428 lines
20 KiB
Python
428 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2023 The 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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""" CLAP model configuration"""
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import os
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from typing import Union
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class ClapTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the CLAP
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[calp-hsat-fused](https://huggingface.co/laion/clap-hsat-fused) 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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vocab_size (`int`, *optional*, defaults to 30522):
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Vocabulary size of the CLAP model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`ClapTextModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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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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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"relu"`,
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`"relu"`, `"silu"` and `"relu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`ClapTextModel`].
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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
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Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
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positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
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[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
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For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
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with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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is_decoder (`bool`, *optional*, defaults to `False`):
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Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
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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). Only
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relevant if `config.is_decoder=True`.
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projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
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The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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projection_dim (`int`, *optional*, defaults to 512)
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Dimension of the projection head of the `ClapTextModelWithProjection`.
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Examples:
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```python
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>>> from transformers import ClapTextConfig, ClapTextModel
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>>> # Initializing a CLAP text configuration
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>>> configuration = ClapTextConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = ClapTextModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "clap_text_model"
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def __init__(
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self,
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vocab_size=50265,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=514,
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type_vocab_size=1,
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initializer_factor=1.0,
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layer_norm_eps=1e-12,
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projection_dim=512,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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position_embedding_type="absolute",
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use_cache=True,
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projection_hidden_act="relu",
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**kwargs,
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_factor = initializer_factor
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self.layer_norm_eps = layer_norm_eps
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self.position_embedding_type = position_embedding_type
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self.use_cache = use_cache
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self.projection_hidden_act = projection_hidden_act
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self.projection_dim = projection_dim
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the text config dict if we are loading from ClapConfig
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if config_dict.get("model_type") == "clap":
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config_dict = config_dict["text_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class ClapAudioConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a
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CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the audio encoder of the CLAP
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[laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) 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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window_size (`int`, *optional*, defaults to 8):
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Image size of the spectrogram
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num_mel_bins (`int`, *optional*, defaults to 64):
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Number of mel features used per frames. Should correspond to the value used in the `ClapProcessor` class.
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spec_size (`int`, *optional*, defaults to 256):
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Desired input size of the spectrogram that the model supports. It can be different from the output of the
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`ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size`
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of the audio models.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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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"`, `"silu"` and `"gelu_new"` are supported.
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patch_size (`int`, *optional*, defaults to 4):
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Patch size for the audio spectrogram
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patch_stride (`list`, *optional*, defaults to `[4, 4]`):
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Patch stride for the audio spectrogram
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num_classes (`int`, *optional*, defaults to 527):
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Number of classes used for the head training
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hidden_size (`int`, *optional*, defaults to 768):
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Hidden size of the output of the audio encoder. Correspond to the dimension of the penultimate layer's
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output,which is sent to the projection MLP layer.
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projection_dim (`int`, *optional*, defaults to 512):
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Hidden size of the projection layer.
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depths (`list`, *optional*, defaults to `[2, 2, 6, 2]`):
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Depths used for the Swin Layers of the audio model
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num_attention_heads (`list`, *optional*, defaults to `[4, 8, 16, 32]`):
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Number of attention heads used for the Swin Layers of the audio model
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enable_fusion (`bool`, *optional*, defaults to `False`):
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Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the
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best results.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the encoder.
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fusion_type (`[type]`, *optional*):
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Fusion type used for the patch fusion.
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patch_embed_input_channels (`int`, *optional*, defaults to 1):
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Number of channels used for the input spectrogram
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flatten_patch_embeds (`bool`, *optional*, defaults to `True`):
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Whether or not to flatten the patch embeddings
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patch_embeds_hidden_size (`int`, *optional*, defaults to 96):
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Hidden size of the patch embeddings. It is used as the number of output channels.
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enable_patch_layer_norm (`bool`, *optional*, defaults to `True`):
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Whether or not to enable layer normalization for the patch embeddings
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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Drop path rate for the patch fusion
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to add a bias to the query, key, value projections.
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mlp_ratio (`float`, *optional*, defaults to 4.0):
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Ratio of the mlp hidden dim to embedding dim.
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aff_block_r (`int`, *optional*, defaults to 4):
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downsize_ratio used in the AudioFF block
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num_hidden_layers (`int`, *optional*, defaults to 4):
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Number of hidden layers in the Transformer encoder.
