


"""simple docstring"""






import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
  from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image




class 							A_      :


  def __init__(			self:						Dict							,__lowerCAmelCase:						List[Any]							,__lowerCAmelCase:						Tuple=3							,__lowerCAmelCase:						Union[str, Any]=32							,__lowerCAmelCase:						Optional[Any]=3							,__lowerCAmelCase:						str=10							,__lowerCAmelCase:						Optional[int]=[8, 16, 32, 64]							,__lowerCAmelCase:						Dict=[1, 1, 2, 1]							,__lowerCAmelCase:						List[Any]=True							,__lowerCAmelCase:						Union[str, Any]=True							,__lowerCAmelCase:						int="relu"							,__lowerCAmelCase:						Any=3							,__lowerCAmelCase:						Optional[Any]=None							,__lowerCAmelCase:						Dict=["stage2", "stage3", "stage4"]							,__lowerCAmelCase:						List[Any]=[2, 3, 4]							,__lowerCAmelCase:						Any=1							,):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     parent
     _lowerCamelCase   :				str							=     batch_size
     _lowerCamelCase   :				Any							=     image_size
     _lowerCamelCase   :				Dict							=     num_channels
     _lowerCamelCase   :				Any							=     embeddings_size
     _lowerCamelCase   :				str							=     hidden_sizes
     _lowerCamelCase   :				Optional[int]							=     depths
     _lowerCamelCase   :				Tuple							=     is_training
     _lowerCamelCase   :				Any							=     use_labels
     _lowerCamelCase   :				Dict							=     hidden_act
     _lowerCamelCase   :				Any							=     num_labels
     _lowerCamelCase   :				Dict							=     scope
     _lowerCamelCase   :				List[Any]							=     len(__lowerCAmelCase    )
     _lowerCamelCase   :				Optional[int]							=     out_features
     _lowerCamelCase   :				Any							=     out_indices
     _lowerCamelCase   :				List[str]							=     num_groups


  def 							_lowercase					(			self:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     _lowerCamelCase   :				int							=     None
     if self.use_labels:
        _lowerCamelCase   :				Optional[Any]							=     ids_tensor([self.batch_size]							,self.num_labels    )

     _lowerCamelCase   :				Union[str, Any]							=     self.get_config()

     return config, pixel_values, labels


  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     return BitConfig(
         num_channels=self.num_channels							,embeddings_size=self.embeddings_size							,hidden_sizes=self.hidden_sizes							,depths=self.depths							,hidden_act=self.hidden_act							,num_labels=self.num_labels							,out_features=self.out_features							,out_indices=self.out_indices							,num_groups=self.num_groups							,)


  def 							_lowercase					(			self:						Optional[int]							,__lowerCAmelCase:						Dict							,__lowerCAmelCase:						Optional[Any]							,__lowerCAmelCase:						Any    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     BitModel(config=__lowerCAmelCase    )
     model.to(__lowerCAmelCase    )
     model.eval()
     _lowerCamelCase   :				Optional[int]							=     model(__lowerCAmelCase    )
     self.parent.assertEqual(
         result.last_hidden_state.shape							,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,)


  def 							_lowercase					(			self:						List[Any]							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						Dict							,__lowerCAmelCase:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     self.num_labels
     _lowerCamelCase   :				List[str]							=     BitForImageClassification(__lowerCAmelCase    )
     model.to(__lowerCAmelCase    )
     model.eval()
     _lowerCamelCase   :				Tuple							=     model(__lowerCAmelCase							,labels=__lowerCAmelCase    )
     self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)    )


  def 							_lowercase					(			self:						Any							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						Optional[int]							,__lowerCAmelCase:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     BitBackbone(config=__lowerCAmelCase    )
     model.to(__lowerCAmelCase    )
     model.eval()
     _lowerCamelCase   :				str							=     model(__lowerCAmelCase    )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps    )							,len(config.out_features    )    )
     self.parent.assertListEqual(list(result.feature_maps[0].shape    )							,[self.batch_size, self.hidden_sizes[1], 4, 4]    )

