

'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union

import datasets
import numpy as np
import torch
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

A_       						=							logging.getLogger(__name__)





@dataclass
class 							_snake_case :
   _A       :     str       =					field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,  )
   _A       :     bool       =					field(
       default=_a	,  metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}	,  )
   _A       :     str       =					field(
       default='''main'''	,  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}	,  )
   _A       :     bool       =					field(
       default=_a	,  metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       }	,  )





@dataclass
class 							_snake_case :
   _A       :     Optional[str]       =					field(default=_a	,  metadata={'''help''': '''The input training data file (a text file).'''}						)
   _A       :     Optional[str]       =					field(
       default=_a	,  metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}	,  )
   _A       :     bool       =					field(
       default=_a	,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}						)
   _A       :     Optional[int]       =					field(
       default=_a	,  metadata={'''help''': '''The number of processes to use for the preprocessing.'''}	,  )
   _A       :     Optional[int]       =					field(
       default=_a	,  metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. If passed, sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }	,  )
   _A       :     bool       =					field(
       default=_a	,  metadata={
           '''help''': (
               '''Whether to pad all samples to the maximum sentence length. '''
               '''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
               '''efficient on GPU but very bad for TPU.'''
           )
       }	,  )
   _A       :     Optional[int]       =					field(
       default=_a	,  metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of training examples to this '''
               '''value if set.'''
           )
       }	,  )
   _A       :     Optional[int]       =					field(
       default=_a	,  metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
               '''value if set.'''
           )
       }	,  )
   def     __UpperCamelCase						( self			:      Any							):
     if self.train_file is not None:
       SCREAMING_SNAKE_CASE:Any						      =					self.train_file.split("."							)[-1]
       assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
     if self.validation_file is not None:
       SCREAMING_SNAKE_CASE:Any						      =					self.validation_file.split("."							)[-1]
       assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."





@dataclass
class 							_snake_case :
   _A       :     PreTrainedTokenizerBase
   _A       :     Union[bool, str, PaddingStrategy]       =					True
   _A       :     Optional[int]       =					None
   _A       :     Optional[int]       =					None
   def __call__( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Tuple							):
     SCREAMING_SNAKE_CASE:str						      =					"label" if "label" in features[0].keys() else "labels"
     SCREAMING_SNAKE_CASE:List[Any]						      =					[feature.pop(SCREAMING_SNAKE_CASE__							) for feature in features]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					len(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					len(features[0]["input_ids"]							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[
         [{k: v[i] for k, v in feature.items()} for i in range(SCREAMING_SNAKE_CASE__							)] for feature in features
     ]
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					list(chain(*SCREAMING_SNAKE_CASE__							)							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					self.tokenizer.pad(
         SCREAMING_SNAKE_CASE__     ,padding=self.padding     ,max_length=self.max_length     ,pad_to_multiple_of=self.pad_to_multiple_of     ,return_tensors="pt"     ,)

     # Un-flatten
     SCREAMING_SNAKE_CASE:Dict						      =					{k: v.view(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,-1							) for k, v in batch.items()}
     # Add back labels
     SCREAMING_SNAKE_CASE:Any						      =					torch.tensor(SCREAMING_SNAKE_CASE__     ,dtype=torch.intaa							)
     return batch





def    A_  ( ):
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  SCREAMING_SNAKE_CASE:Tuple						      =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
  if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
  else:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[int]						      =					parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_swag"      ,       snake_case      ,       snake_case )

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      ,       datefmt="%m/%d/%Y %H:%M:%S"      ,       handlers=[logging.StreamHandler(sys.stdout )]      ,       )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  SCREAMING_SNAKE_CASE:Any						      =					training_args.get_process_log_level()
  logger.setLevel(snake_case )
  datasets.utils.logging.set_verbosity(snake_case )
  transformers.utils.logging.set_verbosity(snake_case )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
      + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
  logger.info(F'''Training/evaluation parameters {training_args}''' )

  # Detecting last checkpoint.
  SCREAMING_SNAKE_CASE:Any						      =					None
  if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
    SCREAMING_SNAKE_CASE:str						      =					get_last_checkpoint(training_args.output_dir )
    if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
      raise ValueError(
          F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
          "Use --overwrite_output_dir to overcome." )
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
          "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Set seed before initializing model.
  set_seed(training_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).

