





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


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

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCAmelCase_						   =    logging.getLogger(__name__)



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       Optional[int]      					=field(
      default=128							,    metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }							,    )
  a_							:       bool      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}							)
  a_							:       bool      					=field(
      default=UpperCAmelCase_							,    metadata={
          """help""": (
              """Whether to pad all samples to `max_seq_length`. """
              """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
          )
      }							,    )
  a_							:       Optional[int]      					=field(
      default=UpperCAmelCase_							,    metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }							,    )
  a_							:       Optional[int]      					=field(
      default=UpperCAmelCase_							,    metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }							,    )
  a_							:       Optional[int]      					=field(
      default=UpperCAmelCase_							,    metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of prediction examples to this """
              """value if set."""
          )
      }							,    )




@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       str      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
  a_							:       str      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Train language if it is different from the evaluation language."""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,    )
  a_							:       Optional[bool]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}							,    )
  a_							:       bool      					=field(
      default=UpperCAmelCase_							,    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=UpperCAmelCase_							,    metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }							,    )
  a_							:       bool      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}							,    )




def     lowerCamelCase_	(      )->		List[str]:
  # 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.

  _snake_case   :				Union[str, Any]														=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
  _snake_case							,					_snake_case							,					_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_xnli'    ,							lowerCAmelCase				)

  # 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()

  _snake_case   :				Union[str, Any]														=	training_args.get_process_log_level()
  logger.setLevel(lowerCAmelCase				)
  datasets.utils.logging.set_verbosity(lowerCAmelCase				)
  transformers.utils.logging.set_verbosity(lowerCAmelCase				)
  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.
  _snake_case   :				Optional[Any]														=	None
  if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
    _snake_case   :				List[Any]														=	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:
      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				)

  # In distributed training, the load_dataset function guarantees that only one local process can concurrently
  # download the dataset.
  # Downloading and loading xnli dataset from the hub.
  if training_args.do_train:
    if model_args.train_language is None:
      _snake_case   :				List[Any]														=	load_dataset(
          'xnli'    ,							model_args.language    ,							split='train'    ,							cache_dir=model_args.cache_dir    ,							use_auth_token=True if model_args.use_auth_token else None    ,							)
    else:
      _snake_case   :				Optional[Any]														=	load_dataset(
          'xnli'    ,							model_args.train_language    ,							split='train'    ,							cache_dir=model_args.cache_dir    ,							use_auth_token=True if model_args.use_auth_token else None    ,							)
    _snake_case   :				str														=	train_dataset.features['label'].names

  if training_args.do_eval:
    _snake_case   :				List[str]														=	load_dataset(
        'xnli'    ,							model_args.language    ,							split='validation'    ,							cache_dir=model_args.cache_dir    ,							use_auth_token=True if model_args.use_auth_token else None    ,							)
    _snake_case   :				Tuple														=	eval_dataset.features['label'].names

  if training_args.do_predict:
    _snake_case   :				List[str]														=	load_dataset(
        'xnli'    ,							model_args.language    ,							split='test'    ,							cache_dir=model_args.cache_dir    ,							use_auth_token=True if model_args.use_auth_token else None    ,							)
    _snake_case   :				Optional[int]														=	predict_dataset.features['label'].names

  # Labels
  _snake_case   :				Any														=	len(lowerCAmelCase				)

  # Load pretrained model and tokenizer
  # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  _snake_case   :				str														=	AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,							num_labels=lowerCAmelCase    ,							idalabel={str(lowerCAmelCase				): label for i, label in enumerate(lowerCAmelCase				)}    ,							labelaid={label: i for i, label in enumerate(lowerCAmelCase				)}    ,							finetuning_task='xnli'    ,							cache_dir=model_args.cache_dir    ,							revision=model_args.model_revision    ,							use_auth_token=True if model_args.use_auth_token else None    ,							)
  _snake_case   :				Any														=	AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,							do_lower_case=model_args.do_lower_case    ,							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    ,							)
  _snake_case   :				str														=	AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path    ,							from_tf=bool('.ckpt' in model_args.model_name_or_path				)    ,							config=lowerCAmelCase    ,							cache_dir=model_args.cache_dir    ,							revision=model_args.model_revision    ,							use_auth_token=True if model_args.use_auth_token else None    ,							ignore_mismatched_sizes=model_args.ignore_mismatched_sizes    ,							)

