# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""MRA model configuration"""

from ...configuration_utils import PreTrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class MraConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MraModel`]. It is used to instantiate an MRA
    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 Mra
    [uw-madison/mra-base-512-4](https://huggingface.co/uw-madison/mra-base-512-4) 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 50265):
            Vocabulary size of the Mra model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MraModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimension of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            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).
        type_vocab_size (`int`, *optional*, defaults to 1):
            The vocabulary size of the `token_type_ids` passed when calling [`MraModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        block_per_row (`int`, *optional*, defaults to 4):
            Used to set the budget for the high resolution scale.
        approx_mode (`str`, *optional*, defaults to `"full"`):
            Controls whether both low and high resolution approximations are used. Set to `"full"` for both low and
            high resolution and `"sparse"` for only low resolution.
        initial_prior_first_n_blocks (`int`, *optional*, defaults to 0):
            The initial number of blocks for which high resolution is used.
        initial_prior_diagonal_n_blocks (`int`, *optional*, defaults to 0):
            The number of diagonal blocks for which high resolution is used.

    Example:

    ```python
    >>> from transformers import MraConfig, MraModel

    >>> # Initializing a Mra uw-madison/mra-base-512-4 style configuration
    >>> configuration = MraConfig()

    >>> # Initializing a model (with random weights) from the uw-madison/mra-base-512-4 style configuration
    >>> model = MraModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "mra"

    def __init__(
        self,
        vocab_size=50265,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=1,
        initializer_range=0.02,
        layer_norm_eps=1e-5,
        block_per_row=4,
        approx_mode="full",
        initial_prior_first_n_blocks=0,
        initial_prior_diagonal_n_blocks=0,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        add_cross_attention=False,
        tie_word_embeddings=True,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.tie_word_embeddings = tie_word_embeddings
        self.add_cross_attention = add_cross_attention
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.type_vocab_size = type_vocab_size
        self.layer_norm_eps = layer_norm_eps
        self.block_per_row = block_per_row
        self.approx_mode = approx_mode
        self.initial_prior_first_n_blocks = initial_prior_first_n_blocks
        self.initial_prior_diagonal_n_blocks = initial_prior_diagonal_n_blocks


__all__ = ["MraConfig"]
