


'''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


UpperCAmelCase_      :		Optional[Any]          =		trt.Logger(trt.Logger.WARNING)
UpperCAmelCase_      :		str          =		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

UpperCAmelCase_      :		Optional[Any]          =		logging.getLogger(__name__)

UpperCAmelCase_      :		int          =		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",
)

UpperCAmelCase_      :		Any          =		parser.parse_args()

if args.tokenizer_name:
 UpperCAmelCase_      :		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)

UpperCAmelCase_      :		Optional[int]          =		args.per_device_eval_batch_size

UpperCAmelCase_      :		List[str]          =		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCAmelCase_      :		Tuple          =		True

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

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

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

 with builder.create_builder_config() as config:
  UpperCAmelCase_      :		List[str]          =		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)
  UpperCAmelCase_      :		List[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)
  UpperCAmelCase_      :		Dict          =		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		,	A		,	A		,	A		,	A		,	A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  List[Any]						=  np.asarray(inputs['input_ids']		,	dtype=np.intaa      )
   _a					:  Dict						=  np.asarray(inputs['attention_mask']		,	dtype=np.intaa      )
   _a					:  Union[str, 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
   _a					:  Union[str, 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
   _a					:  List[str]						=  time.time()
   _a					:  List[Any]						=  end_time - start_time
   _a					:  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.
UpperCAmelCase_      :		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.
 UpperCAmelCase_      :		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.

UpperCAmelCase_      :		List[str]          =		raw_datasets["validation"].column_names

UpperCAmelCase_      :		int          =		"question" if "question" in column_names else column_names[0]
UpperCAmelCase_      :		Optional[Any]          =		"context" if "context" in column_names else column_names[1]
UpperCAmelCase_      :		str          =		"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
UpperCAmelCase_      :		Union[str, 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}.'''
 )

UpperCAmelCase_      :		Optional[int]          =		min(args.max_seq_length, tokenizer.model_max_length)


def    UpperCAmelCase_  (       A      ):
   '''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					:  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=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.
   _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					:  Tuple						=  []

   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					:  Tuple						=  tokenized_examples.sequence_ids(A      )
      _a					:  int						=  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					:  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.
      _a					:  Optional[int]						=  [
          (o if sequence_ids[k] == context_index else None)
          for k, o in enumerate(tokenized_examples['offset_mapping'][i]      )
      ]

   return tokenized_examples


UpperCAmelCase_      :		Dict          =		raw_datasets["validation"]
# Validation Feature Creation
UpperCAmelCase_      :		List[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",
)

UpperCAmelCase_      :		str          =		default_data_collator

UpperCAmelCase_      :		int          =		eval_dataset.remove_columns(["example_id", "offset_mapping"])
UpperCAmelCase_      :		Optional[Any]          =		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)


def    UpperCAmelCase_  (       A		,	A		,	A		,	A="eval"      ):
   '''simple docstring'''





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

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


UpperCAmelCase_      :		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    UpperCAmelCase_  (       A      ):
    '''simple docstring'''





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

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

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

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

 UpperCAmelCase_      :		List[str]          =		0.0
 UpperCAmelCase_      :		Optional[Any]          =		0
 UpperCAmelCase_      :		Union[str, Any]          =		timeit.default_timer()

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

  UpperCAmelCase_     ,					UpperCAmelCase_      :		Union[str, Any]          =		outputs
  UpperCAmelCase_      :		Optional[int]          =		torch.tensor(start_logits)
  UpperCAmelCase_      :		Union[str, Any]          =		torch.tensor(end_logits)

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

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

 if all_preds is not None:
  UpperCAmelCase_      :		Optional[int]          =		nested_truncate(all_preds, len(eval_dataset))

 UpperCAmelCase_      :		Dict          =		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)

UpperCAmelCase_      :		Dict          =		post_processing_function(eval_examples, eval_dataset, all_preds)
UpperCAmelCase_      :		List[str]          =		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)







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






'''simple docstring'''




import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Dict						=  args.pruning_method
   _a					:  Optional[Any]						=  args.threshold

   _a					:  Union[str, Any]						=  args.model_name_or_path.rstrip('/'      )
   _a					:  Dict						=  args.target_model_path

   print(f'''Load fine-pruned model from {model_name_or_path}'''      )
   _a					:  List[str]						=  torch.load(os.path.join(A		,	'pytorch_model.bin'      )      )
   _a					:  str						=  {}

   for name, tensor in model.items():
      if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
         _a					:  List[Any]						=  tensor
         print(f'''Copied layer {name}'''      )
      elif "classifier" in name or "qa_output" in name:
         _a					:  List[str]						=  tensor
         print(f'''Copied layer {name}'''      )
      elif "bias" in name:
         _a					:  Optional[Any]						=  tensor
         print(f'''Copied layer {name}'''      )
      else:
         if pruning_method == "magnitude":
            _a					:  Tuple						=  MagnitudeBinarizer.apply(inputs=A		,	threshold=A      )
            _a					:  Union[str, Any]						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         elif pruning_method == "topK":
            if "mask_scores" in name:
               continue
            _a					:  str						=  name[:-6]
            _a					:  int						=  model[f'''{prefix_}mask_scores''']
            _a					:  List[str]						=  TopKBinarizer.apply(A		,	A      )
            _a					:  Tuple						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         elif pruning_method == "sigmoied_threshold":
            if "mask_scores" in name:
               continue
            _a					:  str						=  name[:-6]
            _a					:  List[str]						=  model[f'''{prefix_}mask_scores''']
            _a					:  Tuple						=  ThresholdBinarizer.apply(A		,	A		,	A      )
            _a					:  Union[str, Any]						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         elif pruning_method == "l0":
            if "mask_scores" in name:
               continue
            _a					:  Optional[int]						=  name[:-6]
            _a					:  Any						=  model[f'''{prefix_}mask_scores''']
            _a		,						_a					:  Optional[int]						=  -0.1, 1.1
            _a					:  List[str]						=  torch.sigmoid(A      )
            _a					:  Dict						=  s * (r - l) + l
            _a					:  int						=  s_bar.clamp(min=0.0		,	max=1.0      )
            _a					:  List[str]						=  tensor * mask
            print(f'''Pruned layer {name}'''      )
         else:
            raise ValueError('Unknown pruning method'      )

   if target_model_path is None:
      _a					:  Tuple						=  os.path.join(
          os.path.dirname(A      )		,	f'''bertarized_{os.path.basename(A      )}'''      )

   if not os.path.isdir(A      ):
      shutil.copytree(A		,	A      )
      print(f'''\nCreated folder {target_model_path}'''      )

   torch.save(A		,	os.path.join(A		,	'pytorch_model.bin'      )      )
   print('\nPruned model saved! See you later!'      )


if __name__ == "__main__":
 UpperCAmelCase_      :		str          =		argparse.ArgumentParser()

 parser.add_argument(
     "--pruning_method",
     choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
     type=str,
     required=True,
     help=(
         "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
         " sigmoied_threshold = Soft movement pruning)"
     ),
 )
 parser.add_argument(
     "--threshold",
     type=float,
     required=False,
     help=(
         "For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
         "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
         "Not needed for `l0`"
     ),
 )
 parser.add_argument(
     "--model_name_or_path",
     type=str,
     required=True,
     help="Folder containing the model that was previously fine-pruned",
 )
 parser.add_argument(
     "--target_model_path",
     default=None,
     type=str,
     required=False,
     help="Folder containing the model that was previously fine-pruned",
 )

 UpperCAmelCase_      :		Optional[Any]          =		parser.parse_args()

 main(args)


