



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

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

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    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


lowercase__		:    Tuple				   =     logging.getLogger(__name__)

# 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>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")

def     UpperCamelCase_   (   lowerCAmelCase__				:    np.ndarray   ,       lowerCAmelCase__				:    float   ,       lowerCAmelCase__				:    int = 1_6000						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    str								=			int(round(sample_rate * max_length						)						)
  if len(lowerCAmelCase__						) <= sample_length:
    return wav
  lowerCAmelCase_       :    List[str]								=			randint(0   ,       len(lowerCAmelCase__						) - sample_length - 1						)
  return wav[random_offset : random_offset + sample_length]







@dataclass
class 				UpperCamelCase__       :




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					field(default=lowercase_, metadata={"""help""": """Name of a dataset from the datasets package"""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """A file containing the training audio paths and labels."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """A file containing the validation audio paths and labels."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default="""train""", metadata={
              """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
          }, )
      _SCREAMING_SNAKE_CASE      =					field(
          default="""validation""", metadata={
              """help""": (
                  """The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
              )
          }, )
      _SCREAMING_SNAKE_CASE      =					field(
          default="""audio""", metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default="""label""", metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of training examples to this """
                  """value if set."""
              )
          }, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={
              """help""": (
                  """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                  """value if set."""
              )
          }, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=20, metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""}, )







@dataclass
class 				UpperCamelCase__       :




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					field(
          default="""facebook/wav2vec2-base""", metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default="""main""", metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Name or path of preprocessor config."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={
              """help""": (
                  """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                  """with private models)."""
              )
          }, )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}  )
      _SCREAMING_SNAKE_CASE      =					field(
          default=lowercase_, metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}, )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        if not self.freeze_feature_extractor and self.freeze_feature_encoder:
          warnings.warn(
              'The argument `--freeze_feature_extractor` is deprecated and '
              'will be removed in a future version. Use `--freeze_feature_encoder`'
              'instead. Setting `freeze_feature_encoder==True`.'   ,      SCREAMING_SNAKE_CASE_   ,      )
        if self.freeze_feature_extractor and not self.freeze_feature_encoder:
          raise ValueError(
              'The argument `--freeze_feature_extractor` is deprecated and '
              'should not be used in combination with `--freeze_feature_encoder`.'
              'Only make use of `--freeze_feature_encoder`.'	)




def     UpperCamelCase_   (   )						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    str								=			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.
    lowerCAmelCase_       :    Union[str, Any]								=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
  else:
    lowerCAmelCase_       :    Tuple								=			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_audio_classification'   ,       lowerCAmelCase__   ,       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()

  lowerCAmelCase_       :    str								=			training_args.get_process_log_level()
  logger.setLevel(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}"						)

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

  # Detecting last checkpoint.
  lowerCAmelCase_       :    Dict								=			None
  if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
    lowerCAmelCase_       :    int								=			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 train from scratch.'						)
    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.'						)

    # Initialize our dataset and prepare it for the audio classification task.
  lowerCAmelCase_       :    int								=			DatasetDict()
  lowerCAmelCase_       :    Union[str, Any]								=			load_dataset(
      data_args.dataset_name   ,       data_args.dataset_config_name   ,       split=data_args.train_split_name   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )
  lowerCAmelCase_       :    Any								=			load_dataset(
      data_args.dataset_name   ,       data_args.dataset_config_name   ,       split=data_args.eval_split_name   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )

  if data_args.audio_column_name not in raw_datasets["train"].column_names:
    raise ValueError(
        f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
        'Make sure to set `--audio_column_name` to the correct audio column - one of '
        f"{', '.join(raw_datasets['train'].column_names						)}."						)

  if data_args.label_column_name not in raw_datasets["train"].column_names:
    raise ValueError(
        f"--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. "
        'Make sure to set `--label_column_name` to the correct text column - one of '
        f"{', '.join(raw_datasets['train'].column_names						)}."						)

  # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
  # transformer outputs in the classifier, but it doesn't always lead to better accuracy
  lowerCAmelCase_       :    int								=			AutoFeatureExtractor.from_pretrained(
      model_args.feature_extractor_name or model_args.model_name_or_path   ,       return_attention_mask=model_args.attention_mask   ,       cache_dir=model_args.cache_dir   ,       revision=model_args.model_revision   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )

  # `datasets` takes care of automatically loading and resampling the audio,
  # so we just need to set the correct target sampling rate.
  lowerCAmelCase_       :    Optional[int]								=			raw_datasets.cast_column(
      data_args.audio_column_name   ,       datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate						)						)

  lowerCAmelCase_       :    Any								=			feature_extractor.model_input_names[0]

  def train_transforms(lowerCAmelCase__				:    Optional[Any]						):
    lowerCAmelCase_       :    Optional[Any]								=			[]
    for audio in batch[data_args.audio_column_name]:
      lowerCAmelCase_       :    Union[str, Any]								=			random_subsample(
          audio['array']   ,       max_length=data_args.max_length_seconds   ,       sample_rate=feature_extractor.sampling_rate						)
      subsampled_wavs.append(lowerCAmelCase__						)
    lowerCAmelCase_       :    Union[str, Any]								=			feature_extractor(lowerCAmelCase__   ,       sampling_rate=feature_extractor.sampling_rate						)
    lowerCAmelCase_       :    int								=			{model_input_name: inputs.get(lowerCAmelCase__						)}
    lowerCAmelCase_       :    Optional[Any]								=			list(batch[data_args.label_column_name]						)

    return output_batch

  def val_transforms(lowerCAmelCase__				:    int						):
    lowerCAmelCase_       :    Optional[int]								=			[audio['array'] for audio in batch[data_args.audio_column_name]]
    lowerCAmelCase_       :    Optional[int]								=			feature_extractor(lowerCAmelCase__   ,       sampling_rate=feature_extractor.sampling_rate						)
    lowerCAmelCase_       :    List[Any]								=			{model_input_name: inputs.get(lowerCAmelCase__						)}
    lowerCAmelCase_       :    Optional[int]								=			list(batch[data_args.label_column_name]						)

    return output_batch

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  lowerCAmelCase_       :    Tuple								=			raw_datasets['train'].features[data_args.label_column_name].names
  lowerCAmelCase_       :    Dict								=			{}, {}
  for i, label in enumerate(lowerCAmelCase__						):
    lowerCAmelCase_       :    List[Any]								=			str(lowerCAmelCase__						)
    lowerCAmelCase_       :    List[str]								=			label

  # Load the accuracy metric from the datasets package
  lowerCAmelCase_       :    Any								=			evaluate.load('accuracy'						)

  # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
  # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
  def compute_metrics(lowerCAmelCase__				:    str						):
    lowerCAmelCase_       :    Optional[int]								=			np.argmax(eval_pred.predictions   ,       axis=1						)
    return metric.compute(predictions=lowerCAmelCase__   ,       references=eval_pred.label_ids						)

  lowerCAmelCase_       :    Dict								=			AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path   ,       num_labels=len(lowerCAmelCase__						)   ,       labelaid=lowerCAmelCase__   ,       idalabel=lowerCAmelCase__   ,       finetuning_task='audio-classification'   ,       cache_dir=model_args.cache_dir   ,       revision=model_args.model_revision   ,       use_auth_token=True if model_args.use_auth_token else None   ,       )
  lowerCAmelCase_       :    Union[str, Any]								=			AutoModelForAudioClassification.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   ,       )

  # freeze the convolutional waveform encoder
  if model_args.freeze_feature_encoder:
    model.freeze_feature_encoder()

  if training_args.do_train:
    if data_args.max_train_samples is not None:
      lowerCAmelCase_       :    List[Any]								=			(
          raw_datasets['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
      )
    # Set the training transforms
    raw_datasets["train"].set_transform(lowerCAmelCase__   ,       output_all_columns=lowerCAmelCase__						)

  if training_args.do_eval:
    if data_args.max_eval_samples is not None:
      lowerCAmelCase_       :    Dict								=			(
          raw_datasets['eval'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
      )
    # Set the validation transforms
    raw_datasets["eval"].set_transform(lowerCAmelCase__   ,       output_all_columns=lowerCAmelCase__						)

