





'''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


A__    :       Any              =trt.Logger(trt.Logger.WARNING)
A__    :       List[Any]              =absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

A__    :       str              =logging.getLogger(__name__)

A__    :       Any              =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_84,
    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_28,
    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''',
)

A__    :       List[str]              =parser.parse_args()

if args.tokenizer_name:
   A__    :       List[str]              =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__    :       Union[str, Any]              =args.per_device_eval_batch_size

A__    :       Any              =(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
A__    :       List[Any]              =True

A__    :       Dict              ='temp_engine/bert-fp32.engine'
if args.fpaa:
   A__    :       Any              ='temp_engine/bert-fp16.engine'
if args.inta:
   A__    :       Union[str, Any]              ='temp_engine/bert-int8.engine'

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

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

   with builder.create_builder_config() as config:
      A__    :       Tuple              =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)
      A__    :       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)
      A__    :       str              =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__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							np.asarray(inputs["""input_ids"""]							,     dtype=np.intaa  )
      _lowerCAmelCase												=							np.asarray(inputs["""attention_mask"""]							,     dtype=np.intaa  )
      _lowerCAmelCase												=							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
      _lowerCAmelCase												=							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
      _lowerCAmelCase												=							time.time()
      _lowerCAmelCase												=							end_time - start_time
      _lowerCAmelCase												=							(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__    :       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.
   A__    :       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.

A__    :       str              =raw_datasets['validation'].column_names

A__    :       Optional[Any]              ='question' if 'question' in column_names else column_names[0]
A__    :       Any              ='context' if 'context' in column_names else column_names[1]
A__    :       str              ='answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
A__    :       Optional[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}."""
   )

A__    :       List[Any]              =min(args.max_seq_length, tokenizer.model_max_length)
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							[q.lstrip() for q in examples[question_column_name]]

      # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
      # in one example possible giving several features when a context is long, each of those features having a
      # context that overlaps a bit the context of the previous feature.
      _lowerCAmelCase												=							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.
      _lowerCAmelCase												=							tokenized_examples.pop("""overflow_to_sample_mapping"""  )

      # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
      # corresponding example_id and we will store the offset mappings.
      _lowerCAmelCase												=							[]

      for i in range(len(tokenized_examples["""input_ids"""]  )  ):
            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            _lowerCAmelCase												=							tokenized_examples.sequence_ids(lowerCamelCase_  )
            _lowerCAmelCase												=							1 if pad_on_right else 0

            # One example can give several spans, this is the index of the example containing this span of text.
            _lowerCAmelCase												=							sample_mapping[i]
            tokenized_examples["example_id"].append(examples["""id"""][sample_index]  )

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            _lowerCAmelCase												=							[
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]  )
            ]

      return tokenized_examples


A__    :       List[Any]              =raw_datasets['validation']
# Validation Feature Creation
A__    :       Tuple              =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__    :       Dict              =default_data_collator

A__    :       List[str]              =eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
A__    :       Optional[int]              =DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase="eval"  ):





      """simple docstring"""


      _lowerCAmelCase												=							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:
            _lowerCAmelCase												=							[
                {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
            ]
      else:
            _lowerCAmelCase												=							[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


A__    :       Any              =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__				(		lowerCAmelCase  ):





         """simple docstring"""


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

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

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

   # Create a stream in which to copy inputs/outputs and run inference.
   A__    :       Any              =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__    :       Union[str, Any]              =0.0
   A__    :       Tuple              =0
   A__    :       str              =timeit.default_timer()

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

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

      # necessary to pad predictions and labels for being gathered
      A__    :       Any              =accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
      A__    :       List[Any]              =accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

      A__    :       Optional[int]              =(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
      A__    :       List[Any]              =logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

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

   A__    :       int              =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 * 10_00 / niter))
   logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 10_00))





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

A__    :       Any              =post_processing_function(eval_examples, eval_dataset, all_preds)
A__    :       Union[str, Any]              =metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





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













'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							cva.getAffineTransform(lowerCAmelCase							,     lowerCAmelCase  )
      return cva.warpAffine(lowerCAmelCase							,     lowerCAmelCase							,     (rows, cols)  )


if __name__ == "__main__":
      # read original image
      A__    :       Any              =cva.imread(
          str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
      )
      # turn image in gray scale value
      A__    :       Any              =cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      A__       ,						A__    :       Tuple              =gray_img.shape

      # set different points to rotate image
      A__    :       Optional[int]              =np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
      A__    :       Tuple              =np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
      A__    :       List[str]              =np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
      A__    :       Dict              =np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

      # add all rotated images in a list
      A__    :       List[Any]              =[
          gray_img,
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      ]

      # plot different image rotations
      A__    :       Tuple              =plt.figure(1)
      A__    :       int              =['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
      for i, image in enumerate(images):
            plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
            plt.title(titles[i])
            plt.axis('''off''')
            plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
      plt.show()






