




"""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_				:    int							=     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_				:    Dict							=     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=3_84,
    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=1_28,
    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_				:    int							=     parser.parse_args()

if args.tokenizer_name:
					a_				:    List[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[str]							=     (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a_				:    Tuple							=     True

a_				:    Tuple							=     '''temp_engine/bert-fp32.engine'''
if args.fpaa:
					a_				:    Dict							=     '''temp_engine/bert-fp16.engine'''
if args.inta:
					a_				:    Optional[Any]							=     '''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_				:    List[Any]							=     [network.get_input(i) for i in range(network.num_inputs)]
					a_				:    Optional[Any]							=     [_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_				:    Any							=     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_				:    List[str]							=     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 						UpperCAmelCase				(			A__:    Any							,				A__:    Dict							,				A__:    Any							,				A__:    int							,				A__:    List[Any]							,				A__:    str							,				A__:    int							,				A__:    Optional[int]     )  ->     str:
			__lowerCamelCase					:	str	       =				np.asarray(inputs['input_ids']							,				dtype=np.intaa     )
			__lowerCamelCase					:	Tuple	       =				np.asarray(inputs['attention_mask']							,				dtype=np.intaa     )
			__lowerCamelCase					:	int	       =				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
			__lowerCamelCase					:	Tuple	       =				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
			__lowerCamelCase					:	str	       =				time.time()
			__lowerCamelCase					:	Union[str, Any]	       =				end_time - start_time
			__lowerCamelCase					:	Optional[Any]	       =				(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_				:    Tuple							=     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_				:    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_				:    Optional[Any]							=     raw_datasets['''validation'''].column_names

a_				:    Dict							=     '''question''' if '''question''' in column_names else column_names[0]
a_				:    Any							=     '''context''' if '''context''' in column_names else column_names[1]
a_				:    Union[str, Any]							=     '''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
a_				:    List[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_				:    List[str]							=     min(args.max_seq_length, tokenizer.model_max_length)





def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     str:
			__lowerCamelCase					:	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.
			__lowerCamelCase					:	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=_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.
			__lowerCamelCase					:	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.
			__lowerCamelCase					:	Dict	       =				[]

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

			return tokenized_examples


a_				:    Dict							=     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_				:    Optional[Any]							=     default_data_collator

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





def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    Dict							,				A__:    Optional[Any]							,				A__:    Optional[int]="eval"     )  ->     List[str]:
			__lowerCamelCase					:	Any	       =				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:
						__lowerCamelCase					:	List[Any]	       =				[
						    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
						]
			else:
						__lowerCamelCase					:	Tuple	       =				[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


a_				:    Union[str, 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 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     Tuple:
								return trt.volume(engine.get_binding_shape(_lowercase     )     ) * engine.get_binding_dtype(_lowercase     ).itemsize

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

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

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

					a_				:    str							=     None
					for step, batch in enumerate(eval_dataloader):
										a_						,							a_				:    Union[str, 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_				:    Dict							=     torch.tensor(start_logits)
										a_				:    Tuple							=     torch.tensor(end_logits)

										# necessary to pad predictions and labels for being gathered
										a_				:    List[str]							=     accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
										a_				:    Any							=     accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

										a_				:    Optional[int]							=     (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
										a_				:    Optional[int]							=     logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

					a_				:    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 * 10_00 / niter))
					logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 10_00))






					logger.info('''Total Number of Inference =  %d''', niter)

a_				:    List[Any]							=     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"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_				:    Tuple										=     logging.get_logger(__name__)

a_				:    str										=     {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Union[str, Any]     		= 'openai-gpt'
					__a						:						List[Any]     		= {
					    'max_position_embeddings': 'n_positions',
					    'hidden_size': 'n_embd',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}
					def __init__(    self						,	__a=40478						,	__a=512						,	__a=768						,	__a=12						,	__a=12						,	__a="gelu"						,	__a=0.1						,	__a=0.1						,	__a=0.1						,	__a=1E-5						,	__a=0.02						,	__a="cls_index"						,	__a=True						,	__a=None						,	__a=True						,	__a=0.1						,	**__a						,	):
								__lowerCamelCase					:	List[str]	       =				vocab_size
								__lowerCamelCase					:	Union[str, Any]	       =				n_positions
								__lowerCamelCase					:	Optional[Any]	       =				n_embd
								__lowerCamelCase					:	int	       =				n_layer
								__lowerCamelCase					:	List[str]	       =				n_head
								__lowerCamelCase					:	Dict	       =				afn
								__lowerCamelCase					:	Optional[Any]	       =				resid_pdrop
								__lowerCamelCase					:	int	       =				embd_pdrop
								__lowerCamelCase					:	int	       =				attn_pdrop
								__lowerCamelCase					:	Dict	       =				layer_norm_epsilon
								__lowerCamelCase					:	List[str]	       =				initializer_range
								__lowerCamelCase					:	Any	       =				summary_type
								__lowerCamelCase					:	Tuple	       =				summary_use_proj
								__lowerCamelCase					:	List[Any]	       =				summary_activation
								__lowerCamelCase					:	str	       =				summary_first_dropout
								__lowerCamelCase					:	Tuple	       =				summary_proj_to_labels
								super().__init__(**__a     )