  # Initialize our trainer
  lowerCAmelCase_       :    str								=			Trainer(
      model=lowerCAmelCase__   ,       args=lowerCAmelCase__   ,       train_dataset=raw_datasets['train'] if training_args.do_train else None   ,       eval_dataset=raw_datasets['eval'] if training_args.do_eval else None   ,       compute_metrics=lowerCAmelCase__   ,       tokenizer=lowerCAmelCase__   ,       )

  # Training
  if training_args.do_train:
    lowerCAmelCase_       :    int								=			None
    if training_args.resume_from_checkpoint is not None:
      lowerCAmelCase_       :    Any								=			training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowerCAmelCase_       :    Dict								=			last_checkpoint
    lowerCAmelCase_       :    str								=			trainer.train(resume_from_checkpoint=lowerCAmelCase__						)
    trainer.save_model()
    trainer.log_metrics('train'   ,       train_result.metrics						)
    trainer.save_metrics('train'   ,       train_result.metrics						)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    lowerCAmelCase_       :    Optional[int]								=			trainer.evaluate()
    trainer.log_metrics('eval'   ,       lowerCAmelCase__						)
    trainer.save_metrics('eval'   ,       lowerCAmelCase__						)

  # Write model card and (optionally) push to hub
  lowerCAmelCase_       :    List[str]								=			{
      'finetuned_from': model_args.model_name_or_path,
      'tasks': 'audio-classification',
      'dataset': data_args.dataset_name,
      'tags': ['audio-classification'],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**lowerCAmelCase__						)
  else:
    trainer.create_model_card(**lowerCAmelCase__						)


if __name__ == "__main__":
     main()










"""simple docstring"""







import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
     import tensorflow as tf
     from tensorflow.python.framework.errors_impl import ResourceExhaustedError

     from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
     import pyanvml.pyanvml as nvml

lowercase__		:    str				   =     logging.get_logger(__name__)

def     UpperCamelCase_   (   lowerCAmelCase__				:    bool   ,       lowerCAmelCase__				:    bool						)						->       List[Any]:

  """simple docstring"""




  def run_func(lowerCAmelCase__				:    int						):
    @wraps(lowerCAmelCase__						)
    def run_in_eager_mode(*lowerCAmelCase__				:    List[Any]   ,       **lowerCAmelCase__				:    int						):
      return func(*lowerCAmelCase__   ,       **lowerCAmelCase__						)

    @wraps(lowerCAmelCase__						)
    @tf.function(experimental_compile=lowerCAmelCase__						)
    def run_in_graph_mode(*lowerCAmelCase__				:    Dict   ,       **lowerCAmelCase__				:    Any						):
      return func(*lowerCAmelCase__   ,       **lowerCAmelCase__						)

    if do_eager_mode is True:
      if use_xla is not False:
        raise ValueError(
            'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'						)
      return run_in_eager_mode
    else:
      return run_in_graph_mode

  return run_func

def     UpperCamelCase_   (   lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int						)						->       ["tf.Tensor"]:

  """simple docstring"""




  lowerCAmelCase_       :    Dict								=			random.Random()
  lowerCAmelCase_       :    List[str]								=			[rng.randint(0   ,       vocab_size - 1						) for i in range(batch_size * sequence_length						)]
  return tf.constant(lowerCAmelCase__   ,       shape=(batch_size, sequence_length)   ,       dtype=tf.intaa						)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					42
      _SCREAMING_SNAKE_CASE      =					42
      _SCREAMING_SNAKE_CASE      =					"TensorFlow"



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        return tf.__version__



