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

    __lowercase						:	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 			UpperCAmelCase_     (  __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    :	Optional[Any]						=						model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
  total_time += infer_time
  niter += 1

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





 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}')










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


lowerCamelCase	:							Optional[int]				=logging.get_logger(__name__)

lowerCamelCase	:							Dict				={
    '''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  (    A__       ):
 _lowerCAmelCase       :       Tuple        = '''speech_to_text_2'''
 _lowerCAmelCase       :       Dict        = ['''past_key_values''']
 _lowerCAmelCase       :       Any        = {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=1_00_00							,   SCREAMING_SNAKE_CASE					:   List[Any]=6							,   SCREAMING_SNAKE_CASE					:   List[Any]=20_48							,   SCREAMING_SNAKE_CASE					:   Dict=4							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   List[Any]="relu"							,   SCREAMING_SNAKE_CASE					:   Tuple=2_56							,   SCREAMING_SNAKE_CASE					:   List[str]=0.1							,   SCREAMING_SNAKE_CASE					:   Tuple=0.0							,   SCREAMING_SNAKE_CASE					:   Any=0.0							,   SCREAMING_SNAKE_CASE					:   int=0.0_2							,   SCREAMING_SNAKE_CASE					:   Dict=2							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   Any=1							,   SCREAMING_SNAKE_CASE					:   List[Any]=0							,   SCREAMING_SNAKE_CASE					:   Any=2							,   SCREAMING_SNAKE_CASE					:   str=10_24							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  vocab_size
      UpperCamelCase__			:   Optional[Any]       				=  d_model
      UpperCamelCase__			:   Optional[Any]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Any       				=  decoder_attention_heads
      UpperCamelCase__			:   List[str]       				=  dropout
      UpperCamelCase__			:   int       				=  attention_dropout
      UpperCamelCase__			:   Optional[int]       				=  activation_dropout
      UpperCamelCase__			:   Union[str, Any]       				=  activation_function
      UpperCamelCase__			:   Tuple       				=  init_std
      UpperCamelCase__			:   Optional[int]       				=  decoder_layerdrop
      UpperCamelCase__			:   Dict       				=  use_cache
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Union[str, Any]       				=  scale_embedding  # scale factor will be sqrt(d_model) if True
      UpperCamelCase__			:   Optional[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							,   )