'''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


_a   :	Dict 							=   trt.Logger(trt.Logger.WARNING)
_a   :	Optional[Any] 							=   absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_a   :	str 							=   logging.getLogger(__name__)

_a   :	List[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",
)

_a   :	Optional[int] 							=   parser.parse_args()

if args.tokenizer_name:
   _a   :	List[Any] 							=   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)

_a   :	Tuple 							=   args.per_device_eval_batch_size

_a   :	Dict 							=   (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_a   :	Dict 							=   True

_a   :	List[Any] 							=   '''temp_engine/bert-fp32.engine'''
if args.fpaa:
   _a   :	Optional[int] 							=   '''temp_engine/bert-fp16.engine'''
if args.inta:
   _a   :	List[str] 							=   '''temp_engine/bert-int8.engine'''

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

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

   with builder.create_builder_config() as config:
      _a   :	str 							=   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)
      _a   :	Optional[int] 							=   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)
      _a   :	int 							=   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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		np.asarray(inputs["input_ids"]							,    dtype=np.intaa						)
       __UpperCAmelCase  :				Any       				=		np.asarray(inputs["attention_mask"]							,    dtype=np.intaa						)
       __UpperCAmelCase  :				Optional[Any]       				=		np.asarray(inputs["token_type_ids"]							,    dtype=np.intaa						)

       # Copy inputs
       cuda.memcpy_htod_async(d_inputs[0]							,    input_ids.ravel()							,    _lowercase						)
       cuda.memcpy_htod_async(d_inputs[1]							,    attention_mask.ravel()							,    _lowercase						)
       cuda.memcpy_htod_async(d_inputs[2]							,    token_type_ids.ravel()							,    _lowercase						)
       # start time
       __UpperCAmelCase  :				Optional[int]       				=		time.time()
       # Run inference
       context.execute_async(
           bindings=[int(_lowercase						) for d_inp in d_inputs] + [int(_lowercase						), int(_lowercase						)]							,    stream_handle=stream.handle						)
       # Transfer predictions back from GPU
       cuda.memcpy_dtoh_async(_lowercase							,    _lowercase							,    _lowercase						)
       cuda.memcpy_dtoh_async(_lowercase							,    _lowercase							,    _lowercase						)
       # Synchronize the stream and take time
       stream.synchronize()
       # end time
       __UpperCAmelCase  :				str       				=		time.time()
       __UpperCAmelCase  :				Any       				=		end_time - start_time
       __UpperCAmelCase  :				List[Any]       				=		(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.
_a   :	int 							=   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.
   _a   :	int 							=   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.

_a   :	Dict 							=   raw_datasets['''validation'''].column_names

_a   :	Tuple 							=   '''question''' if '''question''' in column_names else column_names[0]
_a   :	Dict 							=   '''context''' if '''context''' in column_names else column_names[1]
_a   :	Any 							=   '''answers''' if '''answers''' in column_names else column_names[2]

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

_a   :	int 							=   min(args.max_seq_length, tokenizer.model_max_length)

def _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		[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[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=_lowercase							,    stride=args.doc_stride							,    return_overflowing_tokens=_lowercase							,    return_offsets_mapping=_lowercase							,    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  :				Tuple       				=		tokenized_examples.sequence_ids(_lowercase						)
              __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  :				Dict       				=		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  :				Any       				=		[
                  (o if sequence_ids[k] == context_index else None)
                  for k, o in enumerate(tokenized_examples["offset_mapping"][i]						)
              ]

       return tokenized_examples


_a   :	Union[str, Any] 							=   raw_datasets['''validation''']
# Validation Feature Creation
_a   :	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",
)

_a   :	Optional[int] 							=   default_data_collator

_a   :	Tuple 							=   eval_dataset.remove_columns(["example_id", "offset_mapping"])
_a   :	Optional[Any] 							=   DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__="eval"						)					->       List[str]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		postprocess_qa_predictions(
           examples=_lowercase							,    features=_lowercase							,    predictions=_lowercase							,    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=_lowercase							,    )
       # Format the result to the format the metric expects.
       if args.version_2_with_negative:
              __UpperCAmelCase  :				Optional[Any]       				=		[
                  {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
              ]
       else:
              __UpperCAmelCase  :				List[str]       				=		[{"id": k, "prediction_text": v} for k, v in predictions.items()]

       __UpperCAmelCase  :				Optional[Any]       				=		[{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
       return EvalPrediction(predictions=_lowercase							,    label_ids=_lowercase						)


_a   :	Union[str, Any] 							=   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 _lowercase	(      lowerCamelCase__						)					->       List[Any]:





          """simple docstring"""


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

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

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

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

   _a   :	str 							=   0.0
   _a   :	List[Any] 							=   0
   _a   :	Any 							=   timeit.default_timer()

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

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

      # necessary to pad predictions and labels for being gathered
      _a   :	int 							=   accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
      _a   :	List[str] 							=   accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

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

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

   _a   :	Union[str, Any] 							=   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)

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


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



'''simple docstring'''




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


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	int 							=   {
    "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 		__A       (__magic_name__   ):
       snake_case       :Optional[int]				    =					"speech_to_text_2"
       snake_case       :List[Any]				    =					["past_key_values"]
       snake_case       :str				    =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}






       def __init__(      self				,     UpperCamelCase_=1_00_00				,     UpperCamelCase_=6				,     UpperCamelCase_=20_48				,     UpperCamelCase_=4				,     UpperCamelCase_=0.0				,     UpperCamelCase_=True				,     UpperCamelCase_="relu"				,     UpperCamelCase_=2_56				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=2				,     UpperCamelCase_=True				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_=10_24				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Any       				=		vocab_size
              __UpperCAmelCase  :				Optional[int]       				=		d_model
              __UpperCAmelCase  :				Tuple       				=		decoder_ffn_dim
              __UpperCAmelCase  :				List[str]       				=		decoder_layers
              __UpperCAmelCase  :				str       				=		decoder_attention_heads
              __UpperCAmelCase  :				Dict       				=		dropout
              __UpperCAmelCase  :				Optional[Any]       				=		attention_dropout
              __UpperCAmelCase  :				int       				=		activation_dropout
              __UpperCAmelCase  :				Dict       				=		activation_function
              __UpperCAmelCase  :				Tuple       				=		init_std
              __UpperCAmelCase  :				Any       				=		decoder_layerdrop
              __UpperCAmelCase  :				str       				=		use_cache
              __UpperCAmelCase  :				int       				=		decoder_layers
              __UpperCAmelCase  :				Any       				=		scale_embedding  # scale factor will be sqrt(d_model) if True
              __UpperCAmelCase  :				Union[str, Any]       				=		max_target_positions

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