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        # initialize GPU on separate process
        lowerCAmelCase_       :    List[Any]								=			self.args.strategy
        if strategy is None:
          raise ValueError('A device strategy has to be initialized before using TensorFlow.'	)
        lowerCAmelCase_       :    List[str]								=			self._prepare_inference_func(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        return self._measure_speed(_inference	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        lowerCAmelCase_       :    Optional[int]								=			self.args.strategy
        if strategy is None:
          raise ValueError('A device strategy has to be initialized before using TensorFlow.'	)
        lowerCAmelCase_       :    Any								=			self._prepare_train_func(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        return self._measure_speed(_train	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        # initialize GPU on separate process
        if self.args.is_gpu:
          tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Any								=			self.args.strategy
        if strategy is None:
          raise ValueError('A device strategy has to be initialized before using TensorFlow.'	)
        lowerCAmelCase_       :    Optional[Any]								=			self._prepare_inference_func(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        return self._measure_memory(_inference	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        if self.args.is_gpu:
          tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Any								=			self.args.strategy
        if strategy is None:
          raise ValueError('A device strategy has to be initialized before using TensorFlow.'	)

        lowerCAmelCase_       :    Optional[int]								=			self._prepare_train_func(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
        return self._measure_memory(_train	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        lowerCAmelCase_       :    Any								=			self.config_dict[model_name]

        if self.args.fpaa:
          raise NotImplementedError('Mixed precision is currently not supported.'	)

        lowerCAmelCase_       :    Union[str, Any]								=			(
            hasattr(SCREAMING_SNAKE_CASE_   ,      'architectures'	)
            and isinstance(config.architectures   ,      SCREAMING_SNAKE_CASE_	)
            and len(config.architectures	) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
            lowerCAmelCase_       :    Any								=			'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
            lowerCAmelCase_       :    Any								=			__import__('transformers'   ,      fromlist=[model_class]	)
            lowerCAmelCase_       :    Dict								=			getattr(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
            lowerCAmelCase_       :    List[str]								=			model_cls(SCREAMING_SNAKE_CASE_	)
          except ImportError:
            raise ImportError(
                F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'	)
        else:
          lowerCAmelCase_       :    str								=			TF_MODEL_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_	)

        # encoder-decoder has vocab size saved differently
        lowerCAmelCase_       :    List[Any]								=			config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_   ,      'vocab_size'	) else config.encoder.vocab_size
        lowerCAmelCase_       :    Tuple								=			random_input_ids(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        @run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla	)
        def encoder_decoder_forward():
          return model(SCREAMING_SNAKE_CASE_   ,      decoder_input_ids=SCREAMING_SNAKE_CASE_   ,      training=SCREAMING_SNAKE_CASE_	)

        @run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla	)
        def encoder_forward():
          return model(SCREAMING_SNAKE_CASE_   ,      training=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

        return _inference



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				int	):
        lowerCAmelCase_       :    Union[str, Any]								=			self.config_dict[model_name]

        if self.args.eager_mode is not False:
          raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'	)

        if self.args.fpaa:
          raise NotImplementedError('Mixed precision is currently not supported.'	)

        lowerCAmelCase_       :    Dict								=			(
            hasattr(SCREAMING_SNAKE_CASE_   ,      'architectures'	)
            and isinstance(config.architectures   ,      SCREAMING_SNAKE_CASE_	)
            and len(config.architectures	) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
            lowerCAmelCase_       :    Optional[Any]								=			'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
            lowerCAmelCase_       :    int								=			__import__('transformers'   ,      fromlist=[model_class]	)
            lowerCAmelCase_       :    Any								=			getattr(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)
            lowerCAmelCase_       :    Optional[Any]								=			model_cls(SCREAMING_SNAKE_CASE_	)
          except ImportError:
            raise ImportError(
                F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
                ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'	)
        else:
          lowerCAmelCase_       :    Union[str, Any]								=			TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_	)

        # encoder-decoder has vocab size saved differently
        lowerCAmelCase_       :    int								=			config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_   ,      'vocab_size'	) else config.encoder.vocab_size
        lowerCAmelCase_       :    Optional[Any]								=			random_input_ids(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