  # In distributed training, the load_dataset function guarantee that only one local process can concurrently
  # download the dataset.
  if data_args.train_file is not None or data_args.validation_file is not None:
    SCREAMING_SNAKE_CASE:Dict						      =					{}
    if data_args.train_file is not None:
      SCREAMING_SNAKE_CASE:List[Any]						      =					data_args.train_file
    if data_args.validation_file is not None:
      SCREAMING_SNAKE_CASE:List[str]						      =					data_args.validation_file
    SCREAMING_SNAKE_CASE:Any						      =					data_args.train_file.split("." )[-1]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					load_dataset(
        snake_case      ,       data_files=snake_case      ,       cache_dir=model_args.cache_dir      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )
  else:
    # Downloading and loading the swag dataset from the hub.
    SCREAMING_SNAKE_CASE:Optional[int]						      =					load_dataset(
        "swag"      ,       "regular"      ,       cache_dir=model_args.cache_dir      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )
  # 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.

  # Load pretrained model and tokenizer

  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  SCREAMING_SNAKE_CASE:List[str]						      =					AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,       cache_dir=model_args.cache_dir      ,       revision=model_args.model_revision      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,       cache_dir=model_args.cache_dir      ,       use_fast=model_args.use_fast_tokenizer      ,       revision=model_args.model_revision      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )
  SCREAMING_SNAKE_CASE:Any						      =					AutoModelForMultipleChoice.from_pretrained(
      model_args.model_name_or_path      ,       from_tf=bool(".ckpt" in model_args.model_name_or_path )      ,       config=snake_case      ,       cache_dir=model_args.cache_dir      ,       revision=model_args.model_revision      ,       use_auth_token=True if model_args.use_auth_token else None      ,       )

  # When using your own dataset or a different dataset from swag, you will probably need to change this.
  SCREAMING_SNAKE_CASE:int						      =					[F'''ending{i}''' for i in range(4 )]
  SCREAMING_SNAKE_CASE:Dict						      =					"sent1"
  SCREAMING_SNAKE_CASE:int						      =					"sent2"

  if data_args.max_seq_length is None:
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer.model_max_length
    if max_seq_length > 1024:
      logger.warning(
          "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
          " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
          " override this default with `--block_size xxx`." )
      SCREAMING_SNAKE_CASE:Optional[int]						      =					1024
  else:
    if data_args.max_seq_length > tokenizer.model_max_length:
      logger.warning(
          F'''The max_seq_length passed ({data_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}.''' )
    SCREAMING_SNAKE_CASE:int						      =					min(data_args.max_seq_length      ,       tokenizer.model_max_length )

  # Preprocessing the datasets.
  def preprocess_function(snake_case ):
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					[[context] * 4 for context in examples[context_name]]
    SCREAMING_SNAKE_CASE:List[str]						      =					examples[question_header_name]
    SCREAMING_SNAKE_CASE:Tuple						      =					[
        [F'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(snake_case )
    ]

    # Flatten out
    SCREAMING_SNAKE_CASE:List[str]						      =					list(chain(*snake_case ) )
    SCREAMING_SNAKE_CASE:Tuple						      =					list(chain(*snake_case ) )

    # Tokenize
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenizer(
        snake_case      ,       snake_case      ,       truncation=snake_case      ,       max_length=snake_case      ,       padding="max_length" if data_args.pad_to_max_length else False      ,       )
    # Un-flatten
    return {k: [v[i : i + 4] for i in range(0      ,       len(snake_case )      ,       4 )] for k, v in tokenized_examples.items()}

  if training_args.do_train:
    if "train" not in raw_datasets:
      raise ValueError("--do_train requires a train dataset" )
    SCREAMING_SNAKE_CASE:Optional[int]						      =					raw_datasets["train"]
    if data_args.max_train_samples is not None:
      SCREAMING_SNAKE_CASE:List[str]						      =					min(len(snake_case )      ,       data_args.max_train_samples )
      SCREAMING_SNAKE_CASE:Optional[int]						      =					train_dataset.select(range(snake_case ) )
    with training_args.main_process_first(desc="train dataset map pre-processing" ):
      SCREAMING_SNAKE_CASE:int						      =					train_dataset.map(
          snake_case      ,       batched=snake_case      ,       num_proc=data_args.preprocessing_num_workers      ,       load_from_cache_file=not data_args.overwrite_cache      ,       )

