch.tensor(end_logits)

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

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

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

		a		:				Any  			=     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		:				Dict  			=     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"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 10					, _lowercase		:					int = 1000					, _lowercase		:					bool = True      )				->int:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	), "Invalid type of value(s) specified to function!"

	if min_val > max_val:
		raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"      )
	return min_val if option else max_val






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int      )				->int:






	'''simple docstring'''






	return int((number_a + number_a) / 2      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int					, _lowercase		:					int      )				->None:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      )
	), 'argument values must be type of "int"'

	if lower > higher:
		raise ValueError("argument value for lower and higher must be(lower > higher)"      )

	if not lower < to_guess < higher:
		raise ValueError(
		    "guess value must be within the range of lower and higher value"      )

	def answer(_lowercase		:					int      ) -> str:
		if number > to_guess:
			return "high"
		elif number < to_guess:
			return "low"
		else:
			return "same"

	print("started..."      )

	a   :	Optional[Any]			=       lower
	a   :	List[Any]			=       higher

	a   :	Tuple			=       []

	while True:
		a   :	List[Any]			=       get_avg(_lowercase					, _lowercase      )
		last_numbers.append(_lowercase      )

		if answer(_lowercase      ) == "low":
			a   :	Optional[int]			=       number
		elif answer(_lowercase      ) == "high":
			a   :	Tuple			=       number
		else:
			break

	print(F"""guess the number : {last_numbers[-1]}"""      )
	print(F"""details : {last_numbers!s}"""      )






def    _SCREAMING_SNAKE_CASE	(			)				->None:






	'''simple docstring'''






	a   :	Tuple			=       int(input("Enter lower value : "      ).strip()      )
	a   :	Dict			=       int(input("Enter high value : "      ).strip()      )
	a   :	Optional[int]			=       int(input("Enter value to guess : "      ).strip()      )
	guess_the_number(_lowercase					, _lowercase					, _lowercase      )


if __name__ == "__main__":
		main()