        @run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla	)
        def encoder_decoder_train():
          lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      decoder_input_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      training=SCREAMING_SNAKE_CASE_	)[0]
          lowerCAmelCase_       :    Optional[int]								=			tf.gradients(SCREAMING_SNAKE_CASE_   ,      model.trainable_variables	)
          return gradients

        @run_with_tf_optimizations(self.args.eager_mode   ,      self.args.use_xla	)
        def encoder_train():
          lowerCAmelCase_       :    Dict								=			model(SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      training=SCREAMING_SNAKE_CASE_	)[0]
          lowerCAmelCase_       :    str								=			tf.gradients(SCREAMING_SNAKE_CASE_   ,      model.trainable_variables	)
          return gradients

        lowerCAmelCase_       :    int								=			encoder_decoder_train if config.is_encoder_decoder else encoder_train

        return _train



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        with self.args.strategy.scope():
          try:
            if self.args.is_tpu or self.args.use_xla:
              # run additional 10 times to stabilize compilation for tpu
              logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'	)
              timeit.repeat(SCREAMING_SNAKE_CASE_   ,      repeat=1   ,      number=5	)

            # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
            lowerCAmelCase_       :    Dict								=			timeit.repeat(
                SCREAMING_SNAKE_CASE_   ,      repeat=self.args.repeat   ,      number=1_0   ,      )

            return min(SCREAMING_SNAKE_CASE_	) / 10.0
          except ResourceExhaustedError as e:
            self.print_fn(F"Doesn't fit on GPU. {e}"	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Callable[[], None]	):
        logger.info(
            'Note that TensorFlow allocates more memory than '
            'it might need to speed up computation. '
            'The memory reported here corresponds to the memory '
            'reported by `nvidia-smi`, which can vary depending '
            'on total available memory on the GPU that is used.'	)
        with self.args.strategy.scope():
          try:
            if self.args.trace_memory_line_by_line:
              if not self.args.eager_mode:
                raise ValueError(
                    '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
                    ' consumption line by line.'	)
              lowerCAmelCase_       :    Union[str, Any]								=			start_memory_tracing('transformers'	)

            if self.args.is_tpu:
              # tpu
              raise NotImplementedError(
                  'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
                  ' with `args.memory=False`'	)
            elif self.args.is_gpu:
              # gpu
              if not is_pyanvml_available():
                logger.warning(
                    'py3nvml not installed, we won\'t log GPU memory usage. '
                    'Install py3nvml (pip install py3nvml) to log information about GPU.'	)
                lowerCAmelCase_       :    Tuple								=			'N/A'
              else:
                logger.info(
                    'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
                    ' running on the same GPU.'	)
                # init nvml
                nvml.nvmlInit()
                func()
                lowerCAmelCase_       :    int								=			nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
                lowerCAmelCase_       :    Union[str, Any]								=			nvml.nvmlDeviceGetMemoryInfo(SCREAMING_SNAKE_CASE_	)
                lowerCAmelCase_       :    List[Any]								=			meminfo.used
                lowerCAmelCase_       :    int								=			Memory(SCREAMING_SNAKE_CASE_	)
                # shutdown nvml
                nvml.nvmlShutdown()
            else:
              # cpu
              if self.args.trace_memory_line_by_line:
                logger.info(
                    'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
                    ' TensorFlow.'	)
                lowerCAmelCase_       :    Optional[int]								=			None
              else:
                lowerCAmelCase_       :    Union[str, Any]								=			measure_peak_memory_cpu(SCREAMING_SNAKE_CASE_	)
                lowerCAmelCase_       :    Any								=			Memory(SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) else memory_bytes
            if self.args.trace_memory_line_by_line:
              lowerCAmelCase_       :    List[Any]								=			stop_memory_tracing(SCREAMING_SNAKE_CASE_	)
              if memory is None:
                lowerCAmelCase_       :    Union[str, Any]								=			summary.total
            else:
              lowerCAmelCase_       :    List[str]								=			None

            return memory, summary
          except ResourceExhaustedError as e:
            self.print_fn(F"Doesn't fit on GPU. {e}"	)
            return "N/A", None





