Upload config

config.json

@@ -1,10 +1,7 @@
 {
-  "architectures": [
-    "MERaLiONForConditionalGeneration"
-  ],
+  "_attn_implementation_autoset": true,
   "auto_map": {
-    "AutoConfig": "configuration_meralion.MERaLiONConfig",
-    "AutoModelForSpeechSeq2Seq": "modeling_meralion.MERaLiONForConditionalGeneration"
+    "AutoConfig": "configuration_meralion.MERaLiONConfig"
   },
   "head_dim": 256,
   "hidden_size": 3584,
@@ -51,7 +48,7 @@
     ],
     "mask_time_length": 20,
     "max_length": 448,
-    "model_type": "meralion_speech_encoder",
+    "model_type": "whisper",
     "num_hidden_layers": 32,
     "num_mel_bins": 80,
     "pad_token_id": 50257,
@@ -159,13 +156,12 @@
     "hidden_act": "gelu_pytorch_tanh",
     "hidden_size": 3584,
     "intermediate_size": 14336,
-    "model_type": "meralion_text_decoder",
+    "model_type": "gemma2",
     "num_hidden_layers": 42,
     "num_key_value_heads": 8,
     "query_pre_attn_scalar": 256,
     "sliding_window_size": 4096,
     "torch_dtype": "bfloat16"
   },
-  "torch_dtype": "bfloat16",
   "transformers_version": "4.46.3"
-}
+}

configuration_meralion.py

@@ -1,442 +1,13 @@
 """MERaLiON AudioLLM model configuration"""
 
-from collections import OrderedDict
-from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
-
+from transformers import Gemma2Config, WhisperConfig
 from transformers.configuration_utils import PretrainedConfig
-from transformers.onnx import OnnxConfig
 from transformers.utils import logging
 
 
-if TYPE_CHECKING:
-    from transformers.feature_extraction_utils import FeatureExtractionMixin
-    from transformers.tokenization_utils_base import PreTrainedTokenizerBase
-    from transformers.utils import TensorType
-
-
 logger = logging.get_logger(__name__)
 
