



import math

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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''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_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""data2vec-audio"""



       def __init__(     self     ,lowercase=32     ,lowercase=768     ,lowercase=12     ,lowercase=12     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=0.0     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=0.02     ,lowercase=1E-5     ,lowercase="gelu"     ,lowercase=(512, 512, 512, 512, 512, 512, 512)     ,lowercase=(5, 2, 2, 2, 2, 2, 2)     ,lowercase=(10, 3, 3, 3, 3, 2, 2)     ,lowercase=False     ,lowercase=16     ,lowercase=19     ,lowercase=5     ,lowercase=0.05     ,lowercase=10     ,lowercase=2     ,lowercase=0.0     ,lowercase=10     ,lowercase=0     ,lowercase="sum"     ,lowercase=False     ,lowercase=False     ,lowercase=256     ,lowercase=(512, 512, 512, 512, 1500)     ,lowercase=(5, 3, 3, 1, 1)     ,lowercase=(1, 2, 3, 1, 1)     ,lowercase=512     ,lowercase=0     ,lowercase=1     ,lowercase=2     ,lowercase=False     ,lowercase=3     ,lowercase=2     ,lowercase=3     ,lowercase=None     ,**lowercase     ,):

             """simple docstring"""





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

             # ctc loss
             UpperCAmelCase_  :	int				      =		ctc_loss_reduction
             UpperCAmelCase_  :	str				      =		ctc_zero_infinity

             # adapter
             UpperCAmelCase_  :	Optional[Any]				      =		add_adapter
             UpperCAmelCase_  :	Tuple				      =		adapter_kernel_size
             UpperCAmelCase_  :	Optional[Any]				      =		adapter_stride
             UpperCAmelCase_  :	List[str]				      =		num_adapter_layers
             UpperCAmelCase_  :	Optional[int]				      =		output_hidden_size or hidden_size

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

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



       @property
       def  A_   (     self):

             """simple docstring"""





             return math.prod(self.conv_stride)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase         =			{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase         =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