  # Preprocessing the datasets
  # Padding strategy
  if data_args.pad_to_max_length:
    _snake_case   :				Dict														=	'max_length'
  else:
    # We will pad later, dynamically at batch creation, to the max sequence length in each batch
    _snake_case   :				Optional[int]														=	False

  def preprocess_function(lowerCAmelCase:	Optional[Any]				):
    # Tokenize the texts
    return tokenizer(
        examples['premise']    ,							examples['hypothesis']    ,							padding=lowerCAmelCase    ,							max_length=data_args.max_seq_length    ,							truncation=lowerCAmelCase    ,							)

  if training_args.do_train:
    if data_args.max_train_samples is not None:
      _snake_case   :				Optional[int]														=	min(len(lowerCAmelCase				)    ,							data_args.max_train_samples				)
      _snake_case   :				List[Any]														=	train_dataset.select(range(lowerCAmelCase				)				)
    with training_args.main_process_first(desc='train dataset map pre-processing'				):
      _snake_case   :				str														=	train_dataset.map(
          lowerCAmelCase    ,							batched=lowerCAmelCase    ,							load_from_cache_file=not data_args.overwrite_cache    ,							desc='Running tokenizer on train dataset'    ,							)
    # Log a few random samples from the training set:
    for index in random.sample(range(len(lowerCAmelCase				)				)    ,							3				):
      logger.info(F"""Sample {index} of the training set: {train_dataset[index]}."""				)

  if training_args.do_eval:
    if data_args.max_eval_samples is not None:
      _snake_case   :				Optional[int]														=	min(len(lowerCAmelCase				)    ,							data_args.max_eval_samples				)
      _snake_case   :				Union[str, Any]														=	eval_dataset.select(range(lowerCAmelCase				)				)
    with training_args.main_process_first(desc='validation dataset map pre-processing'				):
      _snake_case   :				Optional[int]														=	eval_dataset.map(
          lowerCAmelCase    ,							batched=lowerCAmelCase    ,							load_from_cache_file=not data_args.overwrite_cache    ,							desc='Running tokenizer on validation dataset'    ,							)

  if training_args.do_predict:
    if data_args.max_predict_samples is not None:
      _snake_case   :				Union[str, Any]														=	min(len(lowerCAmelCase				)    ,							data_args.max_predict_samples				)
      _snake_case   :				Union[str, Any]														=	predict_dataset.select(range(lowerCAmelCase				)				)
    with training_args.main_process_first(desc='prediction dataset map pre-processing'				):
      _snake_case   :				int														=	predict_dataset.map(
          lowerCAmelCase    ,							batched=lowerCAmelCase    ,							load_from_cache_file=not data_args.overwrite_cache    ,							desc='Running tokenizer on prediction dataset'    ,							)

    # Get the metric function
  _snake_case   :				str														=	evaluate.load('xnli'				)

  # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(lowerCAmelCase:	EvalPrediction				):
    _snake_case   :				Dict														=	p.predictions[0] if isinstance(p.predictions    ,							lowerCAmelCase				) else p.predictions
    _snake_case   :				Any														=	np.argmax(lowerCAmelCase    ,							axis=1				)
    return metric.compute(predictions=lowerCAmelCase    ,							references=p.label_ids				)

  # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
  if data_args.pad_to_max_length:
    _snake_case   :				Tuple														=	default_data_collator
  elif training_args.fpaa:
    _snake_case   :				List[str]														=	DataCollatorWithPadding(lowerCAmelCase    ,							pad_to_multiple_of=8				)
  else:
    _snake_case   :				str														=	None

  # Initialize our Trainer
  _snake_case   :				List[Any]														=	Trainer(
      model=lowerCAmelCase    ,							args=lowerCAmelCase    ,							train_dataset=train_dataset if training_args.do_train else None    ,							eval_dataset=eval_dataset if training_args.do_eval else None    ,							compute_metrics=lowerCAmelCase    ,							tokenizer=lowerCAmelCase    ,							data_collator=lowerCAmelCase    ,							)

