





'''simple docstring'''

import math

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """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 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-audio'''


    def __init__(		self ,		A=32 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=0.1 ,		A=0.0 ,		A=0.1 ,		A=0.1 ,		A=0.02 ,		A=1e-5 ,		A="gelu" ,		A=(512, 512, 512, 512, 512, 512, 512) ,		A=(5, 2, 2, 2, 2, 2, 2) ,		A=(10, 3, 3, 3, 3, 2, 2) ,		A=False ,		A=16 ,		A=19 ,		A=5 ,		A=0.05 ,		A=10 ,		A=2 ,		A=0.0 ,		A=10 ,		A=0 ,		A="sum" ,		A=False ,		A=False ,		A=256 ,		A=(512, 512, 512, 512, 1500) ,		A=(5, 3, 3, 1, 1) ,		A=(1, 2, 3, 1, 1) ,		A=512 ,		A=0 ,		A=1 ,		A=2 ,		A=False ,		A=3 ,		A=2 ,		A=3 ,		A=None ,		**A ,		)		->   Dict:
        super().__init__(**A ,		pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			feat_extract_activation
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			conv_bias
        _SCREAMING_SNAKE_CASE									=			num_conv_pos_embeddings
        _SCREAMING_SNAKE_CASE									=			num_conv_pos_embedding_groups
        _SCREAMING_SNAKE_CASE									=			conv_pos_kernel_size
        _SCREAMING_SNAKE_CASE									=			len(self.conv_dim       )
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_dropout
        _SCREAMING_SNAKE_CASE									=			attention_dropout
        _SCREAMING_SNAKE_CASE									=			activation_dropout
        _SCREAMING_SNAKE_CASE									=			feat_proj_dropout
        _SCREAMING_SNAKE_CASE									=			final_dropout
        _SCREAMING_SNAKE_CASE									=			layerdrop
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			mask_time_prob
        _SCREAMING_SNAKE_CASE									=			mask_time_length
        _SCREAMING_SNAKE_CASE									=			mask_time_min_masks
        _SCREAMING_SNAKE_CASE									=			mask_feature_prob
        _SCREAMING_SNAKE_CASE									=			mask_feature_length
        _SCREAMING_SNAKE_CASE									=			mask_feature_min_masks

        # ctc loss
        _SCREAMING_SNAKE_CASE									=			ctc_loss_reduction
        _SCREAMING_SNAKE_CASE									=			ctc_zero_infinity

        # adapter
        _SCREAMING_SNAKE_CASE									=			add_adapter
        _SCREAMING_SNAKE_CASE									=			adapter_kernel_size
        _SCREAMING_SNAKE_CASE									=			adapter_stride
        _SCREAMING_SNAKE_CASE									=			num_adapter_layers
        _SCREAMING_SNAKE_CASE									=			output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        _SCREAMING_SNAKE_CASE									=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			xvector_output_dim





    @property
    def 			snake_case_(		self       )		->   str:
        return math.prod(self.conv_stride       )










'''simple docstring'''

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_					= logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=30_522, type=int)
      lowercase_					= parser.parse_args()

      logger.info(f"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase_					= pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase_					= Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase_					= [0] * args.vocab_size
      for k, v in counter.items():
            lowercase_					= v

      logger.info(f"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