  if training_args.do_eval:
    if "validation" not in raw_datasets:
      raise ValueError("--do_eval requires a validation dataset" )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					raw_datasets["validation"]
    if data_args.max_eval_samples is not None:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					min(len(snake_case )      ,       data_args.max_eval_samples )
      SCREAMING_SNAKE_CASE:List[Any]						      =					eval_dataset.select(range(snake_case ) )
    with training_args.main_process_first(desc="validation dataset map pre-processing" ):
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					eval_dataset.map(
          snake_case      ,       batched=snake_case      ,       num_proc=data_args.preprocessing_num_workers      ,       load_from_cache_file=not data_args.overwrite_cache      ,       )

    # Data collator
  SCREAMING_SNAKE_CASE:Any						      =					(
      default_data_collator
      if data_args.pad_to_max_length
      else DataCollatorForMultipleChoice(tokenizer=snake_case      ,       pad_to_multiple_of=8 if training_args.fpaa else None )
  )

  # Metric
  def compute_metrics(snake_case ):
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					eval_predictions
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					np.argmax(snake_case      ,       axis=1 )
    return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}

  # Initialize our Trainer
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					Trainer(
      model=snake_case      ,       args=snake_case      ,       train_dataset=train_dataset if training_args.do_train else None      ,       eval_dataset=eval_dataset if training_args.do_eval else None      ,       tokenizer=snake_case      ,       data_collator=snake_case      ,       compute_metrics=snake_case      ,       )

  # Training
  if training_args.do_train:
    SCREAMING_SNAKE_CASE:List[str]						      =					None
    if training_args.resume_from_checkpoint is not None:
      SCREAMING_SNAKE_CASE:Union[str, Any]						      =					training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      SCREAMING_SNAKE_CASE:Tuple						      =					last_checkpoint
    SCREAMING_SNAKE_CASE:Any						      =					trainer.train(resume_from_checkpoint=snake_case )
    trainer.save_model()  # Saves the tokenizer too for easy upload
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					train_result.metrics

    SCREAMING_SNAKE_CASE:Tuple						      =					(
        data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case )
    )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					min(snake_case      ,       len(snake_case ) )

    trainer.log_metrics("train"      ,       snake_case )
    trainer.save_metrics("train"      ,       snake_case )
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    logger.info("*** Evaluate ***" )

    SCREAMING_SNAKE_CASE:List[str]						      =					trainer.evaluate()
    SCREAMING_SNAKE_CASE:str						      =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case )
    SCREAMING_SNAKE_CASE:int						      =					min(snake_case      ,       len(snake_case ) )

    trainer.log_metrics("eval"      ,       snake_case )
    trainer.save_metrics("eval"      ,       snake_case )

  SCREAMING_SNAKE_CASE:Dict						      =					{
      "finetuned_from": model_args.model_name_or_path,
      "tasks": "multiple-choice",
      "dataset_tags": "swag",
      "dataset_args": "regular",
      "dataset": "SWAG",
      "language": "en",
  }

  if training_args.push_to_hub:
    trainer.push_to_hub(**snake_case )
  else:
    trainer.create_model_card(**snake_case )





def    A_  ( snake_case ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()









'''simple docstring'''

