# coding=utf-8
# Copyright 2024 Descript and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""XYTokenizer model configuration"""

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

logger = logging.get_logger(__name__)


class XYTokenizerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`XYTokenizerModel`]. It is used to instantiate a
    XY Tokenizer 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:
        input_sample_rate (`int`, *optional*, defaults to 16000):
            The sampling rate of the input audio.
        output_sample_rate (`int`, *optional*, defaults to 16000):
            The sampling rate of the output audio.
        encoder_downsample_rate (`int`, *optional*, defaults to 1280):
            The total downsampling factor of the encoder part.
        decoder_upsample_rate (`int`, *optional*, defaults to 1920):
            The total upsampling factor of the decoder part.
        code_dim (`int`, *optional*, defaults to 1280):
            The dimension of the code embeddings.
        
        // ... (All other parameters from the original YAML/dict config would be listed here) ...
        // For brevity, we will define them with default values based on the provided code.
        
        Example:
        semantic_encoder_d_model (`int`, *optional*, defaults to 1280):
             Hidden dimension for the semantic encoder.
        num_quantizers (`int`, *optional*, defaults to 32):
            Number of residual quantizers.
        ...
    """
    model_type = "xy_tokenizer"

    # A comprehensive config would flatten all nested kwargs from the original `generator_params`.
    # For this example, we will create a simplified version. A real implementation would
    # have all parameters explicitly defined here.
    def __init__(
        self,
        input_sample_rate=16000,
        output_sample_rate=16000,
        encoder_downsample_rate=1280,
        decoder_upsample_rate=1920,
        code_dim=1280,
        # A real config would have dozens of parameters here.
        # We will dynamically accept them via **kwargs.
        **kwargs,
    ):
        self.input_sample_rate = input_sample_rate
        self.output_sample_rate = output_sample_rate
        self.encoder_downsample_rate = encoder_downsample_rate
        self.decoder_upsample_rate = decoder_upsample_rate
        self.code_dim = code_dim
        
        # Store all other parameters dynamically. This is a shortcut.
        # A production-ready config should list all parameters explicitly.
        self.params = kwargs
        
        super().__init__(**kwargs)


__all__ = ["XYTokenizerConfig"]