 
-# fmt: off
-NON_SPEECH_TOKENS = [
-    1, 2, 7, 8, 9, 10, 14, 25,
-    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
-    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
-    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
-    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
-    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
-    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
-    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
-    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
-]
-NON_SPEECH_TOKENS_MULTI = [
-    1, 2, 7, 8, 9, 10, 14, 25,
-    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
-    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
-    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
-    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
-    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
-    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
-    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
-    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
-]
-# fmt: on
-
-# Copied from transformers.models.whisper.configuration_whisper.WhisperConfig
-class MERaLiONSpeechConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MERaLiONSpeechModel`]. It is used to instantiate a
-    MERaLiONSpeech 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 MERaLiONSpeech
-    [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) 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 51865):
-            Vocabulary size of the MERaLiONSpeech model. Defines the number of different tokens that can be represented by the
-            `decoder_input_ids` passed when calling [`MERaLiONSpeechModel`]
-        num_mel_bins (`int`, *optional*, defaults to 80):
-            Number of mel features used per input features. Should correspond to the value used in the
-            `MERaLiONSpeechProcessor` class.
-        encoder_layers (`int`, *optional*, defaults to 4):
-            Number of encoder layers.
-        decoder_layers (`int`, *optional*, defaults to 4):
-            Number of decoder layers.
-        encoder_attention_heads (`int`, *optional*, defaults to 6):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        decoder_attention_heads (`int`, *optional*, defaults to 6):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        encoder_ffn_dim (`int`, *optional*, defaults to 1536):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
-        decoder_ffn_dim (`int`, *optional*, defaults to 1536):
-            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
-        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
-            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
-            for more details.
-        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
-            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
-            for more details.
-        decoder_start_token_id (`int`, *optional*, defaults to 50257):
-            Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
-            are provided to the `generate` function. It is used to guide the model`s generation process depending on
-            the task.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
-        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
-            Whether the model is used as an encoder/decoder or not.
-        activation_function (`str`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"silu"` and `"gelu_new"` are supported.
-        d_model (`int`, *optional*, defaults to 384):
-            Dimensionality of the layers.
-        dropout (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        activation_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for activations inside the fully connected layer.
-        init_std (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        scale_embedding (`bool`, *optional*, defaults to False):
-            Scale embeddings by diving by sqrt(d_model).
-        max_source_positions (`int`, *optional*, defaults to 1500):
-            The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
-        max_target_positions (`int`, *optional*, defaults to 448):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        pad_token_id (`int`, *optional*, defaults to 50256):
-            Padding token id.
-        bos_token_id (`int`, *optional*, defaults to 50256):
-            Begin of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 50256):
-            End of stream token id.
-        suppress_tokens (`List[int]`, *optional*):
-            A list containing the non-speech tokens that will be used by the logit processor in the `generate`
-            function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the
-            `multilingual` model.
-        begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`):
-            A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as
-            the token for `" "` (`blank_token_id`) and the `eos_token_id`
-        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 [`MERaLiONSpeechForAudioClassification`].
-        classifier_proj_size (`int`, *optional*, defaults to 256):
-            Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an
-            instance of [`MERaLiONSpeechForAudioClassification`].
-        apply_spec_augment (`bool`, *optional*, defaults to `False`):
-            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://arxiv.org/abs/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
-            procecure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If
-            reasoning from the propability 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 == 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 procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over
-            the axis. If reasoning from the propability 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`.
-        median_filter_width (`int`, *optional*, defaults to 7):
-            Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps.
-            Should be an odd number.
-    """
-
-    model_type = "meralion_speech_encoder"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "num_key_value_heads": "encoder_attention_heads",
-        "num_attention_heads": "encoder_attention_heads",
-        "hidden_size": "d_model",
-    }
-
-    def __init__(
-        self,
-        vocab_size=51865,
-        num_mel_bins=80,
-        encoder_layers=4,
-        encoder_attention_heads=6,
-        decoder_layers=4,
-        decoder_attention_heads=6,
-        decoder_ffn_dim=1536,
-        encoder_ffn_dim=1536,
-        encoder_layerdrop=0.0,
-        decoder_layerdrop=0.0,
-        decoder_start_token_id=50257,
-        use_cache=True,
-        is_encoder_decoder=True,
-        activation_function="gelu",
-        d_model=384,
-        dropout=0.0,
-        attention_dropout=0.0,
-        activation_dropout=0.0,
-        init_std=0.02,
-        scale_embedding=False,
-        max_source_positions=1500,
-        max_target_positions=448,
-        pad_token_id=50256,
-        bos_token_id=50256,
-        eos_token_id=50256,
-        suppress_tokens=None,
-        begin_suppress_tokens=[220, 50256],
-        use_weighted_layer_sum=False,
-        classifier_proj_size=256,
-        apply_spec_augment=False,
-        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,
-        median_filter_width=7,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.num_mel_bins = num_mel_bins
-        self.d_model = d_model
-        self.encoder_layers = encoder_layers
-        self.encoder_attention_heads = encoder_attention_heads
-        self.decoder_layers = decoder_layers
-        self.decoder_attention_heads = decoder_attention_heads
-        self.decoder_ffn_dim = decoder_ffn_dim
-        self.encoder_ffn_dim = encoder_ffn_dim