     # verify channels
     self.parent.assertEqual(len(model.channels    )							,len(config.out_features    )    )
     self.parent.assertListEqual(model.channels							,config.hidden_sizes[1:]    )

     # verify backbone works with out_features=None
     _lowerCamelCase   :				List[str]							=     None
     _lowerCamelCase   :				str							=     BitBackbone(config=__lowerCAmelCase    )
     model.to(__lowerCAmelCase    )
     model.eval()
     _lowerCamelCase   :				int							=     model(__lowerCAmelCase    )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps    )							,1    )
     self.parent.assertListEqual(list(result.feature_maps[0].shape    )							,[self.batch_size, self.hidden_sizes[-1], 1, 1]    )

     # verify channels
     self.parent.assertEqual(len(model.channels    )							,1    )
     self.parent.assertListEqual(model.channels							,[config.hidden_sizes[-1]]    )






  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     self.prepare_config_and_inputs()
     _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				List[str]							=     config_and_inputs
     _lowerCamelCase   :				Optional[Any]							=     {"pixel_values": pixel_values}
     return config, inputs_dict




@require_torch
class 							A_      (       _a     ,	_a     ,	unittest.TestCase ):
  lowerCAmelCase__      			=	(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  lowerCAmelCase__      			=	(
      {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCAmelCase__      			=	False
  lowerCAmelCase__      			=	False
  lowerCAmelCase__      			=	False
  lowerCAmelCase__      			=	False
  lowerCAmelCase__      			=	False


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     BitModelTester(self    )
     _lowerCamelCase   :				List[str]							=     ConfigTester(self							,config_class=__lowerCAmelCase							,has_text_modality=__lowerCAmelCase    )


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()


  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     return


  @unittest.skip(reason="Bit does not output attentions"    )
  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     pass


  @unittest.skip(reason="Bit does not use inputs_embeds"    )
  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     pass


  @unittest.skip(reason="Bit does not support input and output embeddings"    )
  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     pass


  def 							_lowercase					(			self:						Tuple    ):







     '''simple docstring'''







     _lowerCamelCase,					_lowerCamelCase   :				Any							=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        _lowerCamelCase   :				Tuple							=     model_class(__lowerCAmelCase    )
        _lowerCamelCase   :				List[str]							=     inspect.signature(model.forward    )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        _lowerCamelCase   :				str							=     [*signature.parameters.keys()]

        _lowerCamelCase   :				Union[str, Any]							=     ["pixel_values"]
        self.assertListEqual(arg_names[:1]							,__lowerCAmelCase    )


  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCAmelCase    )


  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*__lowerCAmelCase    )


  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase,					_lowerCamelCase   :				Tuple							=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        _lowerCamelCase   :				Any							=     model_class(config=__lowerCAmelCase    )
        for name, module in model.named_modules():
           if isinstance(__lowerCAmelCase							,(nn.BatchNormad, nn.GroupNorm)    ):
              self.assertTrue(
                  torch.all(module.weight == 1    )							,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,)
              self.assertTrue(
                  torch.all(module.bias == 0    )							,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							,)


  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     def check_hidden_states_output(__lowerCAmelCase:						Any							,__lowerCAmelCase:						Dict							,__lowerCAmelCase:						Dict    ):
        _lowerCamelCase   :				str							=     model_class(__lowerCAmelCase    )
        model.to(__lowerCAmelCase    )
        model.eval()

        with torch.no_grad():
           _lowerCamelCase   :				str							=     model(**self._prepare_for_class(__lowerCAmelCase							,__lowerCAmelCase    )    )

        _lowerCamelCase   :				str							=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        _lowerCamelCase   :				int							=     self.model_tester.num_stages
        self.assertEqual(len(__lowerCAmelCase    )							,expected_num_stages + 1    )

        # Bit's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]    )							,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,)

