#
# Copyright 2016 The BigDL Authors.
#
# 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.
#
# Some parts of this file is adapted from
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/cohere/modeling_cohere.py

# coding=utf-8
# Copyright 2024 Cohere team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.

# This file is based on the LLama model definition file in transformers

"""PyTorch Cohere model."""
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.utils.checkpoint
from typing import Optional, Tuple, List
from ipex_llm.transformers.models.llama import repeat_kv
from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36
from ipex_llm.transformers.models.utils import use_decoding_fast_path
from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp
from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
from ipex_llm.transformers.kv import DynamicFp8Cache
from ipex_llm.transformers.models.utils import should_use_fuse_rope
from transformers.modeling_outputs import BaseModelOutputWithPast
from ipex_llm.utils.common import invalidInputError
try:
    from transformers.cache_utils import Cache, DynamicCache
except ImportError:
    Cache = Tuple[torch.Tensor]

KV_CACHE_ALLOC_BLOCK_LENGTH = 256


def cohere_model_forward(
    self,
    input_ids: torch.LongTensor = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    cache_position: Optional[torch.LongTensor] = None,
):
    use_cache = use_cache if use_cache is not None \
        else self.config.use_cache
    if use_cache and use_quantize_kv_cache(self.layers[0].mlp.up_proj, input_ids):
        if not isinstance(past_key_values, DynamicFp8Cache):
            past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
    output_attentions = output_attentions if output_attentions is not None \
        else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None
        else self.config.output_hidden_states
    )
    use_cache = use_cache if use_cache is not None else self.config.use_cache
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    if input_ids is not None and inputs_embeds is not None:
        invalidInputError(False,
                          "You cannot specify both input_ids and inputs_embeds at the same time")

    if self.gradient_checkpointing and self.training and use_cache:
        invalidInputError(False,
                          "`use_cache=True` is incompatible "
                          "with gradient checkpointing. Setting `use_cache=False`.")
        use_cache = False

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

    past_seen_tokens = 0
    if use_cache:  # kept for BC (cache positions)
        if not isinstance(past_key_values, Cache):
            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
        past_seen_tokens = past_key_values.get_seq_length()

    if cache_position is None:
        if isinstance(past_key_values, Cache):
            invalidInputError(False, "cache_position is a required argument when using Cache.")
        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 = self._update_causal_mask(attention_mask,
                                           inputs_embeds, cache_position, past_seen_tokens)

    # embed positions
    hidden_states = inputs_embeds

    # decoder layers
    all_hidden_states = () if output_hidden_states else None
    all_self_attns = () if output_attentions else None
    next_decoder_cache = None

    for decoder_layer in self.layers:
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        if self.gradient_checkpointing and self.training:
            layer_outputs = self._gradient_checkpointing_func(
                decoder_layer.__call__,
                hidden_states,
                causal_mask,
                position_ids,
                past_key_values,
                output_attentions,
                use_cache,
                cache_position,
            )
        else:
            # ipex-llm changes
            curr_device = decoder_layer.input_layernorm.weight.device
            if causal_mask is not None:
                causal_mask = causal_mask.to(curr_device)
            if position_ids is not None:
                position_ids = position_ids.to(curr_device)
            # ipex-llm changes end
            layer_outputs = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_value=past_key_values,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
            )

        hidden_states = layer_outputs[0]

        if use_cache:
            next_decoder_cache = layer_outputs[2 if output_attentions else 1]

        if output_attentions:
            all_self_attns += (layer_outputs[1],)

    hidden_states = self.norm(hidden_states)

    # add hidden states from the last decoder layer
    if output_hidden_states:
        all_hidden_states += (hidden_states,)

    next_cache = next_decoder_cache if use_cache else None
    if not return_dict:
        return tuple(v for v in [hidden_states, next_cache,
                                 all_hidden_states, all_self_attns] if v is not None)
    return BaseModelOutputWithPast(
        last_hidden_state=hidden_states,
        past_key_values=next_cache,
        hidden_states=all_hidden_states,
        attentions=all_self_attns,
    )


def cohere_attention_forward(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    cache_position: Optional[torch.LongTensor] = None,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    if use_quantize_kv_cache(self.q_proj, hidden_states):
        forward_function = cohere_attention_forward_quantized
    else:
        forward_function = cohere_attention_forward_origin
    return forward_function(
        self=self,
        hidden_states=hidden_states,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_value=past_key_value,
        output_attentions=output_attentions,
        use_cache=use_cache,
        cache_position=cache_position,
        **kwargs,
    )


def cohere_attention_forward_quantized(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    cache_position: Optional[torch.LongTensor] = None,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    bsz, q_len, _ = hidden_states.size()

    query_states = self.q_proj(hidden_states)
    key_states = self.k_proj(hidden_states)
    value_states = self.v_proj(hidden_states)