-        self.dropout = dropout
-        self.attention_dropout = attention_dropout
-        self.activation_dropout = activation_dropout
-        self.activation_function = activation_function
-        self.init_std = init_std
-        self.encoder_layerdrop = encoder_layerdrop
-        self.decoder_layerdrop = decoder_layerdrop
-        self.use_cache = use_cache
-        self.num_hidden_layers = encoder_layers
-        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
-        self.max_source_positions = max_source_positions
-        self.max_target_positions = max_target_positions
-
-        # Audio Classification-specific parameters. Feel free to ignore for other classes.
-        self.classifier_proj_size = classifier_proj_size
-        self.use_weighted_layer_sum = use_weighted_layer_sum
-
-        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/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
-
-        self.median_filter_width = median_filter_width
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            is_encoder_decoder=is_encoder_decoder,
-            decoder_start_token_id=decoder_start_token_id,
-            suppress_tokens=suppress_tokens,
-            begin_suppress_tokens=begin_suppress_tokens,
-            **kwargs,
-        )
-    @property
-    def inputs(self) -> Mapping[str, Mapping[int, str]]:
-        common_inputs = OrderedDict(
-            [
-                ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
-            ]
-        )
-        if self.use_past:
-            common_inputs["decoder_input_ids"] = {0: "batch"}
-        else:
-            common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
-
-        if self.use_past:
-            self.fill_with_past_key_values_(common_inputs, direction="inputs")
-
-        return common_inputs
-
-    def generate_dummy_inputs(
-        self,
-        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
-        batch_size: int = -1,
-        seq_length: int = -1,
-        is_pair: bool = False,
-        framework: Optional["TensorType"] = None,
-        sampling_rate: int = 22050,
-        time_duration: float = 5.0,
-        frequency: int = 220,
-    ) -> Mapping[str, Any]:
-        dummy_inputs = OrderedDict()
-        encoder_inputs = OnnxConfig.generate_dummy_inputs(
-            self,
-            preprocessor=preprocessor.feature_extractor,
-            batch_size=batch_size,
-            framework=framework,
-            sampling_rate=sampling_rate,
-            time_duration=time_duration,
-            frequency=frequency,
-        )
-        encoder_sequence_length = encoder_inputs["input_features"].shape[2]
-        seq_length = encoder_sequence_length // 2 if self.use_past else seq_length
-
-        decoder_inputs = super().generate_dummy_inputs(
-            preprocessor.tokenizer, batch_size, seq_length, is_pair, framework
-        )
-
-        dummy_inputs["input_features"] = encoder_inputs.pop("input_features")
-        dummy_inputs["decoder_input_ids"] = decoder_inputs.pop("decoder_input_ids")
-
-        if "past_key_values" in decoder_inputs:
-            dummy_inputs["past_key_values"] = decoder_inputs.pop("past_key_values")
-
-        return dummy_inputs
-
-    @property
-    def atol_for_validation(self) -> float:
-        return 1e-3
-    
-
-# Copied from transformers.models.gemma2.configuration_gemma2.Gemma2Config
-class MERaLiONTextConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MERaLiONTextModel`]. It is used to instantiate an MERaLiONText
-    model according to the specified arguments, defining the model 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 256000):
-            Vocabulary size of the MERaLiONText model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MERaLiONTextModel`]
-        hidden_size (`int`, *optional*, defaults to 3072):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 24576):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 28):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        num_key_value_heads (`int`, *optional*, defaults to 16):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
-            `num_attention_heads`.
-        head_dim (`int`, *optional*, defaults to 256):
-            The attention head dimension.
-        hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
-            The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
-            if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
-        max_position_embeddings (`int`, *optional*, defaults to 8192):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        pad_token_id (`int`, *optional*, defaults to 0):
-            Padding token id.
-        eos_token_id (`int`, *optional*, defaults to 1):
-            End of stream token id.
-        bos_token_id (`int`, *optional*, defaults to 2):
-            Beginning of stream token id.
-        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        query_pre_attn_scalar (`float`, *optional*, defaults to 224): scaling factor used on the attention scores
-        sliding_window (`int`, *optional*, defaults to 4096): in MERaLiONText, every other layer uses sliding window attention. This is the
-            size of the sliding window.
-        final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits.
-        attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores.
-        cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
-    """
-
-    model_type = "meralion_text_decoder"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=256000,
-        hidden_size=3072,
-        intermediate_size=24576,
-        num_hidden_layers=28,
-        num_attention_heads=16,
-        num_key_value_heads=16,
-        head_dim=256,
-        hidden_activation="gelu_pytorch_tanh",
-        max_position_embeddings=8192,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        pad_token_id=0,
-        eos_token_id=1,
-        bos_token_id=2,
-        tie_word_embeddings=True,
-        rope_theta=10000.0,
-        attention_bias=False,
-        attention_dropout=0.0,
-        query_pre_attn_scalar=224,
-        sliding_window=4096,
-        final_logit_softcapping=30.0,
-        attn_logit_softcapping=50.0,
-        cache_implementation="hybrid",
-        **kwargs,
-    ):
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.head_dim = head_dim
-        self.num_key_value_heads = num_key_value_heads
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.hidden_activation = hidden_activation
-        self.query_pre_attn_scalar = query_pre_attn_scalar
-        self.sliding_window = sliding_window
-        self.final_logit_softcapping = final_logit_softcapping
-        self.attn_logit_softcapping = attn_logit_softcapping
-        self.cache_implementation = cache_implementation
-
-
 class MERaLiONConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`MERaLiONForConditionalGeneration`]. It is used to instantiate an
@@ -468,9 +39,9 @@ class MERaLiONConfig(PretrainedConfig):
     ):
         
         if isinstance(speech_config, dict):
-            speech_config = MERaLiONSpeechConfig(**speech_config)
+            speech_config = WhisperConfig(**speech_config)
         elif speech_config is None:
-            speech_config = MERaLiONSpeechConfig(
+            speech_config = WhisperConfig(
                 d_model=1280,
                 encoder_attention_heads=20,
                 encoder_ffn_dim=5120,
@@ -485,9 +56,9 @@ class MERaLiONConfig(PretrainedConfig):
         self.speech_config = speech_config
 
         if isinstance(text_config, dict):
-            text_config = MERaLiONTextConfig(**text_config)
+            text_config = Gemma2Config(**text_config)
         elif text_config is None:
-            text_config = MERaLiONTextConfig()
+            text_config = Gemma2Config()
 
         self.text_config = text_config