

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


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

UpperCamelCase__		 =     logging.getLogger(__name__)

UpperCamelCase__		 =     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''',
)

UpperCamelCase__		 =     parser.parse_args()

if args.tokenizer_name:
    UpperCamelCase__		 =     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)

UpperCamelCase__		 =     args.per_device_eval_batch_size

UpperCamelCase__		 =     (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCamelCase__		 =     True

UpperCamelCase__		 =     'temp_engine/bert-fp32.engine'
if args.fpaa:
    UpperCamelCase__		 =     'temp_engine/bert-fp16.engine'
if args.inta:
    UpperCamelCase__		 =     'temp_engine/bert-int8.engine'

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

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

    with builder.create_builder_config() as config:
        UpperCamelCase__		 =     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)
        UpperCamelCase__		 =     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)
        UpperCamelCase__		 =     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__      ( __A    :int	,__A    :Tuple	,__A    :str	,__A    :Optional[Any]	,__A    :Optional[int]	,__A    :int	,__A    :List[Any]	,__A    :List[str]		):


       """simple docstring"""




       __snake_case       	=							np.asarray(inputs["""input_ids"""]	,dtype=np.intaa		)
       __snake_case       	=							np.asarray(inputs["""attention_mask"""]	,dtype=np.intaa		)
       __snake_case       	=							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
       __snake_case       	=							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
       __snake_case       	=							time.time()
       __snake_case       	=							end_time - start_time
       __snake_case       	=							(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.
UpperCamelCase__		 =     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.
    UpperCamelCase__		 =     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.

UpperCamelCase__		 =     raw_datasets['validation'].column_names

UpperCamelCase__		 =     'question' if 'question' in column_names else column_names[0]
UpperCamelCase__		 =     'context' if 'context' in column_names else column_names[1]
UpperCamelCase__		 =     'answers' if 'answers' in column_names else column_names[2]

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

UpperCamelCase__		 =     min(args.max_seq_length, tokenizer.model_max_length)







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       __snake_case       	=							[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.
       __snake_case       	=							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.
       __snake_case       	=							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.
       __snake_case       	=							[]

       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).
              __snake_case       	=							tokenized_examples.sequence_ids(UpperCamelCase__		)
              __snake_case       	=							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.
              __snake_case       	=							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.
              __snake_case       	=							[
                  (o if sequence_ids[k] == context_index else None)
                  for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]		)
              ]

       return tokenized_examples


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

UpperCamelCase__		 =     default_data_collator

UpperCamelCase__		 =     eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
UpperCamelCase__		 =     DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)







def 						lowerCamelCase__      ( __A    :Tuple	,__A    :List[str]	,__A    :Optional[int]	,__A    :Dict="eval"		):


       """simple docstring"""




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

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


UpperCamelCase__		 =     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__      ( __A    :List[str]		):


           """simple docstring"""




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

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

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

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

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

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

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

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

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

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

    UpperCamelCase__		 =     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)

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




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








import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # Initialise PyTorch model
       A__		   =		BigBirdConfig.from_json_file(UpperCamelCase__    )
       print(f"Building PyTorch model from configuration: {config}"    )

       if is_trivia_qa:
              A__		   =		BigBirdForQuestionAnswering(UpperCamelCase__    )
       else:
              A__		   =		BigBirdForPreTraining(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_big_bird(UpperCamelCase__					,	UpperCamelCase__					,	is_trivia_qa=UpperCamelCase__    )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"    )
       model.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[int]							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--big_bird_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
       )
       a__: Tuple							   =						parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





