


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

lowerCamelCase						=		logging.getLogger(__name__)

lowerCamelCase						=		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''',
)

lowerCamelCase						=		parser.parse_args()

if args.tokenizer_name:
		lowerCamelCase						=		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						=		args.per_device_eval_batch_size

lowerCamelCase						=		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCamelCase						=		True

lowerCamelCase						=		'''temp_engine/bert-fp32.engine'''
if args.fpaa:
		lowerCamelCase						=		'''temp_engine/bert-fp16.engine'''
if args.inta:
		lowerCamelCase						=		'''temp_engine/bert-int8.engine'''

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

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

		with builder.create_builder_config() as config:
				lowerCamelCase						=		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)
				lowerCamelCase						=		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						=		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_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= np.asarray(inputs['''input_ids''']     ,       dtype=np.intaa	)
		lowerCAmelCase__       :      Any   					= np.asarray(inputs['''attention_mask''']     ,       dtype=np.intaa	)
		lowerCAmelCase__       :      List[Any]   					= 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
		lowerCAmelCase__       :      Optional[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
		lowerCAmelCase__       :      int   					= time.time()
		lowerCAmelCase__       :      int   					= end_time - start_time
		lowerCAmelCase__       :      Optional[int]   					= (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						=		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						=		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						=		raw_datasets['''validation'''].column_names

lowerCamelCase						=		'''question''' if '''question''' in column_names else column_names[0]
lowerCamelCase						=		'''context''' if '''context''' in column_names else column_names[1]
lowerCamelCase						=		'''answers''' if '''answers''' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
lowerCamelCase						=		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						=		min(args.max_seq_length, tokenizer.model_max_length)






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		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__       :      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=_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.
		lowerCAmelCase__       :      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.
		lowerCAmelCase__       :      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).
				lowerCAmelCase__       :      Dict   					= tokenized_examples.sequence_ids(_a	)
				lowerCAmelCase__       :      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__       :      Dict   					= 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__       :      Union[str, Any]   					= [
				    (o if sequence_ids[k] == context_index else None)
				    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]	)
				]

		return tokenized_examples


lowerCamelCase						=		raw_datasets['''validation''']
# Validation Feature Creation
lowerCamelCase						=		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						=		default_data_collator

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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a="eval"	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[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:
				lowerCAmelCase__       :      Tuple   					= [
				    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
				]
		else:
				lowerCAmelCase__       :      int   					= [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

		lowerCAmelCase__       :      str   					= [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
		return EvalPrediction(predictions=_a     ,       label_ids=_a	)


lowerCamelCase						=		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_   (	_a	):




				"""simple docstring"""






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

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

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

		# Create a stream in which to copy inputs/outputs and run inference.
		lowerCamelCase						=		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						=		0.0
		lowerCamelCase						=		0
		lowerCamelCase						=		timeit.default_timer()

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

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

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

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

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

		lowerCamelCase						=		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)

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




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








from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .notes_encoder import SpectrogramNotesEncoder
		from .continous_encoder import SpectrogramContEncoder
		from .pipeline_spectrogram_diffusion import (
		    SpectrogramContEncoder,
		    SpectrogramDiffusionPipeline,
		    TaFilmDecoder,
		)

try:
		if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
		from .midi_utils import MidiProcessor




