# Copyright 2024 Cohere Inc. 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.
from collections.abc import Callable

import torch
import torch.nn as nn
from huggingface_hub.dataclasses import strict

from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import PreTrainedConfig
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_outputs import BaseModelOutputWithPast
from ...modeling_rope_utils import (
    RopeParameters,
    dynamic_rope_update,
)
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, logging
from ...utils.generic import maybe_autocast
from ..cohere.modeling_cohere import (
    CohereAttention,
    CohereDecoderLayer,
    CohereForCausalLM,
    CohereLayerNorm,
    CoherePreTrainedModel,
    CohereRotaryEmbedding,
    apply_rotary_pos_emb,
    eager_attention_forward,
)
from ..gemma2.modeling_gemma2 import Gemma2Model


logger = logging.get_logger(__name__)


@auto_docstring(checkpoint="CohereForAI/c4ai-command-r-v01")
@strict
class Cohere2Config(PreTrainedConfig):
    r"""
    logit_scale (`float`, *optional*, defaults to 0.0625):
        The scaling factor for the output logits.

    ```python
    >>> from transformers import Cohere2Model, Cohere2Config

    >>> # Initializing a Cohere Nextmodel configuration
    >>> configuration = Cohere2Config()

    >>> # Initializing a model from the Cohere2 configuration
    >>> model = Cohere2Model(configuration) # doctest: +SKIP

    >>> # Accessing the model configuration
    >>> configuration = model.config # doctest: +SKIP
    ```
    """

    model_type = "cohere2"
    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"]),
    }

    vocab_size: int = 256000
    hidden_size: int = 8192
    intermediate_size: int = 22528
    logit_scale: float = 0.0625
    num_hidden_layers: int = 40
    num_attention_heads: int = 64
    num_key_value_heads: int | None = None
    hidden_act: str = "silu"
    max_position_embeddings: int = 8192
    initializer_range: float = 0.02
    layer_norm_eps: float = 1e-5
    use_cache: bool = True
    pad_token_id: int | None = 0
    bos_token_id: int | None = 5
    eos_token_id: int | list[int] | None = 255001
    tie_word_embeddings: bool = True
    rope_parameters: RopeParameters | dict | None = None
    attention_bias: bool = False
    attention_dropout: float | int = 0.0
    sliding_window: int | None = 4096
    layer_types: list[str] | None = None

    def __post_init__(self, **kwargs):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        # Need to specify head_dim in the config so it can be used in the attention forward functions
        self.head_dim = self.hidden_size // self.num_attention_heads

        # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
        if self.layer_types is None:
            # BC -> the pattern used to be a simple int, and it's still present in configs on the Hub
            _sliding_window_pattern = kwargs.pop("sliding_window_pattern", 4)
            self.layer_types = [
                "sliding_attention" if bool((i + 1) % _sliding_window_pattern) else "full_attention"
                for i in range(self.num_hidden_layers)
            ]

        super().__post_init__(**kwargs)


class Cohere2RotaryEmbedding(CohereRotaryEmbedding):
    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.repeat_interleave(freqs, 2, dim=-1)  # diff from Llama: we interleave() instead of cat()
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


class Cohere2LayerNorm(CohereLayerNorm):
    pass


class Cohere2Attention(CohereAttention):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Cohere2Config, layer_idx: int | None = None):
        nn.Module.__init__(self)
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True
        layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
        self.sliding_window = config.sliding_window if layer_type == "sliding_attention" else None

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        past_key_values: Cache | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        if self.sliding_window is not None:
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            sliding_window=self.sliding_window,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class Cohere2DecoderLayer(CohereDecoderLayer):
    def __init__(self, config: Cohere2Config, layer_idx: int):
        super().__init__(config, layer_idx)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states_attention, _ = self.self_attn(
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            attention_mask=attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            **kwargs,
        )

        hidden_states_mlp = self.mlp(hidden_states)
        hidden_states = residual + hidden_states_attention + hidden_states_mlp
        return hidden_states


class Cohere2PreTrainedModel(CoherePreTrainedModel):
    config: Cohere2Config
    _can_record_outputs = {
        "hidden_states": Cohere2DecoderLayer,
        "attentions": Cohere2Attention,
    }


class Cohere2Model(Gemma2Model):
    def __init__(self, config: Cohere2Config):
        super().__init__(config)
        self.norm = Cohere2LayerNorm(hidden_size=(config.hidden_size), eps=config.layer_norm_eps)
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = 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(config=self.config)

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

        if not isinstance(causal_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for i, decoder_layer in enumerate(self.layers):
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[self.config.layer_types[i]],
                position_embeddings=position_embeddings,
                past_key_values=past_key_values,
                use_cache=use_cache,
                position_ids=position_ids,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


class Cohere2ForCausalLM(CohereForCausalLM):
    pass


__all__ = ["Cohere2Config", "Cohere2ForCausalLM", "Cohere2Model", "Cohere2PreTrainedModel"]
