






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

a__  	=  logging.getLogger(__name__)

a__  	=  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""",
)

a__  	=  parser.parse_args()

if args.tokenizer_name:
	a__  	=  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__  	=  args.per_device_eval_batch_size

a__  	=  (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
a__  	=  True

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

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

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

	with builder.create_builder_config() as config:
		a__  	=  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)
		a__  	=  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__  	=  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    :						Dict	,       a    :						Optional[Any]	,       a    :						Union[str, Any]	,       a    :						List[str]	,       a    :						Tuple	,       a    :						Any	,       a    :						List[Any]	,       a    :						int			):
							snake_case__							=							np.asarray(inputs["""input_ids"""]	,       dtype=np.intaa			)
							snake_case__							=							np.asarray(inputs["""attention_mask"""]	,       dtype=np.intaa			)
							snake_case__							=							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
							snake_case__							=							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
							snake_case__							=							time.time()
							snake_case__							=							end_time - start_time
							snake_case__							=							(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__  	=  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__  	=  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__  	=  raw_datasets["""validation"""].column_names

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

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

def _UpperCAmelCase (						a    :						Any			):
							# 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
							snake_case__							=							[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.
							snake_case__							=							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.
							snake_case__							=							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.
							snake_case__							=							[]

							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).
														snake_case__							=							tokenized_examples.sequence_ids(__lowercase			)
														snake_case__							=							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.
														snake_case__							=							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.
														snake_case__							=							[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]			)
														]

							return tokenized_examples


a__  	=  raw_datasets["""validation"""]
# Validation Feature Creation
a__  	=  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__  	=  default_data_collator

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

def _UpperCAmelCase (						a    :						Any	,       a    :						str	,       a    :						Optional[int]	,       a    :						Tuple="eval"			):
							# Post-processing: we match the start logits and end logits to answers in the original context.
							snake_case__							=							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:
														snake_case__							=							[
														    {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
														]
							else:
														snake_case__							=							[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


a__  	=  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    :						Optional[Any]			):
								return trt.volume(engine.get_binding_shape(__lowercase			)			) * engine.get_binding_dtype(__lowercase			).itemsize

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

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

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

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

		a__  	=  outputs
		a__  	=  torch.tensor(start_logits)
		a__  	=  torch.tensor(end_logits)

		# necessary to pad predictions and labels for being gathered
		a__  	=  accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
		a__  	=  accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

		a__  	=  (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
		a__  	=  logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

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

	a__  	=  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)

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



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






'''simple docstring'''







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase      :   Optional[int]      =       logging.get_logger(__name__)

lowercase      :   Union[str, Any]      =       {
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					"""unispeech-sat"""







     def __init__(				self							,__a=32							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.1							,__a=0.1							,__a=0.02							,__a=1E-5							,__a="group"							,__a="gelu"							,__a=(512, 512, 512, 512, 512, 512, 512)							,__a=(5, 2, 2, 2, 2, 2, 2)							,__a=(10, 3, 3, 3, 3, 2, 2)							,__a=False							,__a=128							,__a=16							,__a=False							,__a=True							,__a=0.05							,__a=10							,__a=2							,__a=0.0							,__a=10							,__a=0							,__a=320							,__a=2							,__a=0.1							,__a=100							,__a=256							,__a=256							,__a=0.1							,__a="mean"							,__a=False							,__a=False							,__a=256							,__a=(512, 512, 512, 512, 1_500)							,__a=(5, 3, 3, 1, 1)							,__a=(1, 2, 3, 1, 1)							,__a=512							,__a=0							,__a=1							,__a=2							,__a=504							,**__a							,)      ->				str:
          super().__init__(**__a							,pad_token_id=__a							,bos_token_id=__a							,eos_token_id=__a							)
          snake_case			:      List[Any]        =			hidden_size
          snake_case			:      Any        =			feat_extract_norm
          snake_case			:      Dict        =			feat_extract_activation
          snake_case			:      List[Any]        =			list(__a							)
          snake_case			:      int        =			list(__a							)
          snake_case			:      Dict        =			list(__a							)
          snake_case			:      int        =			conv_bias
          snake_case			:      Optional[Any]        =			num_conv_pos_embeddings
          snake_case			:      Any        =			num_conv_pos_embedding_groups
          snake_case			:      Dict        =			len(self.conv_dim							)
          snake_case			:      str        =			num_hidden_layers
          snake_case			:      Optional[Any]        =			intermediate_size
          snake_case			:      Optional[Any]        =			hidden_act
          snake_case			:      Union[str, Any]        =			num_attention_heads
          snake_case			:      str        =			hidden_dropout
          snake_case			:      Tuple        =			attention_dropout
          snake_case			:      Tuple        =			activation_dropout
          snake_case			:      List[Any]        =			feat_proj_dropout
          snake_case			:      List[str]        =			final_dropout
          snake_case			:      Union[str, Any]        =			layerdrop
          snake_case			:      Optional[int]        =			layer_norm_eps
          snake_case			:      int        =			initializer_range
          snake_case			:      List[Any]        =			vocab_size
          snake_case			:      Any        =			num_clusters
          snake_case			:      Dict        =			do_stable_layer_norm
          snake_case			:      int        =			use_weighted_layer_sum

          if (
              (len(self.conv_stride							) != self.num_feat_extract_layers)
              or (len(self.conv_kernel							) != self.num_feat_extract_layers)
              or (len(self.conv_dim							) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                   """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                   F''' {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,'''
                   F''' `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          snake_case			:      Dict        =			apply_spec_augment
          snake_case			:      Optional[int]        =			mask_time_prob
          snake_case			:      int        =			mask_time_length
          snake_case			:      int        =			mask_time_min_masks
          snake_case			:      int        =			mask_feature_prob
          snake_case			:      Optional[Any]        =			mask_feature_length
          snake_case			:      List[Any]        =			mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          snake_case			:      Optional[Any]        =			num_codevectors_per_group
          snake_case			:      Optional[int]        =			num_codevector_groups
          snake_case			:      Any        =			contrastive_logits_temperature
          snake_case			:      List[str]        =			feat_quantizer_dropout
          snake_case			:      Dict        =			num_negatives
          snake_case			:      int        =			codevector_dim
          snake_case			:      Optional[int]        =			proj_codevector_dim
          snake_case			:      Union[str, Any]        =			diversity_loss_weight

          # ctc loss
          snake_case			:      Union[str, Any]        =			ctc_loss_reduction
          snake_case			:      Optional[int]        =			ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          snake_case			:      Dict        =			classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          snake_case			:      int        =			list(__a							)
          snake_case			:      Tuple        =			list(__a							)
          snake_case			:      List[Any]        =			list(__a							)
          snake_case			:      Dict        =			xvector_output_dim







     @property
     def 			snake_case_      (				self							)      ->				List[str]:
          return functools.reduce(operator.mul							,self.conv_stride							,1							)



