

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		:   Union[str, Any]										=       trt.Logger(trt.Logger.WARNING)
_UpperCAmelCase		:   int										=       absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_UpperCAmelCase		:   str										=       logging.getLogger(__name__)

_UpperCAmelCase		:   Tuple										=       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		:   Dict										=       parser.parse_args()

if args.tokenizer_name:
					_UpperCAmelCase		:   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)

_UpperCAmelCase		:   str										=       args.per_device_eval_batch_size

_UpperCAmelCase		:   List[str]										=       (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_UpperCAmelCase		:   int										=       True

_UpperCAmelCase		:   Union[str, Any]										=       """temp_engine/bert-fp32.engine"""
if args.fpaa:
					_UpperCAmelCase		:   Tuple										=       """temp_engine/bert-fp16.engine"""
if args.inta:
					_UpperCAmelCase		:   List[str]										=       """temp_engine/bert-int8.engine"""

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

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

					with builder.create_builder_config() as config:
										_UpperCAmelCase		:   int										=       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		:   Union[str, Any]										=       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		:   Any										=       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	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''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		:   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.
					_UpperCAmelCase		:   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.

_UpperCAmelCase		:   str										=       raw_datasets["""validation"""].column_names

_UpperCAmelCase		:   List[str]										=       """question""" if """question""" in column_names else column_names[0]
_UpperCAmelCase		:   List[str]										=       """context""" if """context""" in column_names else column_names[1]
_UpperCAmelCase		:   Dict										=       """answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_UpperCAmelCase		:   Tuple										=       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		:   Optional[Any]										=       min(args.max_seq_length, tokenizer.model_max_length)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''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		:   Dict										=       raw_datasets["""validation"""]
# Validation Feature Creation
_UpperCAmelCase		:   Tuple										=       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		:   Optional[Any]										=       default_data_collator

_UpperCAmelCase		:   Optional[int]										=       eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
_UpperCAmelCase		:   Dict										=       DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__="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		:   Optional[int]										=       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	( UpperCamelCase__      ):







												'''simple docstring'''






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

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

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

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

					_UpperCAmelCase		:   str										=       0.0
					_UpperCAmelCase		:   Any										=       0
					_UpperCAmelCase		:   int										=       timeit.default_timer()

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

										_UpperCAmelCase				,						_UpperCAmelCase		:   List[Any]										=       outputs
										_UpperCAmelCase		:   Any										=       torch.tensor(start_logits)
										_UpperCAmelCase		:   int										=       torch.tensor(end_logits)

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

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

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

					_UpperCAmelCase		:   Optional[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)

_UpperCAmelCase		:   Any										=       post_processing_function(eval_examples, eval_dataset, all_preds)
_UpperCAmelCase		:   Optional[Any]										=       metric.compute(predictions=prediction.predictions, references=prediction.label_ids)


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







import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_UpperCAmelCase		:   Tuple										=       logging.get_logger(__name__)







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			['''input_features''', '''is_longer''']




				def __init__(					self      ,   snake_case=64      ,   snake_case=4_8000      ,   snake_case=480      ,   snake_case=10      ,   snake_case=1024      ,   snake_case=0.0      ,   snake_case=False      ,   snake_case = 0      ,   snake_case = 1_4000      ,   snake_case = None      ,   snake_case = "fusion"      ,   snake_case = "repeatpad"      ,   **snake_case      ,   ):
											super().__init__(
											    feature_size=snake_case      ,   sampling_rate=snake_case      ,   padding_value=snake_case      ,   return_attention_mask=snake_case      ,   **snake_case      ,   )
											snake_case_							=    top_db
											snake_case_							=    truncation
											snake_case_							=    padding
											snake_case_							=    fft_window_size
											snake_case_							=    (fft_window_size >> 1) + 1
											snake_case_							=    hop_length
											snake_case_							=    max_length_s
											snake_case_							=    max_length_s * sampling_rate
											snake_case_							=    sampling_rate
											snake_case_							=    frequency_min
											snake_case_							=    frequency_max
											snake_case_							=    mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins      ,   num_mel_filters=snake_case      ,   min_frequency=snake_case      ,   max_frequency=snake_case      ,   sampling_rate=snake_case      ,   norm=snake_case      ,   mel_scale='htk'      ,   )
											snake_case_							=    mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins      ,   num_mel_filters=snake_case      ,   min_frequency=snake_case      ,   max_frequency=snake_case      ,   sampling_rate=snake_case      ,   norm='slaney'      ,   mel_scale='slaney'      ,   )




				def     a							(					self       ):
											snake_case_							=    copy.deepcopy(self.__dict__       )
											snake_case_							=    self.__class__.__name__
											if "mel_filters" in output:
																		del output["mel_filters"]
											if "mel_filters_slaney" in output:
																		del output["mel_filters_slaney"]
											return output




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    spectrogram(
											    snake_case      ,   window_function(self.fft_window_size      ,   'hann'       )      ,   frame_length=self.fft_window_size      ,   hop_length=self.hop_length      ,   power=2.0      ,   mel_filters=snake_case      ,   log_mel='dB'      ,   )
											return log_mel_spectrogram.T




