





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=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""",
)

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 << 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)
            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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :Union[str, Any]			,							snake_case__  :Union[str, Any]			,							snake_case__  :str			,							snake_case__  :Tuple			,							snake_case__  :int			,							snake_case__  :List[Any]			,							snake_case__  :Any     )      ->						int:
  _lowercase							= np.asarray(inputs['input_ids']			,							dtype=np.intaa     )
  _lowercase							= np.asarray(inputs['attention_mask']			,							dtype=np.intaa     )
  _lowercase							= 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
  _lowercase							= 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
  _lowercase							= time.time()
  _lowercase							= end_time - start_time
  _lowercase							= (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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]     )      ->						Optional[Any]:
  # 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							= [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							= 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.
  _lowercase							= 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							= []

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

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


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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Optional[int]:
        return trt.volume(engine.get_binding_shape(snake_case__     )     ) * engine.get_binding_dtype(snake_case__     ).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=-1_0_0)
            snake_case 				=       accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

            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=-1_0_0)

      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 * 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)

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 string

import numpy

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :int     )      ->						int:
  return b if a == 0 else greatest_common_divisor(b % a			,							snake_case__     )





class 				A_				:







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Dict      =  string.ascii_uppercase + string.digits
       # This cipher takes alphanumerics into account
       # i.e. a total of 36 characters

       # take x and return x % len(key_string)
       SCREAMING_SNAKE_CASE_      : Dict      =  numpy.vectorize(lambda UpperCAmelCase	: x % 3_6	)

       SCREAMING_SNAKE_CASE_      : List[Any]      =  numpy.vectorize(UpperCAmelCase	)







       def __init__(    self	:	Optional[Any]						,__A	:	numpy.ndarray  )						->					None:
         _lowercase							= self.modulus(__A  )  # mod36 calc's on the encrypt key
         self.check_determinant()  # validate the determinant of the encryption key
         _lowercase							= encrypt_key.shape[0]







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	str  )						->					int:
         return self.key_string.index(__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	int  )						->					str:
         return self.key_string[round(__A  )]







       def    __UpperCAmelCase (    self	:	str  )						->					None:
         _lowercase							= round(numpy.linalg.det(self.encrypt_key  )  )

         if det < 0:
           _lowercase							= det % len(self.key_string  )

         _lowercase							= len(self.key_string  )
         if greatest_common_divisor(__A						,len(self.key_string  )  ) != 1:
           _lowercase							= (
               F"""determinant modular {req_l} of encryption key({det}) """
               F"""is not co prime w.r.t {req_l}.\nTry another key."""
           )
           raise ValueError(__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	str  )						->					str:
         _lowercase							= [char for char in text.upper() if char in self.key_string]

         _lowercase							= chars[-1]
         while len(__A  ) % self.break_key != 0:
           chars.append(__A  )

         return "".join(__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	str  )						->					str:
         _lowercase							= self.process_text(text.upper()  )
         _lowercase							= ''

         for i in range(0						,len(__A  ) - self.break_key + 1						,self.break_key  ):
           _lowercase							= text[i : i + self.break_key]
           _lowercase							= [self.replace_letters(__A  ) for char in batch]
           _lowercase							= numpy.array([vec]  ).T
           _lowercase							= self.modulus(self.encrypt_key.dot(__A  )  ).T.tolist()[
               0
           ]
           _lowercase							= ''.join(
               self.replace_digits(__A  ) for num in batch_encrypted  )
           encrypted += encrypted_batch

         return encrypted







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					numpy.ndarray:
         _lowercase							= round(numpy.linalg.det(self.encrypt_key  )  )

         if det < 0:
           _lowercase							= det % len(self.key_string  )
         _lowercase							= None
         for i in range(len(self.key_string  )  ):
           if (det * i) % len(self.key_string  ) == 1:
             _lowercase							= i
             break

         _lowercase							= (
             det_inv
             * numpy.linalg.det(self.encrypt_key  )
             * numpy.linalg.inv(self.encrypt_key  )
         )

         return self.to_int(self.modulus(__A  )  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	str  )						->					str:
         _lowercase							= self.make_decrypt_key()
         _lowercase							= self.process_text(text.upper()  )
         _lowercase							= ''

         for i in range(0						,len(__A  ) - self.break_key + 1						,self.break_key  ):
           _lowercase							= text[i : i + self.break_key]
           _lowercase							= [self.replace_letters(__A  ) for char in batch]
           _lowercase							= numpy.array([vec]  ).T
           _lowercase							= self.modulus(decrypt_key.dot(__A  )  ).T.tolist()[0]
           _lowercase							= ''.join(
               self.replace_digits(__A  ) for num in batch_decrypted  )
           decrypted += decrypted_batch

         return decrypted

def 							SCREAMING_SNAKE_CASE__       (  )      ->						None:
  _lowercase							= int(input('Enter the order of the encryption key: '     )     )
  _lowercase							= []

  print('Enter each row of the encryption key with space separated integers'     )
  for _ in range(snake_case__     ):
    _lowercase							= [int(snake_case__     ) for x in input().split()]
    hill_matrix.append(snake_case__     )

  _lowercase							= HillCipher(numpy.array(snake_case__     )     )

  print('Would you like to encrypt or decrypt some text? (1 or 2)'     )
  _lowercase							= input('\n1. Encrypt\n2. Decrypt\n'     )
  if option == "1":
    _lowercase							= input('What text would you like to encrypt?: '     )
    print('Your encrypted text is:'     )
    print(hc.encrypt(snake_case__     )     )
  elif option == "2":
    _lowercase							= input('What text would you like to decrypt?: '     )
    print('Your decrypted text is:'     )
    print(hc.decrypt(snake_case__     )     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      main()