# Copyright 2024 The Qwen team, Alibaba Group 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
# limitations under the License.
"""Telechat2 Config API."""
__all__ = ['Telechat2Config']

from mindformers.models.configuration_utils import PretrainedConfig
from mindformers.models.model_config_utils import (
    register_mf_model_parameter,
    ignore_and_delete_parameter,
    NotSupportedInfo
)
from mindformers.parallel_core.mf_model_config import MFModelConfig
from mindformers.tools.register import MindFormerRegister, MindFormerModuleType


@MindFormerRegister.register(MindFormerModuleType.CONFIG, legacy=False, search_names='telechat')
class Telechat2Config(PretrainedConfig):
    """
    Args:
        vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model.
        hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states.
        ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states.
        n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer
        n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in 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.
        apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled,
        use the layer norm of the hidden states as the residual in the transformer blocks
        hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout.
        attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs
        use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return
        the last key/values attentions.
        training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning.
        logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation.
        embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm.

    """

    model_type = "telechat"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_hidden_layers": "n_layer",
        "num_attention_heads": "n_head",
    }

    @register_mf_model_parameter(
        mf_model_kwargs=MFModelConfig(
            block_size=32,
            num_blocks=1024,
            normalization='RMSNorm',
            gated_linear_unit=True,
            hidden_act='silu',
            add_mlp_fc1_bias_linear=False,
            add_mlp_fc2_bias_linear=True,
        ))
    @ignore_and_delete_parameter(extra_ignore_param=[
        ('max_window_layers', NotSupportedInfo.useless),
        ('sliding_window', NotSupportedInfo.useless),
        ('use_sliding_window', NotSupportedInfo.useless),
        ('layer_types', NotSupportedInfo.useless),
        ('logn', NotSupportedInfo.useless),
        ('n_inner', NotSupportedInfo.useless),
        ('offset_alibi', NotSupportedInfo.useless),
        ('pretraining_tp', NotSupportedInfo.useless),
        ('skip_bias_add', NotSupportedInfo.useless),
        ('skip_bias_add_qkv', NotSupportedInfo.useless),
        ('slow_but_exact', NotSupportedInfo.useless),
        ('flash_attn', NotSupportedInfo.useless),
        ('slow_but_exact', NotSupportedInfo.useless),
        ('unk_token_id', NotSupportedInfo.useless),
        ('embed_layernorm', NotSupportedInfo.useless),
        ('base_seqlen', NotSupportedInfo.useless),
        ('training_seqlen', NotSupportedInfo.useless),
        ('masked_softmax_fusion', NotSupportedInfo.useless),
    ])
    def __init__(
            self,
            vocab_size=160256,
            hidden_size=4096,
            n_layer=30,
            n_head=32,
            layer_norm_epsilon=1e-5,
            initializer_range=0.02,
            use_cache=True,
            bos_token_id=1,
            eos_token_id=2,
            apply_residual_connection_post_layernorm=False,
            hidden_dropout=0.0,
            attention_dropout=0.0,
            ffn_hidden_size=12288,
            training_seqlen=8192,
            logn=True,
            embed_layernorm=False,
            **kwargs,
    ):
        self.vocab_size = vocab_size
        n_embed = kwargs.pop("n_embed", None)
        self.hidden_size = hidden_size if n_embed is None else n_embed
        self.n_layer = n_layer
        self.n_head = n_head
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.use_cache = use_cache
        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
        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.logn = logn
        self.ffn_hidden_size = ffn_hidden_size
        self.training_seqlen = training_seqlen
        self.embed_layernorm = embed_layernorm
        self.num_key_value_heads = kwargs.pop("num_key_value_heads", None)

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