     _lowerCamelCase,					_lowerCamelCase   :				List[str]							=     self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCamelCase   :				Any							=     ["preactivation", "bottleneck"]
     for model_class in self.all_model_classes:
        for layer_type in layers_type:
           _lowerCamelCase   :				int							=     layer_type
           _lowerCamelCase   :				Tuple							=     True
           check_hidden_states_output(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowerCamelCase   :				Optional[int]							=     True

           check_hidden_states_output(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase    )


  @unittest.skip(reason="Bit does not use feedforward chunking"    )
  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     pass


  def 							_lowercase					(			self:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase    )






  @slow
  def 							_lowercase					(			self:						str    ):







     '''simple docstring'''







     for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _lowerCamelCase   :				Any							=     BitModel.from_pretrained(__lowerCAmelCase    )
        self.assertIsNotNone(__lowerCAmelCase    )







def        lowerCamelCase_(		)    ->     Tuple:
   '''simple docstring'''





   _lowerCamelCase   :				str							=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
   return image




@require_torch
@require_vision
class 							A_      (       unittest.TestCase ):


  @cached_property
  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     return (
         BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ) if is_vision_available() else None
     )






  @slow
  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(__lowerCAmelCase    )

     _lowerCamelCase   :				List[str]							=     self.default_image_processor
     _lowerCamelCase   :				Dict							=     prepare_img()
     _lowerCamelCase   :				Union[str, Any]							=     image_processor(images=__lowerCAmelCase							,return_tensors="pt"    ).to(__lowerCAmelCase    )

     # forward pass
     with torch.no_grad():
        _lowerCamelCase   :				int							=     model(**__lowerCAmelCase    )

     # verify the logits
     _lowerCamelCase   :				List[Any]							=     torch.Size((1, 1_000)    )
     self.assertEqual(outputs.logits.shape							,__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]    ).to(__lowerCAmelCase    )

     self.assertTrue(torch.allclose(outputs.logits[0, :3]							,__lowerCAmelCase							,atol=1e-4    )    )




@require_torch
class 							A_      (       _a     ,	unittest.TestCase ):
  lowerCAmelCase__      			=	(BitBackbone,) if is_torch_available() else ()
  lowerCAmelCase__      			=	BitConfig

  lowerCAmelCase__      			=	False


  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     BitModelTester(self    )






import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
 from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
 _lowerCAmelCase				:   Optional[Any]						       =						True
 from torch.cuda.amp import autocast


_lowerCAmelCase				:   Dict						       =						logging.getLogger(__name__)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}				)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether to log verbose messages or not.'''}     ,					)
     __UpperCamelCase            =  field(
         default=2.0     ,					metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}				)
     __UpperCamelCase            =  field(
         default=0.5     ,					metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}				)
     __UpperCamelCase            =  field(
         default=0.99_9995     ,					metadata={'''help''': '''Decay of gumbel temperature during training.'''}				)




def   __snake_case    (       _lowerCAmelCase			:			ModelArguments					,					_lowerCAmelCase			:			TrainingArguments						)			->    str:
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,					datefmt="%m/%d/%Y %H:%M:%S"					,					handlers=[logging.StreamHandler(sys.stdout						)]					,					)
   A_       :					Optional[int]        =      logging.WARNING
   if model_args.verbose_logging:
      A_       :					Dict        =      logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank						):
      A_       :					int        =      logging.INFO
   logger.setLevel(_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}				)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
     __UpperCamelCase            =  field(
         default='''train'''     ,					metadata={
             '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
         }     ,					)
     __UpperCamelCase            =  field(
         default='''validation'''     ,					metadata={
             '''help''': (
                 '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
             )
         }     ,					)
     __UpperCamelCase            =  field(
         default='''file'''     ,					metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}				)
     __UpperCamelCase            =  field(
         default=1     ,					metadata={
             '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
         }     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''The number of processes to use for the preprocessing.'''}     ,					)
     __UpperCamelCase            =  field(
         default=20.0     ,					metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}				)




