





'''simple docstring'''







import functools
import operator

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


_lowercase       						=			logging.get_logger(__name__)

_lowercase       						=			{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 			_lowercase						(       __UpperCAmelCase					):
     _UpperCAmelCase										=					"""sew"""







     def __init__( self    ,   A__=32    ,   A__=7_68    ,   A__=12    ,   A__=12    ,   A__=30_72    ,   A__=2    ,   A__="gelu"    ,   A__=0.1    ,   A__=0.1    ,   A__=0.1    ,   A__=0.0    ,   A__=0.1    ,   A__=0.1    ,   A__=0.0_2    ,   A__=1e-5    ,   A__="group"    ,   A__="gelu"    ,   A__=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)    ,   A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,   A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,   A__=False    ,   A__=1_28    ,   A__=16    ,   A__=True    ,   A__=0.0_5    ,   A__=10    ,   A__=2    ,   A__=0.0    ,   A__=10    ,   A__=0    ,   A__="mean"    ,   A__=False    ,   A__=False    ,   A__=2_56    ,   A__=0    ,   A__=1    ,   A__=2    ,   **A__    ,   ) ->	Dict:
      super().__init__(**UpperCAmelCase_    ,   pad_token_id=UpperCAmelCase_    ,   bos_token_id=UpperCAmelCase_    ,   eos_token_id=UpperCAmelCase_    )
      snake_case				=			hidden_size
      snake_case				=			feat_extract_norm
      snake_case				=			feat_extract_activation
      snake_case				=			list(UpperCAmelCase_    )
      snake_case				=			list(UpperCAmelCase_    )
      snake_case				=			list(UpperCAmelCase_    )
      snake_case				=			conv_bias
      snake_case				=			num_conv_pos_embeddings
      snake_case				=			num_conv_pos_embedding_groups
      snake_case				=			len(self.conv_dim    )
      snake_case				=			num_hidden_layers
      snake_case				=			intermediate_size
      snake_case				=			squeeze_factor
      snake_case				=			hidden_act
      snake_case				=			num_attention_heads
      snake_case				=			hidden_dropout
      snake_case				=			attention_dropout
      snake_case				=			activation_dropout
      snake_case				=			feat_proj_dropout
      snake_case				=			final_dropout
      snake_case				=			layerdrop
      snake_case				=			layer_norm_eps
      snake_case				=			initializer_range
      snake_case				=			vocab_size

      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)`,'''
           F"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
           F"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      snake_case				=			apply_spec_augment
      snake_case				=			mask_time_prob
      snake_case				=			mask_time_length
      snake_case				=			mask_time_min_masks
      snake_case				=			mask_feature_prob
      snake_case				=			mask_feature_length
      snake_case				=			mask_feature_min_masks

      # ctc loss
      snake_case				=			ctc_loss_reduction
      snake_case				=			ctc_zero_infinity

      # sequence classification
      snake_case				=			use_weighted_layer_sum
      snake_case				=			classifier_proj_size







     @property
     def     UpperCamelCase			( self    ) ->	List[str]:
      return functools.reduce(operator.mul    ,   self.conv_stride    ,   1    )








'''simple docstring'''







from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor