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


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Any       =					['''input_features''', '''is_longer''']
   def __init__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      str=64     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=48_000     ,SCREAMING_SNAKE_CASE__			:      List[str]=480     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=10     ,SCREAMING_SNAKE_CASE__			:      List[Any]=1_024     ,SCREAMING_SNAKE_CASE__			:      List[str]=0.0     ,SCREAMING_SNAKE_CASE__			:      Dict=False     ,SCREAMING_SNAKE_CASE__			:      float = 0     ,SCREAMING_SNAKE_CASE__			:      float = 14_000     ,SCREAMING_SNAKE_CASE__			:      int = None     ,SCREAMING_SNAKE_CASE__			:      str = "fusion"     ,SCREAMING_SNAKE_CASE__			:      str = "repeatpad"     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     super().__init__(
         feature_size=SCREAMING_SNAKE_CASE__     ,sampling_rate=SCREAMING_SNAKE_CASE__     ,padding_value=SCREAMING_SNAKE_CASE__     ,return_attention_mask=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__     ,)
     SCREAMING_SNAKE_CASE:Dict						      =					top_db
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					truncation
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					padding
     SCREAMING_SNAKE_CASE:int						      =					fft_window_size
     SCREAMING_SNAKE_CASE:Tuple						      =					(fft_window_size >> 1) + 1
     SCREAMING_SNAKE_CASE:Dict						      =					hop_length
     SCREAMING_SNAKE_CASE:List[Any]						      =					max_length_s
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					max_length_s * sampling_rate
     SCREAMING_SNAKE_CASE:int						      =					sampling_rate
     SCREAMING_SNAKE_CASE:Optional[int]						      =					frequency_min
     SCREAMING_SNAKE_CASE:List[str]						      =					frequency_max
     SCREAMING_SNAKE_CASE:int						      =					mel_filter_bank(
         num_frequency_bins=self.nb_frequency_bins     ,num_mel_filters=SCREAMING_SNAKE_CASE__     ,min_frequency=SCREAMING_SNAKE_CASE__     ,max_frequency=SCREAMING_SNAKE_CASE__     ,sampling_rate=SCREAMING_SNAKE_CASE__     ,norm=SCREAMING_SNAKE_CASE__     ,mel_scale="htk"     ,)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					mel_filter_bank(
         num_frequency_bins=self.nb_frequency_bins     ,num_mel_filters=SCREAMING_SNAKE_CASE__     ,min_frequency=SCREAMING_SNAKE_CASE__     ,max_frequency=SCREAMING_SNAKE_CASE__     ,sampling_rate=SCREAMING_SNAKE_CASE__     ,norm="slaney"     ,mel_scale="slaney"     ,)
   def     __UpperCamelCase						( self			:      str							):
     SCREAMING_SNAKE_CASE:List[str]						      =					copy.deepcopy(self.__dict__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      np.array     ,SCREAMING_SNAKE_CASE__			:      Optional[np.array] = None							):
     SCREAMING_SNAKE_CASE:Dict						      =					spectrogram(
         SCREAMING_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=SCREAMING_SNAKE_CASE__     ,log_mel="dB"     ,)
     return log_mel_spectrogram.T
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					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
       SCREAMING_SNAKE_CASE:Tuple						      =					[0]
     if len(ranges[2]							) == 0:
       # if the audio is too short, we just use the first chunk
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[0]
     # randomly choose index for each part
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					np.random.choice(ranges[0]							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					np.random.choice(ranges[1]							)
     SCREAMING_SNAKE_CASE:Any						      =					np.random.choice(ranges[2]							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					mel[idx_front : idx_front + chunk_frames, :]
     SCREAMING_SNAKE_CASE:Any						      =					mel[idx_middle : idx_middle + chunk_frames, :]
     SCREAMING_SNAKE_CASE:Optional[int]						      =					mel[idx_back : idx_back + chunk_frames, :]

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.tensor(mel[None, None, :]							)
     SCREAMING_SNAKE_CASE:List[str]						      =					torch.nn.functional.interpolate(
         SCREAMING_SNAKE_CASE__     ,size=[chunk_frames, 64]     ,mode="bilinear"     ,align_corners=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					mel_shrink[0][0].numpy()
     SCREAMING_SNAKE_CASE:Any						      =					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,axis=0							)
     return mel_fusion
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      np.array     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     if waveform.shape[0] > max_length:
       if truncation == "rand_trunc":
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					True
         # random crop to max_length (for compatibility) -> this should be handled by self.pad
         SCREAMING_SNAKE_CASE:Dict						      =					len(SCREAMING_SNAKE_CASE__							) - max_length
         SCREAMING_SNAKE_CASE:List[str]						      =					np.random.randint(0     ,overflow + 1							)
         SCREAMING_SNAKE_CASE:int						      =					waveform[idx : idx + max_length]
         SCREAMING_SNAKE_CASE:Optional[int]						      =					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__     ,self.mel_filters_slaney							)[None, :]
       elif truncation == "fusion":
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__     ,self.mel_filters							)
         SCREAMING_SNAKE_CASE:Optional[int]						      =					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
         SCREAMING_SNAKE_CASE:Dict						      =					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.
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.stack([mel, mel, mel, mel]     ,axis=0							)
           SCREAMING_SNAKE_CASE:Dict						      =					False
         else:
           SCREAMING_SNAKE_CASE:Optional[Any]						      =					self._random_mel_fusion(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
           SCREAMING_SNAKE_CASE:Optional[Any]						      =					True
       else:
         raise NotImplementedError(F'''data_truncating {truncation} not implemented'''							)

