376 lines
20 KiB
Python
376 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2025 The Nari Labs and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Dia model configuration"""
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from typing import Optional
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from ...configuration_utils import PretrainedConfig
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from ...modeling_rope_utils import rope_config_validation
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class DiaEncoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DiaEncoder`]. It is used to instantiate a Dia
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encoder according to the specified arguments, defining the encoder architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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max_position_embeddings (`int`, *optional*, defaults to 1024):
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The maximum sequence length that this model might ever be used with.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the encoder layers and the pooler layer.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 16):
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Number of key and value heads for each attention layer in the Transformer encoder.
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head_dim (`int`, *optional*, defaults to 128):
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Dimensionality of the attention head.
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intermediate_size (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the normalization layers.
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vocab_size (`int`, *optional*, defaults to 256):
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Vocabulary size of the Dia model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`DiaModel`].
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"swish"` and `"gelu_new"` are supported.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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"""
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model_type = "dia_encoder"
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def __init__(
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self,
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max_position_embeddings: int = 1024,
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num_hidden_layers: int = 12,
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hidden_size: int = 1024,
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num_attention_heads: int = 16,
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num_key_value_heads: int = 16,
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head_dim: int = 128,
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intermediate_size: int = 4096,
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norm_eps: float = 1e-5,
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vocab_size: int = 256,
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hidden_act: str = "silu",
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rope_theta: float = 10000.0,
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rope_scaling: Optional[dict] = None,
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initializer_range: float = 0.02,
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**kwargs,
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):
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self.max_position_embeddings = max_position_embeddings
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self.num_hidden_layers = num_hidden_layers
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.head_dim = head_dim
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self.norm_eps = norm_eps
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self.vocab_size = vocab_size
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, copy it it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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self.initializer_range = initializer_range
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super().__init__(**kwargs)
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class DiaDecoderConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DiaDecoder`]. It is used to instantiate a Dia
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decoder according to the specified arguments, defining the decoder architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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max_position_embeddings (`int`, *optional*, defaults to 3072):
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The maximum sequence length that this model might ever be used with.
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num_hidden_layers (`int`, *optional*, defaults to 18):
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Number of hidden layers in the Transformer decoder.
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimensionality of the decoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*, defaults to 4):
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Number of key and value heads for each attention layer in the Transformer decoder.
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head_dim (`int`, *optional*, defaults to 128):
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Dimensionality of the attention head.
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cross_num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each cross-attention layer in the Transformer decoder.
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cross_head_dim (`int`, *optional*, defaults to 128):
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Dimensionality of the cross-attention head.
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cross_num_key_value_heads (`int`, *optional*, defaults to 16):
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Number of key and value heads for each cross-attention layer in the Transformer decoder.
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cross_hidden_size (`int`, *optional*, defaults to 1024):
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Dimensionality of the cross-attention layers.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the normalization layers.
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vocab_size (`int`, *optional*, defaults to 1028):
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Vocabulary size of the Dia model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`DiaModel`].
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, `"relu"`,
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`"swish"` and `"gelu_new"` are supported.
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num_channels (`int`, *optional*, defaults to 9):
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Number of channels for the Dia decoder.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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is_encoder_decoder (`bool`, *optional*, defaults to `True`):
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Indicating that this model is part of an encoder-decoder architecture.