@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  "longest"
     __UpperCamelCase            =  None
     __UpperCamelCase            =  None



     def __call__(				self							:Optional[Any]      ,   snake_case							:List[Dict[str, Union[List[int], torch.Tensor]]]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      self.feature_extractor.pad(
            snake_case      ,   max_length=self.max_length      ,   padding=self.padding      ,   pad_to_multiple_of=self.pad_to_multiple_of      ,   return_tensors="pt"      ,   )
        A_       :					int        =      self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]	)

        A_       :					List[str]        =      batch["input_values"].shape[0]

        # make sure that no loss is computed on padded inputs
        if batch["attention_mask"] is not None:
           # compute real output lengths according to convolution formula
           A_       :					List[str]        =      self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1	)	).to(
               torch.long	)

           A_       :					Union[str, Any]        =      torch.zeros(
               (batch_size, mask_indices_seq_length)      ,   dtype=torch.long      ,   device=batch["input_values"].device	)

           # these two operations makes sure that all values
           # before the output lengths indices are attended to
           A_       :					str        =      1
           A_       :					Union[str, Any]        =      attention_mask.flip([-1]	).cumsum(-1	).flip([-1]	).bool()

        # sample randomly masked indices
        A_       :					Optional[int]        =      _compute_mask_indices(
            (batch_size, mask_indices_seq_length)      ,   self.model.config.mask_time_prob      ,   self.model.config.mask_time_length      ,   attention_mask=snake_case      ,   min_masks=2      ,   )

        return batch




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     def __init__(				self							:int      ,   *snake_case							:Any      ,   snake_case							:Any=1      ,   snake_case							:Dict=0      ,   snake_case							:Dict=1.0      ,   **snake_case							:Any	):





        '''simple docstring'''




        super().__init__(*snake_case      ,   **snake_case	)
        A_       :					Union[str, Any]        =      0
        A_       :					Dict        =      max_gumbel_temp
        A_       :					Optional[int]        =      min_gumbel_temp
        A_       :					List[Any]        =      gumbel_temp_decay





     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:nn.Module      ,   snake_case							:Dict[str, Union[torch.Tensor, Any]]	):





        '''simple docstring'''




        model.train()
        A_       :					List[str]        =      self._prepare_inputs(snake_case	)

        if self.use_amp:
           with autocast():
              A_       :					List[str]        =      self.compute_loss(snake_case      ,   snake_case	)
        else:
           A_       :					str        =      self.compute_loss(snake_case      ,   snake_case	)

        if self.args.n_gpu > 1 or self.deepspeed:
           if model.module.config.ctc_loss_reduction == "mean":
              A_       :					Any        =      loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
              A_       :					Optional[Any]        =      loss.sum() / (inputs["mask_time_indices"]).sum()
           else:
              raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"	)

        if self.args.gradient_accumulation_steps > 1:
           A_       :					Any        =      loss / self.args.gradient_accumulation_steps

        if self.use_amp:
           self.scaler.scale(snake_case	).backward()
        elif self.use_apex:
           with amp.scale_loss(snake_case      ,   self.optimizer	) as scaled_loss:
              scaled_loss.backward()
        elif self.deepspeed:
           self.deepspeed.backward(snake_case	)
        else:
           loss.backward()

        self.num_update_step += 1
        # make sure gumbel softmax temperature is decayed
        if self.args.n_gpu > 1 or self.deepspeed:
           model.module.set_gumbel_temperature(
               max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,   self.min_gumbel_temp	)	)
        else:
           model.set_gumbel_temperature(
               max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,   self.min_gumbel_temp	)	)

        return loss.detach()




def   __snake_case    (       )			->    Tuple:
   # 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.

