


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


__lowerCAmelCase      :					Tuple    							=trt.Logger(trt.Logger.WARNING)
__lowerCAmelCase      :					int    							=absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__lowerCAmelCase      :					Tuple    							=logging.getLogger(__name__)

__lowerCAmelCase      :					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=3_8_4,
    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_2_8,
    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=2_0,
    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=3_0,
    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=4_2, 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',
)

__lowerCAmelCase      :					Tuple    							=parser.parse_args()

if args.tokenizer_name:
						__lowerCAmelCase      :					str    							=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)

__lowerCAmelCase      :					Optional[int]    							=args.per_device_eval_batch_size

__lowerCAmelCase      :					Dict    							=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCAmelCase      :					List[str]    							=True

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

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

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

						with builder.create_builder_config() as config:
												__lowerCAmelCase      :					Dict    							=1 << 5_0
												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)
												__lowerCAmelCase      :					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)
												__lowerCAmelCase      :					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 			_UpperCamelCase				( lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						np.asarray(inputs['''input_ids''']       ,			dtype=np.intaa )
						__SCREAMING_SNAKE_CASE		:	Tuple						     =						np.asarray(inputs['''attention_mask''']       ,			dtype=np.intaa )
						__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						np.asarray(inputs['''token_type_ids''']       ,			dtype=np.intaa )

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

__lowerCAmelCase      :					Tuple    							=raw_datasets['validation'].column_names

__lowerCAmelCase      :					Any    							='question' if 'question' in column_names else column_names[0]
__lowerCAmelCase      :					List[Any]    							='context' if 'context' in column_names else column_names[1]
__lowerCAmelCase      :					str    							='answers' if 'answers' in column_names else column_names[2]

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

__lowerCAmelCase      :					Tuple    							=min(args.max_seq_length, tokenizer.model_max_length)


def 			_UpperCamelCase				( lowercase__ ):
						__SCREAMING_SNAKE_CASE		:	Optional[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.
						__SCREAMING_SNAKE_CASE		:	Optional[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=UpperCAmelCase__       ,			stride=args.doc_stride       ,			return_overflowing_tokens=UpperCAmelCase__       ,			return_offsets_mapping=UpperCAmelCase__       ,			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.
						__SCREAMING_SNAKE_CASE		:	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.
						__SCREAMING_SNAKE_CASE		:	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).
												__SCREAMING_SNAKE_CASE		:	List[Any]						     =						tokenized_examples.sequence_ids(UpperCAmelCase__ )
												__SCREAMING_SNAKE_CASE		:	Any						     =						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.
												__SCREAMING_SNAKE_CASE		:	str						     =						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.
												__SCREAMING_SNAKE_CASE		:	List[str]						     =						[
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
												]

						return tokenized_examples


__lowerCAmelCase      :					Tuple    							=raw_datasets['validation']
# Validation Feature Creation
__lowerCAmelCase      :					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',
)

__lowerCAmelCase      :					Union[str, Any]    							=default_data_collator

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


def 			_UpperCamelCase				( lowercase__       ,			lowercase__       ,			lowercase__       ,			lowercase__="eval" ):
						__SCREAMING_SNAKE_CASE		:	str						     =						postprocess_qa_predictions(
						    examples=UpperCAmelCase__       ,			features=UpperCAmelCase__       ,			predictions=UpperCAmelCase__       ,			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=UpperCAmelCase__       ,			)
						# Format the result to the format the metric expects.
						if args.version_2_with_negative:
												__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						[
												    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
												]
						else:
												__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

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


__lowerCAmelCase      :					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 			_UpperCamelCase				( lowercase__ ):
												return trt.volume(engine.get_binding_shape(UpperCAmelCase__ ) ) * engine.get_binding_dtype(UpperCAmelCase__ ).itemsize

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

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

						# Create a stream in which to copy inputs/outputs and run inference.
						__lowerCAmelCase      :					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}""")

						__lowerCAmelCase      :					Optional[int]    							=0.0
						__lowerCAmelCase      :					Dict    							=0
						__lowerCAmelCase      :					List[Any]    							=timeit.default_timer()

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

												__lowerCAmelCase      :					Dict    							=outputs
												__lowerCAmelCase      :					Dict    							=torch.tensor(start_logits)
												__lowerCAmelCase      :					int    							=torch.tensor(end_logits)

												# necessary to pad predictions and labels for being gathered
												__lowerCAmelCase      :					List[Any]    							=accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
												__lowerCAmelCase      :					Union[str, Any]    							=accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

												__lowerCAmelCase      :					Dict    							=(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
												__lowerCAmelCase      :					Optional[int]    							=logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

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

						__lowerCAmelCase      :					Optional[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 * 1_0_0_0 / niter))
						logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_0_0_0))
						logger.info('Total Number of Inference =  %d', niter)

__lowerCAmelCase      :					Dict    							=post_processing_function(eval_examples, eval_dataset, all_preds)
__lowerCAmelCase      :					Tuple    							=metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    :  Tuple						  =		{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
         'UniSpeechForCTC',
         'UniSpeechForPreTraining',
         'UniSpeechForSequenceClassification',
         'UniSpeechModel',
         'UniSpeechPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech import (
              UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechForCTC,
              UniSpeechForPreTraining,
              UniSpeechForSequenceClassification,
              UniSpeechModel,
              UniSpeechPreTrainedModel,
          )

else:
     import sys

     a_    :  Optional[Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)