






import math

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


lowerCAmelCase			:							Union[str, Any]     =logging.get_logger(__name__)

lowerCAmelCase			:							int     ={
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'data2vec-audio'



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any]=32    ,  _UpperCamelCase     :				Optional[Any]=768    ,  _UpperCamelCase     :				List[str]=12    ,  _UpperCamelCase     :				Optional[int]=12    ,  _UpperCamelCase     :				List[Any]=3072    ,  _UpperCamelCase     :				Union[str, Any]="gelu"    ,  _UpperCamelCase     :				str=0.1    ,  _UpperCamelCase     :				Dict=0.1    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				List[str]=0.0    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				List[str]=0.1    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				int=1E-5    ,  _UpperCamelCase     :				str="gelu"    ,  _UpperCamelCase     :				List[str]=(512, 512, 512, 512, 512, 512, 512)    ,  _UpperCamelCase     :				Any=(5, 2, 2, 2, 2, 2, 2)    ,  _UpperCamelCase     :				Dict=(10, 3, 3, 3, 3, 2, 2)    ,  _UpperCamelCase     :				List[Any]=False    ,  _UpperCamelCase     :				Optional[int]=16    ,  _UpperCamelCase     :				List[str]=19    ,  _UpperCamelCase     :				List[Any]=5    ,  _UpperCamelCase     :				str=0.0_5    ,  _UpperCamelCase     :				Union[str, Any]=10    ,  _UpperCamelCase     :				Dict=2    ,  _UpperCamelCase     :				Union[str, Any]=0.0    ,  _UpperCamelCase     :				str=10    ,  _UpperCamelCase     :				Any=0    ,  _UpperCamelCase     :				Any="sum"    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				List[Any]=False    ,  _UpperCamelCase     :				List[Any]=256    ,  _UpperCamelCase     :				List[str]=(512, 512, 512, 512, 1500)    ,  _UpperCamelCase     :				Optional[Any]=(5, 3, 3, 1, 1)    ,  _UpperCamelCase     :				Optional[int]=(1, 2, 3, 1, 1)    ,  _UpperCamelCase     :				List[str]=512    ,  _UpperCamelCase     :				Tuple=0    ,  _UpperCamelCase     :				int=1    ,  _UpperCamelCase     :				Optional[int]=2    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				Optional[Any]=3    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				int=3    ,  _UpperCamelCase     :				str=None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase    ,  pad_token_id=_UpperCamelCase    ,  bos_token_id=_UpperCamelCase    ,  eos_token_id=_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						hidden_size
          _lowerCamelCase     :		List[str] 						=						feat_extract_activation
          _lowerCamelCase     :		Union[str, Any] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		int 						=						conv_bias
          _lowerCamelCase     :		Union[str, Any] 						=						num_conv_pos_embeddings
          _lowerCamelCase     :		List[str] 						=						num_conv_pos_embedding_groups
          _lowerCamelCase     :		int 						=						conv_pos_kernel_size
          _lowerCamelCase     :		Dict 						=						len(self.conv_dim)
          _lowerCamelCase     :		List[str] 						=						num_hidden_layers
          _lowerCamelCase     :		Any 						=						intermediate_size
          _lowerCamelCase     :		Union[str, Any] 						=						hidden_act
          _lowerCamelCase     :		List[Any] 						=						num_attention_heads
          _lowerCamelCase     :		Optional[int] 						=						hidden_dropout
          _lowerCamelCase     :		List[str] 						=						attention_dropout
          _lowerCamelCase     :		Dict 						=						activation_dropout
          _lowerCamelCase     :		Tuple 						=						feat_proj_dropout
          _lowerCamelCase     :		Optional[Any] 						=						final_dropout
          _lowerCamelCase     :		str 						=						layerdrop
          _lowerCamelCase     :		Tuple 						=						layer_norm_eps
          _lowerCamelCase     :		Any 						=						initializer_range
          _lowerCamelCase     :		Union[str, Any] 						=						vocab_size
          _lowerCamelCase     :		Dict 						=						use_weighted_layer_sum

          if (
              (len(self.conv_stride) != self.num_feat_extract_layers)
              or (len(self.conv_kernel) != self.num_feat_extract_layers)
              or (len(self.conv_dim) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                  """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                  F""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                  F""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          _lowerCamelCase     :		Optional[Any] 						=						mask_time_prob
          _lowerCamelCase     :		Any 						=						mask_time_length
          _lowerCamelCase     :		str 						=						mask_time_min_masks
          _lowerCamelCase     :		Optional[int] 						=						mask_feature_prob
          _lowerCamelCase     :		str 						=						mask_feature_length
          _lowerCamelCase     :		Dict 						=						mask_feature_min_masks

          # ctc loss
          _lowerCamelCase     :		int 						=						ctc_loss_reduction
          _lowerCamelCase     :		Union[str, Any] 						=						ctc_zero_infinity

          # adapter
          _lowerCamelCase     :		List[str] 						=						add_adapter
          _lowerCamelCase     :		Dict 						=						adapter_kernel_size
          _lowerCamelCase     :		str 						=						adapter_stride
          _lowerCamelCase     :		Union[str, Any] 						=						num_adapter_layers
          _lowerCamelCase     :		Optional[int] 						=						output_hidden_size or hidden_size

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          _lowerCamelCase     :		List[Any] 						=						classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          _lowerCamelCase     :		List[str] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						list(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						xvector_output_dim




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Tuple:


          """simple docstring"""


          return math.prod(self.conv_stride)





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        "repo_name",
        "path",
        "copies",
        "size",
        "content",
        "license",
        "hash",
        "line_mean",
        "line_max",
        "alpha_frac",
        "autogenerated",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Tuple     =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")