   A_       :					List[str]        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

   A_   ,    A_   ,    A_       :					Dict        =      parser.parse_args_into_dataclasses()
   configure_logger(_lowerCAmelCase					,					_lowerCAmelCase						)

   # Downloading and loading a dataset from the hub.
   A_       :					Optional[Any]        =      load_dataset(data_args.dataset_name					,					data_args.dataset_config_name					,					cache_dir=model_args.cache_dir						)

   if "validation" not in datasets.keys():
      # make sure only "validation" and "train" keys remain"
      A_       :					Tuple        =      DatasetDict()
      A_       :					Any        =      load_dataset(
          data_args.dataset_name					,					data_args.dataset_config_name					,					split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"					,					cache_dir=model_args.cache_dir					,					)
      A_       :					Union[str, Any]        =      load_dataset(
          data_args.dataset_name					,					data_args.dataset_config_name					,					split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"					,					cache_dir=model_args.cache_dir					,					)
   else:
      # make sure only "validation" and "train" keys remain"
      A_       :					Any        =      DatasetDict()
      A_       :					Optional[Any]        =      load_dataset(
          data_args.dataset_name					,					data_args.dataset_config_name					,					split="validation"					,					cache_dir=model_args.cache_dir					,					)
      A_       :					List[str]        =      load_dataset(
          data_args.dataset_name					,					data_args.dataset_config_name					,					split=f"{data_args.train_split_name}"					,					cache_dir=model_args.cache_dir					,					)

   # only normalized-inputs-training is supported
   A_       :					Union[str, Any]        =      WavaVecaFeatureExtractor.from_pretrained(
       model_args.model_name_or_path					,					cache_dir=model_args.cache_dir					,					do_normalize=_lowerCAmelCase						)

   def prepare_dataset(_lowerCAmelCase			:			str						):
      # check that all files have the correct sampling rate
      A_   ,    A_       :					Tuple        =      librosa.load(batch[data_args.speech_file_column]					,					sr=feature_extractor.sampling_rate						)
      return batch

   # load audio files into numpy arrays
   A_       :					List[Any]        =      datasets.map(
       _lowerCAmelCase					,					num_proc=data_args.preprocessing_num_workers					,					remove_columns=datasets["train"].column_names						)

   # filter audio files that are too long
   A_       :					str        =      vectorized_datasets.filter(
       lambda _lowerCAmelCase						: len(data["speech"]						) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate						)						)

   def normalize(_lowerCAmelCase			:			List[str]						):
      return feature_extractor(batch["speech"]					,					sampling_rate=feature_extractor.sampling_rate						)

   # normalize and transform to `BatchFeatures`
   A_       :					Any        =      vectorized_datasets.map(
       _lowerCAmelCase					,					batched=_lowerCAmelCase					,					num_proc=data_args.preprocessing_num_workers					,					load_from_cache_file=not data_args.overwrite_cache					,					remove_columns=vectorized_datasets["train"].column_names					,					)

   # pretraining is only supported for "newer" stable layer norm architecture
   # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
   A_       :					Any        =      WavaVecaConfig.from_pretrained(
       model_args.model_name_or_path					,					cache_dir=model_args.cache_dir					,					gradient_checkpointing=training_args.gradient_checkpointing					,					)

   if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
      raise ValueError(
          "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
          " ``config.feat_extract_norm='layer'"						)

   A_       :					int        =      WavaVecaForPreTraining(_lowerCAmelCase						)

   A_       :					str        =      DataCollatorForWavaVecaPretraining(model=_lowerCAmelCase					,					feature_extractor=_lowerCAmelCase						)

   A_       :					List[Any]        =      WavaVecaPreTrainer(
       model=_lowerCAmelCase					,					data_collator=_lowerCAmelCase					,					args=_lowerCAmelCase					,					train_dataset=vectorized_datasets["train"]					,					eval_dataset=vectorized_datasets["validation"]					,					tokenizer=_lowerCAmelCase					,					max_gumbel_temp=model_args.max_gumbel_temperature					,					min_gumbel_temp=model_args.min_gumbel_temperature					,					gumbel_temp_decay=model_args.gumbel_temperature_decay					,					)
   trainer.train()


if __name__ == "__main__":
 main()