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projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
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The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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layer_norm_eps (`[type]`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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Example:
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```python
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>>> from transformers import ClapAudioConfig, ClapAudioModel
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>>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration
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>>> configuration = ClapAudioConfig()
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>>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration
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>>> model = ClapAudioModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "clap_audio_model"
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def __init__(
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self,
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window_size=8,
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num_mel_bins=64,
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spec_size=256,
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hidden_act="gelu",
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patch_size=4,
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patch_stride=[4, 4],
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num_classes=527,
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hidden_size=768,
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projection_dim=512,
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depths=[2, 2, 6, 2],
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num_attention_heads=[4, 8, 16, 32],
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enable_fusion=False,
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hidden_dropout_prob=0.1,
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fusion_type=None,
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patch_embed_input_channels=1,
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flatten_patch_embeds=True,
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patch_embeds_hidden_size=96,
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enable_patch_layer_norm=True,
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drop_path_rate=0.0,
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attention_probs_dropout_prob=0.0,
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qkv_bias=True,
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mlp_ratio=4.0,
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aff_block_r=4,
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num_hidden_layers=4,
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projection_hidden_act="relu",
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layer_norm_eps=1e-5,
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initializer_factor=1.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.window_size = window_size
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self.num_mel_bins = num_mel_bins
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self.spec_size = spec_size
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self.patch_size = patch_size
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self.patch_stride = patch_stride
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self.num_classes = num_classes
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self.hidden_size = hidden_size
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self.depths = depths
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.window_size = window_size
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self.enable_fusion = enable_fusion
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self.fusion_type = fusion_type
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.projection_dim = projection_dim
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self.flatten_patch_embeds = flatten_patch_embeds
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self.patch_embeds_hidden_size = patch_embeds_hidden_size
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self.enable_patch_layer_norm = enable_patch_layer_norm
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self.drop_path_rate = drop_path_rate
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.qkv_bias = qkv_bias
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self.mlp_ratio = mlp_ratio
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self.patch_embed_input_channels = patch_embed_input_channels
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self.aff_block_r = aff_block_r
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self.layer_norm_eps = layer_norm_eps
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self.initializer_factor = initializer_factor
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self.projection_hidden_act = projection_hidden_act
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the audio config dict if we are loading from ClapConfig
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if config_dict.get("model_type") == "clap":
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config_dict = config_dict["audio_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class ClapConfig(PretrainedConfig):
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r"""
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[`ClapConfig`] is the configuration class to store the configuration of a [`ClapModel`]. It is used to instantiate
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a CLAP model according to the specified arguments, defining the text model and audio model configs. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the CLAP
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[laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) 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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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`ClapTextConfig`].
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audio_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`ClapAudioConfig`].
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logit_scale_init_value (`float`, *optional*, defaults to 14.29):
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The inital value of the *logit_scale* paramter. Default is used as per the original CLAP implementation.
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projection_dim (`int`, *optional*, defaults to 512):
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Dimentionality of text and audio projection layers.
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projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
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Activation function for the projection layers.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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Factor to scale the initialization of the model weights.
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kwargs (*optional*):
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Dictionary of keyword arguments.
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Example:
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```python
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>>> from transformers import ClapConfig, ClapModel
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>>> # Initializing a ClapConfig with laion-ai/base style configuration
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>>> configuration = ClapConfig()
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>>> # Initializing a ClapModel (with random weights) from the laion-ai/base style configuration
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>>> model = ClapModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # We can also initialize a ClapConfig from a ClapTextConfig and a ClapAudioConfig
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>>> from transformers import ClapTextConfig, ClapAudioConfig
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>>> # Initializing a ClapText and ClapAudioConfig configuration
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>>> config_text = ClapTextConfig()
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>>> config_audio = ClapAudioConfig()
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>>> config = ClapConfig.from_text_audio_configs(config_text, config_audio)
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```"""
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model_type = "clap"
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def __init__(
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self,
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text_config=None,
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audio_config=None,
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logit_scale_init_value=(1 / 0.07),
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projection_dim=512,
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projection_hidden_act="relu",
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initializer_factor=1.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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if text_config is None:
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text_config = {}
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logger.info("text_config is None. Initializing the ClapTextConfig with default values.")
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if audio_config is None:
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audio_config = {}
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logger.info("audio_config is None. initializing the ClapAudioConfig with default values.")
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self.text_config = ClapTextConfig(**text_config)
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self.audio_config = ClapAudioConfig(**audio_config)
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self.text_config.projection_dim = projection_dim
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self.audio_config.projection_dim = projection_dim
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self.text_config.projection_hidden_act = projection_hidden_act
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self.audio_config.projection_hidden_act = projection_hidden_act
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self.projection_dim = projection_dim
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self.projection_hidden_act = projection_hidden_act
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self.hidden_size = self.text_config.hidden_size
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self.logit_scale_init_value = logit_scale_init_value
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self.initializer_factor = initializer_factor
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self.num_hidden_layers = self.text_config.num_hidden_layers + len(self.audio_config.depths)
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@classmethod
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def from_text_audio_configs(cls, text_config: ClapTextConfig, audio_config: ClapAudioConfig, **kwargs):
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r"""
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Instantiate a [`ClapConfig`] (or a derived class) from clap text model configuration and clap audio model
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configuration.
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Returns:
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[`ClapConfig`]: An instance of a configuration object
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"""
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return cls(text_config=text_config.to_dict(), audio_config=audio_config.to_dict(), **kwargs)
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