    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
    if self.use_qk_norm:
        query_states = self.q_norm(query_states)
        key_states = self.k_norm(key_states)

    query_states = query_states.transpose(1, 2)
    key_states = key_states.transpose(1, 2)
    value_states = value_states.view(bsz, q_len,
                                     self.num_key_value_heads, self.head_dim).transpose(1, 2)

    past_key_value = getattr(self, "past_key_value", past_key_value)
    kv_seq_len = key_states.shape[-2]
    if past_key_value is not None:
        kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
    cos, sin = self.rotary_emb(value_states, position_ids)
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

    if past_key_value is not None:
        # sin and cos are specific to RoPE models; position_ids needed for the static cache
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx,
                                                         cache_kwargs, new_layout=True)
    if q_len == 1 and query_states.device.type == 'xpu' and not self.training \
            and not hidden_states.requires_grad:
        import xe_addons
        attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, attention_mask)
        attn_weights = None
    else:
        key_states, value_states = restore_fp8_kv_cache(key_states,
                                                        value_states, query_states.dtype)
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        attn_weights = torch.matmul(query_states,
                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights,
                                             dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights,
                                             p=self.attention_dropout, training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)

    invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
                      "`attn_output` should be of size "
                      f"{(bsz, self.num_heads, q_len, self.head_dim)},"
                      f" but is {attn_output.size()}")

    attn_output = attn_output.transpose(1, 2).contiguous()

    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

    attn_output = self.o_proj(attn_output)

    if not output_attentions:
        attn_weights = None

    return attn_output, attn_weights, past_key_value


def cohere_attention_forward_origin(
    self,
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    bsz, q_len, _ = hidden_states.size()
    device = hidden_states.device
    use_fuse_rope = should_use_fuse_rope(hidden_states, position_ids, self.training)
    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
    decoding_fast_path = use_decoding_fast_path(self.q_proj,
                                                use_fuse_rope,
                                                enough_kv_room,
                                                bsz * q_len)
    if decoding_fast_path:
        hidden_states = hidden_states.view(1, -1)
        cache_k = past_key_value.key_cache[self.layer_idx]
        cache_v = past_key_value.value_cache[self.layer_idx]
        kv_seq_len = cache_k.shape[-2]
        import xe_linear
        query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
                                                                       self.q_proj.weight,
                                                                       self.k_proj.weight,
                                                                       self.v_proj.weight,
                                                                       position_ids,
                                                                       cache_k, cache_v,
                                                                       self.q_proj.weight.qtype,
                                                                       self.v_proj.weight.qtype,
                                                                       kv_seq_len,
                                                                       self.head_dim,
                                                                       self.rotary_emb.base,)
        kv_seq_len += 1
        # update past_key_value's seem_tokens and kv caches.
        if self.layer_idx == 0:
            past_key_value._seen_tokens = kv_seq_len
        past_key_value.key_cache[self.layer_idx] = key_states
        past_key_value.value_cache[self.layer_idx] = value_states
    else:
        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
        if self.use_qk_norm:
            query_states = self.q_norm(query_states)
            key_states = self.k_norm(key_states)

        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
                                         self.head_dim).transpose(1, 2)

        past_key_value = getattr(self, "past_key_value", past_key_value)
        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            if self.layer_idx is None:
                invalidInputError(
                    False,
                    "The cache structure has changed since version v4.36. "
                    f"If you are using {self.__class__.__name__} "
                    "for auto-regressive decoding with k/v caching, "
                    "please make sure to initialize the attention class with a layer index."
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            if self.layer_idx == 0:
                past_key_value._seen_tokens += key_states.shape[-2]

            if len(past_key_value.key_cache) <= self.layer_idx:
                past_key_value.key_cache.append(key_states)
                past_key_value.value_cache.append(value_states)
            else:
                cache_k = past_key_value.key_cache[self.layer_idx]
                cache_v = past_key_value.value_cache[self.layer_idx]

                if not enough_kv_room:
                    # allocate new
                    new_c_k, new_c_v = extend_kv_cache(bsz,
                                                       self.num_key_value_heads,  # Support GQA
                                                       self.head_dim,
                                                       cache_k.size(2),
                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
                                                       dtype=cache_k.dtype,
                                                       device=device)

                    new_c_k[:] = cache_k
                    new_c_v[:] = cache_v
                    cache_k = new_c_k
                    cache_v = new_c_v

                key_states, value_states = append_kv_cache(cache_k,
                                                           cache_v,
                                                           key_states,
                                                           value_states)

                # update past_key_value
                past_key_value.key_cache[self.layer_idx] = key_states
                past_key_value.value_cache[self.layer_idx] = value_states

    key_states = repeat_kv(key_states, self.num_key_value_groups)
    value_states = repeat_kv(value_states, self.num_key_value_groups)

    if not self.training and not hidden_states.requires_grad and \
            use_flash_attention(query_states, key_states, attention_mask):
        attn_output = F.scaled_dot_product_attention(query_states.to(device, dtype=torch.float16),
                                                     key_states.to(device, dtype=torch.float16),
                                                     value_states.to(device, dtype=torch.float16),
                                                     is_causal=True)
        attn_weights = None
    elif not self.training and not hidden_states.requires_grad and \
            use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
        import xe_addons
        if attention_mask is not None:
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        else:
            causal_mask = None
        attn_output = xe_addons.sdp(query_states, key_states, value_states, causal_mask)
        attn_output = attn_output.view(query_states.shape)
        attn_weights = None
    else:
        attn_weights = torch.matmul(query_states,
                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights,
                                             dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights,
                                             p=self.attention_dropout, training=self.training)
        attn_output = torch.matmul(attn_weights, value_states)

    invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
                      "`attn_output` should be of size "
                      f"{(bsz, self.num_heads, q_len, self.head_dim)},"
                      f" but is {attn_output.size()}")

    attn_output = attn_output.transpose(1, 2).contiguous()

    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

    attn_output = self.o_proj(attn_output)

    if not output_attentions:
        attn_weights = None

    return attn_output.to(hidden_states.dtype), attn_weights, past_key_value
