


"""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


__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    _lowerCamelCase   (     lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							Tuple  ):
 lowercase__       :       Union[str, Any]				=						np.asarray(inputs["""input_ids"""]       ,		dtype=np.intaa  )
 lowercase__       :       Optional[int]				=						np.asarray(inputs["""attention_mask"""]       ,		dtype=np.intaa  )
 lowercase__       :       Union[str, Any]				=						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
 lowercase__       :       int				=						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
 lowercase__       :       Union[str, Any]				=						time.time()
 lowercase__       :       Optional[Any]				=						end_time - start_time
 lowercase__       :       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.
__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    _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
 lowercase__       :       str				=						[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.
 lowercase__       :       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.
 lowercase__       :       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.
 lowercase__       :       List[str]				=						[]

 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).
  lowercase__       :       Optional[int]				=						tokenized_examples.sequence_ids(lowerCamelCase__  )
  lowercase__       :       Optional[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.
  lowercase__       :       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.
  lowercase__       :       int				=						[
      (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    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Tuple       ,		lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Optional[Any]="eval"  ):
 # Post-processing: we match the start logits and end logits to answers in the original context.
 lowercase__       :       int				=						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:
  lowercase__       :       Tuple				=						[
      {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
  ]
 else:
  lowercase__       :       List[str]				=						[{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

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


__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    _lowerCamelCase   (     lowerCamelCase__	:							Tuple  ):
    return trt.volume(engine.get_binding_shape(lowerCamelCase__  )  ) * engine.get_binding_dtype(lowerCamelCase__  ).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					,     __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					,     __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}")



"""simple docstring"""


import requests
from bsa import BeautifulSoup


def    _lowerCamelCase   (     lowerCamelCase__	:							str = "https://www.worldometers.info/coronavirus"  ):
 lowercase__       :       Optional[Any]				=						BeautifulSoup(requests.get(lowerCamelCase__  ).text       ,		"""html.parser"""  )
 lowercase__       :       Union[str, Any]				=						soup.findAll("""h1"""  )
 lowercase__       :       List[str]				=						soup.findAll("""div"""       ,		{"""class""": """maincounter-number"""}  )
 keys += soup.findAll("""span"""       ,		{"""class""": """panel-title"""}  )
 values += soup.findAll("""div"""       ,		{"""class""": """number-table-main"""}  )
 return {key.text.strip(): value.text.strip() for key, value in zip(lowerCamelCase__       ,		lowerCamelCase__  )}


if __name__ == "__main__":
   print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
   for key, value in world_covidaa_stats().items():
      print(F"{key}\n{value}\n")