
'''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


__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=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   =       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 << 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   =       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    UpperCAmelCase_						(__a :       List[Any]					,      __a :       str					,      __a :       List[str]					,      __a :       Optional[Any]					,      __a :       Any					,      __a :       List[str]					,      __a :       int					,      __a :       List[Any]					):






   """simple docstring"""

   _a							:      Union[str, Any]     =  np.asarray(inputs['input_ids']					,      dtype=np.intaa					)
   _a							:      Optional[Any]     =  np.asarray(inputs['attention_mask']					,      dtype=np.intaa					)
   _a							:      Any     =  np.asarray(inputs['token_type_ids']					,      dtype=np.intaa					)

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






   """simple docstring"""

   _a							:      Tuple     =  [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							:      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=__snake_case					,      stride=args.doc_stride					,      return_overflowing_tokens=__snake_case					,      return_offsets_mapping=__snake_case					,      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[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.
   _a							:      List[Any]     =  []

   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							:      str     =  tokenized_examples.sequence_ids(__snake_case					)
      _a							:      Optional[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.
      _a							:      int     =  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[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    UpperCAmelCase_						(__a :       Any					,      __a :       str					,      __a :       Optional[int]					,      __a :       Union[str, Any]="eval"					):






   """simple docstring"""

   _a							:      List[str]     =  postprocess_qa_predictions(
       examples=__snake_case					,      features=__snake_case					,      predictions=__snake_case					,      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=__snake_case					,      )
   # Format the result to the format the metric expects.
   if args.version_2_with_negative:
      _a							:      Optional[Any]     =  [
          {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
      ]
   else:
      _a							:      Union[str, Any]     =  [{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


__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    UpperCAmelCase_						(__a :       List[Any]					):






     """simple docstring"""

     return trt.volume(engine.get_binding_shape(__snake_case					)					) * engine.get_binding_dtype(__snake_case					).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=-1_0_0)
    __lowerCAmelCase   =       accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

    __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=-1_0_0)

  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 * 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   =       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}''')





"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if (
					    (cp >= 0x4E00 and cp <= 0x9FFF)
					    or (cp >= 0x3400 and cp <= 0x4DBF)  #
					    or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
					    or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
					    or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
					    or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
					    or (cp >= 0xF900 and cp <= 0xFAFF)
					    or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
					):  #
										return True

					return False



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

					for token in tokens:
										_UpperCamelCase									=    len(__snake_case      ) > 1 and is_chinese(__snake_case      )
										if chinese_word:
															word_set.add(__snake_case      )
					_UpperCamelCase									=    list(__snake_case      )
					return word_list



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					if not chinese_word_set:
										return bert_tokens
					_UpperCamelCase									=    max([len(__snake_case      ) for w in chinese_word_set]      )

					_UpperCamelCase									=    bert_tokens
					_UpperCamelCase					,    _UpperCamelCase									=    0, len(__snake_case      )
					while start < end:
										_UpperCamelCase									=    True
										if is_chinese(bert_word[start]      ):
															_UpperCamelCase									=    min(end - start,	__snake_case      )
															for i in range(__snake_case,	1,	-1      ):
																				_UpperCamelCase									=    ''''''.join(bert_word[start : start + i]      )
																				if whole_word in chinese_word_set:
																									for j in range(start + 1,	start + i      ):
																														_UpperCamelCase									=    '''##''' + bert_word[j]
																									_UpperCamelCase									=    start + i
																									_UpperCamelCase									=    False
																									break
										if single_word:
															start += 1
					return bert_word



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    ltp_tokenizer.pipeline(lines[i : i + 1_00],	tasks=['''cws''']      ).cws
										_UpperCamelCase									=    [get_chinese_word(__snake_case      ) for r in res]
										ltp_res.extend(__snake_case      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    bert_tokenizer(lines[i : i + 1_00],	add_special_tokens=__snake_case,	truncation=__snake_case,	max_length=5_12      )
										bert_res.extend(res['''input_ids''']      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for input_ids, chinese_word in zip(__snake_case,	__snake_case      ):
										_UpperCamelCase									=    []
										for id in input_ids:
															_UpperCamelCase									=    bert_tokenizer._convert_id_to_token(__snake_case      )
															input_tokens.append(__snake_case      )
										_UpperCamelCase									=    add_sub_symbol(__snake_case,	__snake_case      )
										_UpperCamelCase									=    []
										# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
										for i, token in enumerate(__snake_case      ):
															if token[:2] == "##":
																				_UpperCamelCase									=    token[2:]
																				# save chinese tokens' pos
																				if len(__snake_case      ) == 1 and _is_chinese_char(ord(__snake_case      )      ):
																									ref_id.append(__snake_case      )
										ref_ids.append(__snake_case      )

					assert len(__snake_case      ) == len(__snake_case      )

					return ref_ids



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					with open(args.file_name,	'''r''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    f.readlines()
					_UpperCamelCase									=    [line.strip() for line in data if len(__snake_case      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
					_UpperCamelCase									=    LTP(args.ltp      )  # faster in GPU device
					_UpperCamelCase									=    BertTokenizer.from_pretrained(args.bert      )

					_UpperCamelCase									=    prepare_ref(__snake_case,	__snake_case,	__snake_case      )

					with open(args.save_path,	'''w''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    [json.dumps(__snake_case      ) + '''\n''' for ref in ref_ids]
										f.writelines(__snake_case      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(description="""prepare_chinese_ref""")
				parser.add_argument(
				    """--file_name""",
				    required=False,
				    type=str,
				    default="""./resources/chinese-demo.txt""",
				    help="""file need process, same as training data in lm""",
				)
				parser.add_argument(
				    """--ltp""",
				    required=False,
				    type=str,
				    default="""./resources/ltp""",
				    help="""resources for LTP tokenizer, usually a path""",
				)
				parser.add_argument(
				    """--bert""",
				    required=False,
				    type=str,
				    default="""./resources/robert""",
				    help="""resources for Bert tokenizer""",
				)
				parser.add_argument(
				    """--save_path""",
				    required=False,
				    type=str,
				    default="""./resources/ref.txt""",
				    help="""path to save res""",
				)

				_a								=						parser.parse_args()
				main(args)






