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+# coding=utf-8
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Hubert model configuration"""
+
+import functools
+import operator
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class HubertConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
+    Hubert model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Hubert
+    [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32):
+            Vocabulary size of the Hubert model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`HubertModel`]. Vocabulary size of the model. Defines the different
+            tokens that can be represented by the *inputs_ids* passed to the forward method of [`HubertModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout(`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        activation_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for activations inside the fully connected layer.
+        attention_dropout(`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        final_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for the final projection layer of [`Wav2Vec2ForCTC`].
+        layerdrop (`float`, *optional*, defaults to 0.1):
+            The LayerDrop probability. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more
+            details.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        feat_extract_norm (`str`, *optional*, defaults to `"group"`):
+            The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group
+            normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D
+            convolutional layers.
+        feat_proj_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for output of the feature encoder.
+        feat_proj_layer_norm (`bool`, *optional*, defaults to `True`):
+            Whether to apply LayerNorm to the output of the feature encoder.
+        feat_extract_activation (`str, `optional`, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the 1D convolutional layers of the feature
+            extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        conv_dim (`tuple[int]`, *optional*, defaults to `(512, 512, 512, 512, 512, 512, 512)`):
+            A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
+            feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers.
+        conv_stride (`tuple[int]`, *optional*, defaults to `(5, 2, 2, 2, 2, 2, 2)`):
+            A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length
+            of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*.
+        conv_kernel (`tuple[int]`, *optional*, defaults to `(10, 3, 3, 3, 3, 3, 3)`):
+            A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The
+            length of *conv_kernel* defines the number of convolutional layers and has to match the length of
+            *conv_dim*.
+        conv_bias (`bool`, *optional*, defaults to `False`):
+            Whether the 1D convolutional layers have a bias.
+        num_conv_pos_embeddings (`int`, *optional*, defaults to 128):
+            Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
+            embeddings layer.
+        num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16):
+            Number of groups of 1D convolutional positional embeddings layer.
+        conv_pos_batch_norm (`bool`, *optional*, defaults to `False`):
+            Whether to use batch norm instead of weight norm in conv_pos
+        do_stable_layer_norm (`bool`, *optional*, defaults to `False`):
+            Whether do apply *stable* layer norm architecture of the Transformer encoder. `do_stable_layer_norm is
+            True` corresponds to applying layer norm before the attention layer, whereas `do_stable_layer_norm is
+            False` corresponds to applying layer norm after the attention layer.
+        apply_spec_augment (`bool`, *optional*, defaults to `True`):
+            Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
+            [SpecAugment: A Simple Data Augmentation Method for Automatic Speech
+            Recognition](https://huggingface.co/papers/1904.08779).
+        mask_time_prob (`float`, *optional*, defaults to 0.05):
+            Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
+            procedure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If
+            reasoning from the probability of each feature vector to be chosen as the start of the vector span to be
+            masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
+            actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`.
+        mask_time_length (`int`, *optional*, defaults to 10):
+            Length of vector span along the time axis.
+        mask_time_min_masks (`int`, *optional*, defaults to 2),:
+            The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
+            irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
+            mask_time_min_masks''
+        mask_feature_prob (`float`, *optional*, defaults to 0.0):
+            Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
+            masking procedure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over
+            the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector
+            span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
+            may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
+            True`.
+        mask_feature_length (`int`, *optional*, defaults to 10):
+            Length of vector span along the feature axis.
+        mask_feature_min_masks (`int`, *optional*, defaults to 0),:
+            The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
+            step, irrespectively of `mask_feature_prob`. Only relevant if
+            ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks''
+        ctc_loss_reduction (`str`, *optional*, defaults to `"sum"`):
+            Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an
+            instance of [`HubertForCTC`].
+        ctc_zero_infinity (`bool`, *optional*, defaults to `False`):
+            Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly
+            occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance
+            of [`HubertForCTC`].
+        use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
+            Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
+            instance of [`HubertForSequenceClassification`].
+        classifier_proj_size (`int`, *optional*, defaults to 256):
+            Dimensionality of the projection before token mean-pooling for classification.
+
+    Example:
+
+    ```python
+    >>> from transformers import HubertModel, HubertConfig
+
+    >>> # Initializing a Hubert facebook/hubert-base-ls960 style configuration
+    >>> configuration = HubertConfig()
+
+    >>> # Initializing a model from the facebook/hubert-base-ls960 style configuration
+    >>> model = HubertModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "hubert"
+
+    def __init__(
+        self,
+        vocab_size=32,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout=0.1,
+        activation_dropout=0.1,
+        attention_dropout=0.1,
+        feat_proj_layer_norm=True,
+        feat_proj_dropout=0.0,
+        final_dropout=0.1,
+        layerdrop=0.1,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        feat_extract_norm="group",
+        feat_extract_activation="gelu",
+        conv_dim=(512, 512, 512, 512, 512, 512, 512),
+        conv_stride=(5, 2, 2, 2, 2, 2, 2),
+        conv_kernel=(10, 3, 3, 3, 3, 2, 2),
+        conv_bias=False,
+        num_conv_pos_embeddings=128,
+        num_conv_pos_embedding_groups=16,
+        conv_pos_batch_norm=False,
+        do_stable_layer_norm=False,
+        apply_spec_augment=True,
+        mask_time_prob=0.05,
+        mask_time_length=10,
+        mask_time_min_masks=2,
+        mask_feature_prob=0.0,
+        mask_feature_length=10,
+        mask_feature_min_masks=0,
+        ctc_loss_reduction="sum",
+        ctc_zero_infinity=False,
+        use_weighted_layer_sum=False,
+        classifier_proj_size=256,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs,
+    ):
+        super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
+        self.hidden_size = hidden_size
+        self.feat_extract_norm = feat_extract_norm
+        self.feat_extract_activation = feat_extract_activation
+        self.conv_dim = list(conv_dim)
+        self.conv_stride = list(conv_stride)
+        self.conv_kernel = list(conv_kernel)
+        self.conv_bias = conv_bias
+        self.num_conv_pos_embeddings = num_conv_pos_embeddings
+        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
+        self.conv_pos_batch_norm = conv_pos_batch_norm
+        self.num_feat_extract_layers = len(self.conv_dim)
+        self.num_hidden_layers = num_hidden_layers
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.num_attention_heads = num_attention_heads
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.feat_proj_layer_norm = feat_proj_layer_norm
+        self.feat_proj_dropout = feat_proj_dropout
+        self.final_dropout = final_dropout
+        self.layerdrop = layerdrop
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        self.vocab_size = vocab_size
+        self.do_stable_layer_norm = do_stable_layer_norm
+        self.use_weighted_layer_sum = use_weighted_layer_sum
+        self.classifier_proj_size = classifier_proj_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)`, 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://huggingface.co/papers/1904.08779
+        self.apply_spec_augment = apply_spec_augment
+        self.mask_time_prob = mask_time_prob
+        self.mask_time_length = mask_time_length
+        self.mask_time_min_masks = mask_time_min_masks
+        self.mask_feature_prob = mask_feature_prob
+        self.mask_feature_length = mask_feature_length
+        self.mask_feature_min_masks = mask_feature_min_masks
+
+        # ctc loss
+        self.ctc_loss_reduction = ctc_loss_reduction
+        self.ctc_zero_infinity = ctc_zero_infinity
+
+    @property
+    def inputs_to_logits_ratio(self):
+        return functools.reduce(operator.mul, self.conv_stride, 1)
+
+
+__all__ = ["HubertConfig"]