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    np.array_split(list(range(0      ,   total_frames - chunk_frames + 1       )       )      ,   3       )
											if len(ranges[1]       ) == 0:
																		# if the audio is too short, we just use the first chunk
																		snake_case_							=    [0]
											if len(ranges[2]       ) == 0:
																		# if the audio is too short, we just use the first chunk
																		snake_case_							=    [0]
											# randomly choose index for each part
											snake_case_							=    np.random.choice(ranges[0]       )
											snake_case_							=    np.random.choice(ranges[1]       )
											snake_case_							=    np.random.choice(ranges[2]       )

											snake_case_							=    mel[idx_front : idx_front + chunk_frames, :]
											snake_case_							=    mel[idx_middle : idx_middle + chunk_frames, :]
											snake_case_							=    mel[idx_back : idx_back + chunk_frames, :]

											snake_case_							=    torch.tensor(mel[None, None, :]       )
											snake_case_							=    torch.nn.functional.interpolate(
											    snake_case      ,   size=[chunk_frames, 64]      ,   mode='bilinear'      ,   align_corners=snake_case       )
											snake_case_							=    mel_shrink[0][0].numpy()
											snake_case_							=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]      ,   axis=0       )
											return mel_fusion




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case       ):
											if waveform.shape[0] > max_length:
																		if truncation == "rand_trunc":
																									snake_case_							=    True
																									# random crop to max_length (for compatibility) -> this should be handled by self.pad
																									snake_case_							=    len(snake_case       ) - max_length
																									snake_case_							=    np.random.randint(0      ,   overflow + 1       )
																									snake_case_							=    waveform[idx : idx + max_length]
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters_slaney       )[None, :]
																		elif truncation == "fusion":
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters       )
																									snake_case_							=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																									snake_case_							=    mel.shape[0]
																									if chunk_frames == total_frames:
																																# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																																# In this case, we just use the whole audio.
																																snake_case_							=    np.stack([mel, mel, mel, mel]      ,   axis=0       )
																																snake_case_							=    False
																									else:
																																snake_case_							=    self._random_mel_fusion(snake_case      ,   snake_case      ,   snake_case       )
																																snake_case_							=    True
																		else:
																									raise NotImplementedError(F'''data_truncating {truncation} not implemented'''       )

											else:
																		snake_case_							=    False
																		# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																		if waveform.shape[0] < max_length:
																									if padding == "repeat":
																																snake_case_							=    int(max_length / len(snake_case       )       )
																																snake_case_							=    np.stack(np.tile(snake_case      ,   n_repeat + 1       )       )[:max_length]
																									if padding == "repeatpad":
																																snake_case_							=    int(max_length / len(snake_case       )       )
																																snake_case_							=    np.stack(np.tile(snake_case      ,   snake_case       )       )
																									snake_case_							=    np.pad(snake_case      ,   (0, max_length - waveform.shape[0])      ,   mode='constant'      ,   constant_values=0       )

																		if truncation == "fusion":
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters       )
																									snake_case_							=    np.stack([input_mel, input_mel, input_mel, input_mel]      ,   axis=0       )
																		else:
																									snake_case_							=    self._np_extract_fbank_features(snake_case      ,   self.mel_filters_slaney       )[None, :]

											return input_mel, longer




				def __call__(					self      ,   snake_case      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   **snake_case      ,   ):
											snake_case_							=    truncation if truncation is not None else self.truncation
											snake_case_							=    padding if padding else self.padding

											if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
																									    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																									    F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''       )
											else:
																		logger.warning(
																		    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
																		    'Failing to do so can result in silent errors that might be hard to debug.'       )

											snake_case_							=    isinstance(snake_case      ,   np.ndarray       ) and len(raw_speech.shape       ) > 1
											if is_batched_numpy and len(raw_speech.shape       ) > 2:
																		raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''       )
											snake_case_							=    is_batched_numpy or (
											    isinstance(snake_case      ,   (list, tuple)       ) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list)       ))
											)

											if is_batched:
																		snake_case_							=    [np.asarray(snake_case      ,   dtype=np.floataa       ) for speech in raw_speech]
											elif not is_batched and not isinstance(snake_case      ,   np.ndarray       ):
																		snake_case_							=    np.asarray(snake_case      ,   dtype=np.floataa       )
											elif isinstance(snake_case      ,   np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
																		snake_case_							=    raw_speech.astype(np.floataa       )

											# always return batch
											if not is_batched:
																		snake_case_							=    [np.asarray(snake_case       )]

											# convert to mel spectrogram, truncate and pad if needed.
											snake_case_							=    [
											    self._get_input_mel(snake_case      ,   max_length if max_length else self.nb_max_samples      ,   snake_case      ,   snake_case       )
											    for waveform in raw_speech
											]

											snake_case_							=    []
											snake_case_							=    []
											for mel, longer in padded_inputs:
																		input_mel.append(snake_case       )
																		is_longer.append(snake_case       )

											if truncation == "fusion" and sum(snake_case       ) == 0:
																		# if no audio is longer than 10s, then randomly select one audio to be longer
																		snake_case_							=    np.random.randint(0      ,   len(snake_case       )       )
																		snake_case_							=    True

											if isinstance(input_mel[0]      ,   snake_case       ):
																		snake_case_							=    [np.asarray(snake_case      ,   dtype=np.floataa       ) for feature in input_mel]

											# is_longer is a list of bool
											snake_case_							=    [[longer] for longer in is_longer]

											snake_case_							=    {'input_features': input_mel, 'is_longer': is_longer}
											snake_case_							=    BatchFeature(snake_case       )

											if return_tensors is not None:
																		snake_case_							=    input_features.convert_to_tensors(snake_case       )

											return input_features




