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


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

snake_case__				= logging.getLogger(__name__)

snake_case__				= 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',
)

snake_case__				= parser.parse_args()

if args.tokenizer_name:
     snake_case__				= 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)

snake_case__				= args.per_device_eval_batch_size

snake_case__				= (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
snake_case__				= True

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

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

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

     with builder.create_builder_config() as config:
          snake_case__				= 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)
          snake_case__				= 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)
          snake_case__				= 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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''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() ,						__UpperCAmelCase		)
      cuda.memcpy_htod_async(d_inputs[1] ,						attention_mask.ravel() ,						__UpperCAmelCase		)
      cuda.memcpy_htod_async(d_inputs[2] ,						token_type_ids.ravel() ,						__UpperCAmelCase		)
      # start time
      _lowerCamelCase			    =				time.time()
      # Run inference
      context.execute_async(
          bindings=[int(__UpperCAmelCase		) for d_inp in d_inputs] + [int(__UpperCAmelCase		), int(__UpperCAmelCase		)] ,						stream_handle=stream.handle		)
      # Transfer predictions back from GPU
      cuda.memcpy_dtoh_async(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      cuda.memcpy_dtoh_async(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      # 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.
snake_case__				= 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.
     snake_case__				= 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.

snake_case__				= raw_datasets["""validation"""].column_names

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

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

snake_case__				= min(args.max_seq_length, tokenizer.model_max_length)
def       __magic_name__(  __UpperCAmelCase		)						->    List[Any]:



      '''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=__UpperCAmelCase ,						stride=args.doc_stride ,						return_overflowing_tokens=__UpperCAmelCase ,						return_offsets_mapping=__UpperCAmelCase ,						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(__UpperCAmelCase		)
            _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


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

snake_case__				= default_data_collator

snake_case__				= eval_dataset.remove_columns(['example_id', 'offset_mapping'])
snake_case__				= DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase="eval"		)						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				postprocess_qa_predictions(
          examples=__UpperCAmelCase ,						features=__UpperCAmelCase ,						predictions=__UpperCAmelCase ,						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=__UpperCAmelCase ,						)
      # 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=__UpperCAmelCase ,						label_ids=__UpperCAmelCase		)


snake_case__				= 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       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



           '''simple docstring'''

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

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

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

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

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

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

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

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

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

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

     snake_case__				= 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)

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




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

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           warnings.warn(
               '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use DPTImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)