# coding=utf-8
# Copyright 2023 the Falcon authors and HuggingFace Inc. team.  All rights reserved.
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"""Falcon configuration"""

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


logger = logging.get_logger(__name__)


class FalconConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
    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
    [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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 65024):
            Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`FalconModel`]
        hidden_size (`int`, *optional*, defaults to 4544):
            Dimension of the hidden representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 71):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_ln_in_parallel_attn (`int`, *optional*):
            Set to 2 if separate layer norms are to be used for the MLP and the attention output when using parallel
            attention, otherwise, 1.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether the model should return the last key/values attentions (not used by all models). Only relevant if
            `config.is_decoder=True`.
        hidden_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for MLP layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for attention layers.
        num_kv_heads (`int`, *optional*):
            Number of key-value heads to use per attention layer. If unset, defaults to the same value as
            `num_attention_heads`.
        alibi (`bool`, *optional*, defaults to `False`):
            Whether to use ALiBi positional biases during self-attention.
        new_decoder_architecture (`bool`, *optional*, defaults to `False`):
            Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
            arguments are ignored, as the new decoder always uses parallel attention.
        multi_query (`bool`, *optional*, defaults to `True`):
            Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
        parallel_attn (`bool`, *optional*, defaults to `True`):
            Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
            instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
        bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias on Linear layers.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained
            Falcon models with RoPE support up to 2048 tokens.
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
            accordingly.
            Expected contents:
                `rope_type` (`str`):
                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                    'llama3'], with 'default' being the original RoPE implementation.
                `factor` (`float`, *optional*):
                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                    original maximum pre-trained length.
                `original_max_position_embeddings` (`int`, *optional*):
                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
                    pretraining.
                `attention_factor` (`float`, *optional*):
                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, using the
                    `factor` field to infer the suggested value.
                `beta_fast` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                    ramp function. If unspecified, it defaults to 32.
                `beta_slow` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                    ramp function. If unspecified, it defaults to 1.
                `short_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `long_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `low_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
        bos_token_id (`int`, *optional*, defaults to 11):
            The id of the "beginning-of-sequence" token.
        eos_token_id (`int`, *optional*, defaults to 11):
            The id of the "end-of-sequence" token.
        ffn_hidden_size (`int`, *optional*):
            The hidden size of the feedforward layer in the Transformer decoder.
            defaults to 4x hidden dim
        activation (`str`, *optional*, defaults to `"gelu"`):
            The activation function used in the feedforward layer.

    Example:

    ```python
    >>> from transformers import FalconModel, FalconConfig

    >>> # Initializing a small (2-layer) Falcon configuration
    >>> configuration = FalconConfig(num_hidden_layers=2)

    >>> # Initializing a model from the small configuration
    >>> model = FalconModel(configuration)

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

    model_type = "falcon"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=65024,
        hidden_size=4544,
        num_hidden_layers=32,
        num_attention_heads=71,
        num_ln_in_parallel_attn=None,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        use_cache=True,
        hidden_dropout=0.0,
        attention_dropout=0.0,
        num_kv_heads=None,
        alibi=False,
        new_decoder_architecture=False,
        multi_query=True,
        parallel_attn=True,
        bias=False,
        max_position_embeddings=2048,
        rope_theta=10000.0,
        rope_scaling=None,
        bos_token_id=11,
        eos_token_id=11,
        ffn_hidden_size=None,
        activation="gelu",
        **kwargs,
    ):
        self.vocab_size = vocab_size
        # Backward compatibility with n_embed kwarg
        n_embed = kwargs.pop("n_embed", None)
        self.hidden_size = hidden_size if n_embed is None else n_embed
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.use_cache = use_cache
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
        self.alibi = alibi
        self.new_decoder_architecture = new_decoder_architecture
        self.multi_query = multi_query  # Ignored when new_decoder_architecture is True
        self.parallel_attn = parallel_attn
        self.bias = bias
        self.num_ln_in_parallel_attn = num_ln_in_parallel_attn
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.activation = activation
        if ffn_hidden_size is None:
            self.ffn_hidden_size = hidden_size * 4
        else:
            self.ffn_hidden_size = ffn_hidden_size

        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

    @property
    def head_dim(self):
        return self.hidden_size // self.num_attention_heads

    @property
    def rotary(self):
        return not self.alibi


__all__ = ["FalconConfig"]