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"""
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model_type = "dia_decoder"
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def __init__(
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self,
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max_position_embeddings: int = 3072,
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num_hidden_layers: int = 18,
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hidden_size: int = 2048,
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intermediate_size: int = 8192,
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num_attention_heads: int = 16,
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num_key_value_heads: int = 4,
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head_dim: int = 128,
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cross_num_attention_heads: int = 16,
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cross_head_dim: int = 128,
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cross_num_key_value_heads: int = 16,
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cross_hidden_size: int = 1024,
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norm_eps: float = 1e-5,
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vocab_size: int = 1028,
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hidden_act: str = "silu",
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num_channels: int = 9,
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rope_theta: float = 10000.0,
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rope_scaling: Optional[dict] = None,
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initializer_range: float = 0.02,
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use_cache: bool = True,
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is_encoder_decoder: bool = True,
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**kwargs,
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):
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self.max_position_embeddings = max_position_embeddings
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self.num_hidden_layers = num_hidden_layers
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.cross_num_key_value_heads = cross_num_key_value_heads
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self.cross_num_attention_heads = cross_num_attention_heads
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self.cross_head_dim = cross_head_dim
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self.cross_hidden_size = cross_hidden_size
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self.norm_eps = norm_eps
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self.vocab_size = vocab_size
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self.hidden_act = hidden_act
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self.num_channels = num_channels
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, copy it it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs)
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class DiaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DiaModel`]. It is used to instantiate a
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Dia model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the
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[nari-labs/Dia-1.6B](https://huggingface.co/nari-labs/Dia-1.6B) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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encoder_config (`DiaEncoderConfig`, *optional*):
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Configuration for the encoder part of the model. If not provided, a default `DiaEncoderConfig` will be used.
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decoder_config (`DiaDecoderConfig`, *optional*):
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Configuration for the decoder part of the model. If not provided, a default `DiaDecoderConfig` will be used.
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norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the normalization layers.
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is_encoder_decoder (`bool`, *optional*, defaults to `True`):
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Indicating that this model uses an encoder-decoder architecture.
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pad_token_id (`int`, *optional*, defaults to 1025):
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Padding token id.
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eos_token_id (`int`, *optional*, defaults to 1024):
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End of stream token id.
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bos_token_id (`int`, *optional*, defaults to 1026):
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Beginning of stream token id.
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delay_pattern (`list[int]`, *optional*, defaults to `[0, 8, 9, 10, 11, 12, 13, 14, 15]`):
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The delay pattern for the decoder. The length of this list must match `decoder_config.num_channels`.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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Example:
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```python
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>>> from transformers import DiaConfig, DiaModel
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>>> # Initializing a DiaConfig with default values
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>>> configuration = DiaConfig()
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>>> # Initializing a DiaModel (with random weights) from the configuration
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>>> model = DiaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "dia"
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keys_to_ignore_at_inference = ["past_key_values"]
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sub_configs = {"encoder_config": DiaEncoderConfig, "decoder_config": DiaDecoderConfig}
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def __init__(
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self,
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encoder_config: Optional[DiaEncoderConfig] = None,
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decoder_config: Optional[DiaDecoderConfig] = None,
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norm_eps: float = 1e-5,
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is_encoder_decoder: bool = True,
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pad_token_id: int = 1025,
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eos_token_id: int = 1024,
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bos_token_id: int = 1026,
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delay_pattern: Optional[list[int]] = None,
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initializer_range: float = 0.02,
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use_cache: bool = True,
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**kwargs,
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):
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if isinstance(encoder_config, dict):
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encoder_config = DiaEncoderConfig(**encoder_config)
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if isinstance(decoder_config, dict):
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decoder_config = DiaDecoderConfig(**decoder_config)
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self.encoder_config = encoder_config if encoder_config is not None else DiaEncoderConfig()
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self.decoder_config = decoder_config if decoder_config is not None else DiaDecoderConfig()
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self.norm_eps = norm_eps
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self.delay_pattern = delay_pattern if delay_pattern is not None else [0, 8, 9, 10, 11, 12, 13, 14, 15]
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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assert self.decoder_config.num_channels == len(self.delay_pattern), (
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"Number of channels must match delay pattern length."
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)
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super().__init__(
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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bos_token_id=bos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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**kwargs,
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)
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def get_text_config(self, decoder=False):
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"""Defaulting to audio config as it's the decoder in this case which is usually the text backbone"""
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return self.decoder_config
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__all__ = ["DiaConfig", "DiaEncoderConfig", "DiaDecoderConfig"]
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