# coding=utf-8
# Copyright 2024 Google Inc. 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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# Unless required by applicable law or agreed to in writing, software
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from typing import TYPE_CHECKING, Any, Optional

import sentencepiece as spm
import torch
from torch import nn

from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import PretrainedConfig
from ...masking_utils import create_causal_mask
from ...modeling_outputs import BaseModelOutputWithPast
from ...processing_utils import Unpack
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import TransformersKwargs, logging
from ..llama.modeling_llama import (
    LlamaForCausalLM,
    LlamaForSequenceClassification,
    LlamaForTokenClassification,
    LlamaMLP,
    LlamaModel,
)
from ..llama.tokenization_llama import LlamaTokenizer


if TYPE_CHECKING:
    from ...tokenization_utils_base import TextInput

VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}

SPIECE_UNDERLINE = "▁"


logger = logging.get_logger(__name__)


class GemmaConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
    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 Gemma-7B.
    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
    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 Gemma model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GemmaModel`]
        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, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        head_dim (`int`, *optional*, defaults to 256):
            The attention head dimension.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
            The legacy activation function. It is overwritten by the `hidden_activation`.
        hidden_activation (`str` or `function`, *optional*):
            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.
    ```python
    >>> from transformers import GemmaModel, GemmaConfig
    >>> # Initializing a Gemma gemma-7b style configuration
    >>> configuration = GemmaConfig()
    >>> # Initializing a model from the gemma-7b style configuration
    >>> model = GemmaModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "gemma"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    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_act="gelu_pytorch_tanh",
        hidden_activation=None,
        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,
        **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.hidden_act = hidden_act
        self.hidden_activation = hidden_activation
        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

        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,
        )


class GemmaTokenizer(LlamaTokenizer, PreTrainedTokenizer):
    """
    Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
    no padding token in the original model.

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`):
            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`):
            The end of sequence token.
        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"`):
            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
            attention mechanisms or loss computation.
        sp_model_kwargs (`dict[str, Any]`, `Optional`, *optional*):
            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
            to set:

            - `enable_sampling`: Enable subword regularization.
            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

              - `nbest_size = {0,1}`: No sampling is performed.
              - `nbest_size > 1`: samples from the nbest_size results.
              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
                using forward-filtering-and-backward-sampling algorithm.

            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
              BPE-dropout.

        add_bos_token (`bool`, *optional*, defaults to `True`):
            Whether or not to add an `bos_token` at the start of sequences.
        add_eos_token (`bool`, *optional*, defaults to `False`):
            Whether or not to add an `eos_token` at the end of sequences.
        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
            extra spaces.
        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
            Whether or not the default system prompt for Gemma should be used.
        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
            Whether or not to add spaces between special tokens.
    """

    def __init__(
        self,
        vocab_file,
        unk_token="<unk>",
        bos_token="<bos>",
        eos_token="<eos>",
        pad_token="<pad>",
        sp_model_kwargs: Optional[dict[str, Any]] = None,
        add_bos_token=True,
        add_eos_token=False,
        clean_up_tokenization_spaces=False,
        use_default_system_prompt=False,
        spaces_between_special_tokens=False,
        **kwargs,
    ):
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token

        self.vocab_file = vocab_file
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token
        self.use_default_system_prompt = use_default_system_prompt
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(vocab_file)

        PreTrainedTokenizer.__init__(
            self,
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            add_bos_token=add_bos_token,
            add_eos_token=add_eos_token,
            sp_model_kwargs=sp_model_kwargs,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            use_default_system_prompt=use_default_system_prompt,
            spaces_between_special_tokens=spaces_between_special_tokens,
            **kwargs,
        )

    def get_spm_processor(self):
        raise AttributeError("Not needed for Gemma")

    def unk_token_length(self):
        raise AttributeError("Not needed for Gemma")

    def tokenize(self, text: "TextInput", **kwargs) -> list[str]:
        """
        Args:
            text: TextInput
        Simply calls PreTrainedTokenizer's method
        """
        return PreTrainedTokenizer.tokenize(self, text, **kwargs)

    def _tokenize(self, text, **kwargs):
        """
        Args:
            text: TextInput
        Returns a tokenized string. The Gemma tokenizer never adds a prefix space.
        """
        return self.sp_model.encode(text, out_type=str)

    def _decode(
        self,
        token_ids: list[int],
        skip_special_tokens: bool = False,
        spaces_between_special_tokens: bool = False,
        **kwargs,
    ) -> str:
        sub_texts = []
        current_sub_text = []
        for ids in token_ids:
            if skip_special_tokens and ids in self.all_special_ids:
                continue
            if ids in self._added_tokens_decoder:
                if current_sub_text:
                    sub_texts.append(self.sp_model.decode(current_sub_text))
                sub_texts.append(self._added_tokens_decoder[ids].content)
                current_sub_text = []
            else:
                current_sub_text.append(ids)
        if current_sub_text:
            sub_texts.append(self.sp_model.decode(current_sub_text))

        if spaces_between_special_tokens:
            sub_texts = " ".join(sub_texts)
        else:
            sub_texts = "".join(sub_texts)

        return sub_texts.replace(SPIECE_UNDERLINE, " ")

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        current_sub_tokens = []
        out_string = ""
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self._added_tokens_encoder:
                out_string += self.sp_model.decode(current_sub_tokens) + token
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string


class GemmaRMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.zeros(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        output = self._norm(x.float())
        # Llama does x.to(float16) * w whilst Gemma is (x * w).to(float16)
        # See https://github.com/huggingface/transformers/pull/29402
        output = output * (1.0 + self.weight.float())
        return output.type_as(x)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.eps}"


class GemmaMLP(LlamaMLP):
    def __init__(self, config):
        super().__init__()
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)


class GemmaModel(LlamaModel):
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            config=self.config,
            input_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        # embed positions
        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        # normalized
        # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
        # See https://github.com/huggingface/transformers/pull/29402
        normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
        hidden_states = hidden_states * normalizer

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_value=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **kwargs,
            )
        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
        )


class GemmaForCausalLM(LlamaForCausalLM):
    def forward(**super_kwargs):
        r"""
        Example:

        ```python
        >>> from transformers import AutoTokenizer, GemmaForCausalLM

        >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "What is your favorite condiment?"
        ```"""
        return super().forward(**super_kwargs)


class GemmaForSequenceClassification(LlamaForSequenceClassification):
    pass


class GemmaForTokenClassification(LlamaForTokenClassification):
    pass


__all__ = [
    "GemmaConfig",
    "GemmaTokenizer",
    "GemmaModel",
    "GemmaForCausalLM",
    "GemmaForSequenceClassification",
    "GemmaForTokenClassification",
    "GemmaPreTrainedModel",  # noqa: F822
]