     else:
       SCREAMING_SNAKE_CASE:List[str]						      =					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":
           SCREAMING_SNAKE_CASE:List[Any]						      =					int(max_length / len(SCREAMING_SNAKE_CASE__							)							)
           SCREAMING_SNAKE_CASE:Dict						      =					np.stack(np.tile(SCREAMING_SNAKE_CASE__     ,n_repeat + 1							)							)[:max_length]
         if padding == "repeatpad":
           SCREAMING_SNAKE_CASE:Dict						      =					int(max_length / len(SCREAMING_SNAKE_CASE__							)							)
           SCREAMING_SNAKE_CASE:int						      =					np.stack(np.tile(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)							)
         SCREAMING_SNAKE_CASE:Tuple						      =					np.pad(SCREAMING_SNAKE_CASE__     ,(0, max_length - waveform.shape[0])     ,mode="constant"     ,constant_values=0							)

       if truncation == "fusion":
         SCREAMING_SNAKE_CASE:List[Any]						      =					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__     ,self.mel_filters							)
         SCREAMING_SNAKE_CASE:Dict						      =					np.stack([input_mel, input_mel, input_mel, input_mel]     ,axis=0							)
       else:
         SCREAMING_SNAKE_CASE:Any						      =					self._np_extract_fbank_features(SCREAMING_SNAKE_CASE__     ,self.mel_filters_slaney							)[None, :]

     return input_mel, longer







   def __call__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,SCREAMING_SNAKE_CASE__			:      str = None     ,SCREAMING_SNAKE_CASE__			:      Optional[str] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[int] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					truncation if truncation is not None else self.truncation
     SCREAMING_SNAKE_CASE:List[Any]						      =					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."							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					isinstance(SCREAMING_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}'''							)
     SCREAMING_SNAKE_CASE:Dict						      =					is_batched_numpy or (
         isinstance(SCREAMING_SNAKE_CASE__     ,(list, tuple)							) and (isinstance(raw_speech[0]     ,(np.ndarray, tuple, list)							))
     )

     if is_batched:
       SCREAMING_SNAKE_CASE:Any						      =					[np.asarray(SCREAMING_SNAKE_CASE__     ,dtype=np.floataa							) for speech in raw_speech]
     elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE__     ,np.ndarray							):
       SCREAMING_SNAKE_CASE:str						      =					np.asarray(SCREAMING_SNAKE_CASE__     ,dtype=np.floataa							)
     elif isinstance(SCREAMING_SNAKE_CASE__     ,np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					raw_speech.astype(np.floataa							)

     # always return batch
     if not is_batched:
       SCREAMING_SNAKE_CASE:List[str]						      =					[np.asarray(SCREAMING_SNAKE_CASE__							)]

     # convert to mel spectrogram, truncate and pad if needed.
     SCREAMING_SNAKE_CASE:List[str]						      =					[
         self._get_input_mel(SCREAMING_SNAKE_CASE__     ,max_length if max_length else self.nb_max_samples     ,SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)
         for waveform in raw_speech
     ]

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[]
     SCREAMING_SNAKE_CASE:Dict						      =					[]
     for mel, longer in padded_inputs:
       input_mel.append(SCREAMING_SNAKE_CASE__							)
       is_longer.append(SCREAMING_SNAKE_CASE__							)

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

     if isinstance(input_mel[0]     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Dict						      =					[np.asarray(SCREAMING_SNAKE_CASE__     ,dtype=np.floataa							) for feature in input_mel]

     # is_longer is a list of bool
     SCREAMING_SNAKE_CASE:int						      =					[[longer] for longer in is_longer]

     SCREAMING_SNAKE_CASE:Tuple						      =					{"input_features": input_mel, "is_longer": is_longer}
     SCREAMING_SNAKE_CASE:str						      =					BatchFeature(SCREAMING_SNAKE_CASE__							)

     if return_tensors is not None:
       SCREAMING_SNAKE_CASE:List[str]						      =					input_features.convert_to_tensors(SCREAMING_SNAKE_CASE__							)

     return input_features