  # Training
  if training_args.do_train:
    _snake_case   :				Any														=	None
    if training_args.resume_from_checkpoint is not None:
      _snake_case   :				Dict														=	training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      _snake_case   :				Tuple														=	last_checkpoint
    _snake_case   :				str														=	trainer.train(resume_from_checkpoint=lowerCAmelCase				)
    _snake_case   :				Any														=	train_result.metrics
    _snake_case   :				List[str]														=	(
        data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase				)
    )
    _snake_case   :				List[str]														=	min(lowerCAmelCase    ,							len(lowerCAmelCase				)				)

    trainer.save_model()  # Saves the tokenizer too for easy upload

    trainer.log_metrics('train'    ,							lowerCAmelCase				)
    trainer.save_metrics('train'    ,							lowerCAmelCase				)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    logger.info('*** Evaluate ***'				)
    _snake_case   :				Dict														=	trainer.evaluate(eval_dataset=lowerCAmelCase				)

    _snake_case   :				List[Any]														=	data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase				)
    _snake_case   :				Tuple														=	min(lowerCAmelCase    ,							len(lowerCAmelCase				)				)

    trainer.log_metrics('eval'    ,							lowerCAmelCase				)
    trainer.save_metrics('eval'    ,							lowerCAmelCase				)

  # Prediction
  if training_args.do_predict:
    logger.info('*** Predict ***'				)
    _snake_case							,					_snake_case							,					_snake_case   :				Optional[int]														=	trainer.predict(lowerCAmelCase    ,							metric_key_prefix='predict'				)

    _snake_case   :				Tuple														=	(
        data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCAmelCase				)
    )
    _snake_case   :				List[str]														=	min(lowerCAmelCase    ,							len(lowerCAmelCase				)				)

    trainer.log_metrics('predict'    ,							lowerCAmelCase				)
    trainer.save_metrics('predict'    ,							lowerCAmelCase				)

    _snake_case   :				List[Any]														=	np.argmax(lowerCAmelCase    ,							axis=1				)
    _snake_case   :				int														=	os.path.join(training_args.output_dir    ,							'predictions.txt'				)
    if trainer.is_world_process_zero():
      with open(lowerCAmelCase    ,							'w'				) as writer:
        writer.write('index\tprediction\n'				)
        for index, item in enumerate(lowerCAmelCase				):
          _snake_case   :				Union[str, Any]														=	label_list[item]
          writer.write(F"""{index}\t{item}\n"""				)


if __name__ == "__main__":
      main()











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


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """MIT/ast-finetuned-audioset-10-10-0.4593""": (
        """https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
    ),
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Tuple      					="""audio-spectrogram-transformer"""







  def __init__(      self      :					List[Any]      ,				UpperCamelCase      :					Union[str, Any]=7_68      ,				UpperCamelCase      :					int=12      ,				UpperCamelCase      :					str=12      ,				UpperCamelCase      :					Tuple=30_72      ,				UpperCamelCase      :					Optional[Any]="gelu"      ,				UpperCamelCase      :					Any=0.0      ,				UpperCamelCase      :					Dict=0.0      ,				UpperCamelCase      :					List[Any]=0.02      ,				UpperCamelCase      :					Dict=1e-1_2      ,				UpperCamelCase      :					str=16      ,				UpperCamelCase      :					List[Any]=True      ,				UpperCamelCase      :					Any=10      ,				UpperCamelCase      :					Optional[int]=10      ,				UpperCamelCase      :					int=10_24      ,				UpperCamelCase      :					Optional[Any]=1_28      ,				**UpperCamelCase      :					Optional[Any]      ,				):


    '''simple docstring'''





    super().__init__(**UpperCamelCase							)

    _snake_case   :				Tuple														=	hidden_size
    _snake_case   :				str														=	num_hidden_layers
    _snake_case   :				Optional[Any]														=	num_attention_heads
    _snake_case   :				Optional[Any]														=	intermediate_size
    _snake_case   :				Optional[Any]														=	hidden_act
    _snake_case   :				List[str]														=	hidden_dropout_prob
    _snake_case   :				Union[str, Any]														=	attention_probs_dropout_prob
    _snake_case   :				Any														=	initializer_range
    _snake_case   :				List[str]														=	layer_norm_eps
    _snake_case   :				int														=	patch_size
    _snake_case   :				List[str]														=	qkv_bias
    _snake_case   :				int														=	frequency_stride
    _snake_case   :				List[Any]														=	time_stride
    _snake_case   :				List[Any]														=	max_length
    _snake_case   :				List[str]														=	num_mel_bins




