



'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""roformer"""






  def __init__(							self :       List[Any]	,_a :       Tuple=50000	,_a :       List[str]=None	,_a :       int=768	,_a :       List[str]=12	,_a :       Optional[Any]=12	,_a :       Union[str, Any]=3072	,_a :       Optional[int]="gelu"	,_a :       Dict=0.1	,_a :       List[str]=0.1	,_a :       Any=1536	,_a :       Optional[Any]=2	,_a :       List[Any]=0.02	,_a :       Dict=1e-12	,_a :       Union[str, Any]=0	,_a :       List[str]=False	,_a :       str=True	,**_a :       Optional[Any]	,):

        '''simple docstring'''




        super().__init__(pad_token_id=_a	,**_a )

        A_						:  Optional[int]          =				vocab_size
        A_						:  str          =				hidden_size if embedding_size is None else embedding_size
        A_						:  int          =				hidden_size
        A_						:  Any          =				num_hidden_layers
        A_						:  Tuple          =				num_attention_heads
        A_						:  List[str]          =				hidden_act
        A_						:  str          =				intermediate_size
        A_						:  Union[str, Any]          =				hidden_dropout_prob
        A_						:  Any          =				attention_probs_dropout_prob
        A_						:  Union[str, Any]          =				max_position_embeddings
        A_						:  List[str]          =				type_vocab_size
        A_						:  List[str]          =				initializer_range
        A_						:  List[str]          =				layer_norm_eps
        A_						:  Optional[Any]          =				rotary_value
        A_						:  Union[str, Any]          =				use_cache







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        if self.task == "multiple-choice":
              A_						:  Tuple          =				{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              A_						:  int          =				{0: """batch""", 1: """sequence"""}
        A_						:  Union[str, Any]          =				{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ] )




'''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


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

__magic_name__			=      logging.getLogger(__name__)

__magic_name__			=      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',
)

__magic_name__			=      parser.parse_args()

if args.tokenizer_name:
     __magic_name__			=      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)

__magic_name__			=      args.per_device_eval_batch_size

__magic_name__			=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__magic_name__			=      True

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

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

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

     with builder.create_builder_config() as config:
          __magic_name__			=      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)
          __magic_name__			=      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)
          __magic_name__			=      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					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Any							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      List[str]):
      A_						:  str          =				np.asarray(inputs["""input_ids"""]							,						dtype=np.intaa)
      A_						:  int          =				np.asarray(inputs["""attention_mask"""]							,						dtype=np.intaa)
      A_						:  Optional[int]          =				np.asarray(inputs["""token_type_ids"""]							,						dtype=np.intaa)

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

__magic_name__			=      raw_datasets['validation'].column_names

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

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

__magic_name__			=      min(args.max_seq_length, tokenizer.model_max_length)




def      lowerCamelCase					(      lowerCamelCase  :      Dict):
      # 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
      A_						:  List[Any]          =				[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[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=lowerCamelCase							,						stride=args.doc_stride							,						return_overflowing_tokens=lowerCamelCase							,						return_offsets_mapping=lowerCamelCase							,						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_						:  List[str]          =				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_						:  Union[str, 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_						:  Any          =				tokenized_examples.sequence_ids(lowerCamelCase)
            A_						:  Tuple          =				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_						:  Union[str, Any]          =				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_						:  Dict          =				[
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples["""offset_mapping"""][i])
            ]

      return tokenized_examples


__magic_name__			=      raw_datasets['validation']
# Validation Feature Creation
__magic_name__			=      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',
)

__magic_name__			=      default_data_collator

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




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Optional[Any]="eval"):
      # Post-processing: we match the start logits and end logits to answers in the original context.
      A_						:  Tuple          =				postprocess_qa_predictions(
          examples=lowerCamelCase							,						features=lowerCamelCase							,						predictions=lowerCamelCase							,						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=lowerCamelCase							,						)
      # Format the result to the format the metric expects.
      if args.version_2_with_negative:
            A_						:  Dict          =				[
                {"""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=lowerCamelCase							,						label_ids=lowerCamelCase)


__magic_name__			=      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					(      lowerCamelCase  :      Union[str, Any]):
           return trt.volume(engine.get_binding_shape(lowerCamelCase)) * engine.get_binding_dtype(lowerCamelCase).itemsize

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

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

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

     __magic_name__			=      0.0
     __magic_name__			=      0
     __magic_name__			=      timeit.default_timer()

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

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

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

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

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

     __magic_name__			=      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_000 / niter))
     logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1_000))






     logger.info('Total Number of Inference =  %d', niter)

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






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