



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 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Any       ,     snake_case__       :    List[Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    Tuple       ,     snake_case__       :    Optional[Any]	)	->     Optional[int]:
   UpperCamelCase    :  int							=	np.asarray(inputs['input_ids']       ,     dtype=np.intaa	)
   UpperCamelCase    :  List[str]							=	np.asarray(inputs['attention_mask']       ,     dtype=np.intaa	)
   UpperCamelCase    :  List[Any]							=	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
   UpperCamelCase    :  Union[str, Any]							=	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
   UpperCamelCase    :  int							=	time.time()
   UpperCamelCase    :  int							=	end_time - start_time
   UpperCamelCase    :  Tuple							=	(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 		UpperCamelCase	(   snake_case__       :    str	)	->     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
   UpperCamelCase    :  Optional[int]							=	[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    :  Dict							=	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.
   UpperCamelCase    :  List[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    :  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).
      UpperCamelCase    :  str							=	tokenized_examples.sequence_ids(snake_case__	)
      UpperCamelCase    :  Optional[Any]							=	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    :  Any							=	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    :  Optional[int]							=	[
          (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 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Tuple       ,     snake_case__       :    Any       ,     snake_case__       :    List[Any]="eval"	)	->     Dict:
   # Post-processing: we match the start logits and end logits to answers in the original context.
   UpperCamelCase    :  Dict							=	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:
      UpperCamelCase    :  Optional[int]							=	[
          {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
      ]
   else:
      UpperCamelCase    :  Union[str, Any]							=	[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


__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 		UpperCamelCase	(   snake_case__       :    Any	)	->     Union[str, Any]:
       return trt.volume(engine.get_binding_shape(snake_case__	)	) * engine.get_binding_dtype(snake_case__	).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   ,  __UpperCAmelCase          =       model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
        total_time += infer_time
        niter += 1

        __UpperCAmelCase   ,  __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 * 1_000 / niter))
    logger.info('''Total Inference Time =  {:.3f} ms'''.format(total_time * 1_000))


    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}""")








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Optional[Any]        =  "speech_to_text_2"
       UpperCAmelCase__				:					List[Any]        =  ["past_key_values"]
       UpperCAmelCase__				:					Any        =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
       def __init__(			self,			SCREAMING_SNAKE_CASE_=1_0000,			SCREAMING_SNAKE_CASE_=6,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_="relu",			SCREAMING_SNAKE_CASE_=256,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1024,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          UpperCamelCase    :  Optional[int]							=	vocab_size
          UpperCamelCase    :  List[str]							=	d_model
          UpperCamelCase    :  List[str]							=	decoder_ffn_dim
          UpperCamelCase    :  Optional[Any]							=	decoder_layers
          UpperCamelCase    :  Any							=	decoder_attention_heads
          UpperCamelCase    :  Tuple							=	dropout
          UpperCamelCase    :  str							=	attention_dropout
          UpperCamelCase    :  str							=	activation_dropout
          UpperCamelCase    :  Union[str, Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	init_std
          UpperCamelCase    :  Tuple							=	decoder_layerdrop
          UpperCamelCase    :  Dict							=	use_cache
          UpperCamelCase    :  Any							=	decoder_layers
          UpperCamelCase    :  Union[str, Any]							=	scale_embedding  # scale factor will be sqrt(d_model) if True
          UpperCamelCase    :  Union[str, Any]							=	max_target_positions

          super().__init__(
              pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			decoder_start_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)



