






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


lowerCamelCase	:							List[str]				=trt.Logger(trt.Logger.WARNING)
lowerCamelCase	:							Optional[int]				=absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

lowerCamelCase	:							str				=logging.getLogger(__name__)

lowerCamelCase	:							str				=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''',
)

lowerCamelCase	:							int				=parser.parse_args()

if args.tokenizer_name:
 lowerCamelCase	:							Optional[int]				=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)

lowerCamelCase	:							Any				=args.per_device_eval_batch_size

lowerCamelCase	:							Union[str, Any]				=(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
lowerCamelCase	:							Tuple				=True

lowerCamelCase	:							Dict				='''temp_engine/bert-fp32.engine'''
if args.fpaa:
 lowerCamelCase	:							Any				='''temp_engine/bert-fp16.engine'''
if args.inta:
 lowerCamelCase	:							Optional[Any]				='''temp_engine/bert-int8.engine'''

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

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

 with builder.create_builder_config() as config:
  lowerCamelCase	:							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)
  lowerCamelCase	:							Dict				=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)
  lowerCamelCase	:							Tuple				=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							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	Union[str, Any]:
     UpperCamelCase__			:   Union[str, Any]       				=  np.asarray(inputs["input_ids"]					,    dtype=np.intaa			)
     UpperCamelCase__			:   Union[str, Any]       				=  np.asarray(inputs["attention_mask"]					,    dtype=np.intaa			)
     UpperCamelCase__			:   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
     UpperCamelCase__			:   str       				=  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
     UpperCamelCase__			:   Optional[Any]       				=  time.time()
     UpperCamelCase__			:   str       				=  end_time - start_time
     UpperCamelCase__			:   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.
lowerCamelCase	:							Union[str, Any]				=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.
 lowerCamelCase	:							List[str]				=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.

lowerCamelCase	:							List[Any]				=raw_datasets['''validation'''].column_names

lowerCamelCase	:							List[Any]				='''question''' if '''question''' in column_names else column_names[0]
lowerCamelCase	:							int				='''context''' if '''context''' in column_names else column_names[1]
lowerCamelCase	:							Any				='''answers''' if '''answers''' in column_names else column_names[2]

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

lowerCamelCase	:							Union[str, Any]				=min(args.max_seq_length, tokenizer.model_max_length)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     # 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
     UpperCamelCase__			:   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.
     UpperCamelCase__			:   Optional[Any]       				=  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.
     UpperCamelCase__			:   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.
     UpperCamelCase__			:   Optional[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).
          UpperCamelCase__			:   List[str]       				=  tokenized_examples.sequence_ids(__lowerCAmelCase			)
          UpperCamelCase__			:   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.
          UpperCamelCase__			:   List[str]       				=  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.
          UpperCamelCase__			:   Tuple       				=  [
              (o if sequence_ids[k] == context_index else None)
              for k, o in enumerate(tokenized_examples["offset_mapping"][i]			)
          ]

     return tokenized_examples


lowerCamelCase	:							int				=raw_datasets['''validation''']
# Validation Feature Creation
lowerCamelCase	:							Optional[Any]				=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''',
)

lowerCamelCase	:							Tuple				=default_data_collator

lowerCamelCase	:							Optional[int]				=eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
lowerCamelCase	:							List[Any]				=DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase="eval"			) ->	int:
     # Post-processing: we match the start logits and end logits to answers in the original context.
     UpperCamelCase__			:   List[str]       				=  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:
          UpperCamelCase__			:   List[Any]       				=  [
              {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
          ]
     else:
          UpperCamelCase__			:   Dict       				=  [{"id": k, "prediction_text": v} for k, v in predictions.items()]

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


lowerCamelCase	:							Dict				=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							(					__lowerCAmelCase			) ->	Optional[Any]:
      return trt.volume(engine.get_binding_shape(__lowerCAmelCase			)			) * engine.get_binding_dtype(__lowerCAmelCase			).itemsize

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

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

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

 lowerCamelCase	:							Dict				=0.0
 lowerCamelCase	:							Dict				=0
 lowerCamelCase	:							Union[str, Any]				=timeit.default_timer()

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

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

  # necessary to pad predictions and labels for being gathered
  lowerCamelCase	:							Tuple				=accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
  lowerCamelCase	:							Optional[Any]				=accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

  lowerCamelCase	:							int				=(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
  lowerCamelCase	:							List[str]				=logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

 if all_preds is not None:
  lowerCamelCase	:							List[str]				=nested_truncate(all_preds, len(eval_dataset))

 lowerCamelCase	:							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 * 1000 / niter))
 logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1000))




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

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




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







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
 import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class 			__a  (    unittest.TestCase       ,   A__       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  load_tool("text-to-speech"						)
      self.tool.setup()







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   Union[str, Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   Any       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   int       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)