# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 ..._utils import pad_vocab_size
from ...functional import Tensor
from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear,
                       Embedding, LayerNorm)
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
                              PretrainedConfig)


class OPTDecoderLayer(Module):

    def __init__(self, config: PretrainedConfig, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.config = config
        self.do_layer_norm_before = self.config.do_layer_norm_before

        hidden_size = config.hidden_size
        dtype = config.dtype
        tp_group = config.mapping.tp_group
        tp_size = config.mapping.tp_size

        self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
                                         dtype=dtype)

        layers_range = config.mapping.pp_layers(config.num_hidden_layers)
        local_layer_idx = layer_idx - layers_range[0]
        self.attention = Attention(
            local_layer_idx=local_layer_idx,
            hidden_size=hidden_size,
            num_attention_heads=config.num_attention_heads,
            max_position_embeddings=config.max_position_embeddings,
            attention_mask_type=AttentionMaskType.causal,
            dtype=dtype,
            tp_group=tp_group,
            tp_size=tp_size,
            quant_mode=config.quant_mode)

        mlp_hidden_size = hidden_size * 4 if config.intermediate_size is None else config.intermediate_size

        self.mlp = MLP(hidden_size=hidden_size,
                       ffn_hidden_size=mlp_hidden_size,
                       hidden_act=config.hidden_act,
                       dtype=dtype,
                       tp_group=tp_group,
                       tp_size=tp_size,
                       quant_mode=config.quant_mode)
        self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
                                        dtype=dtype)

    def forward(self,
                hidden_states: Tensor,
                attention_mask=None,
                use_cache=False,
                kv_cache_params=None,
                attention_params=None):
        residual = hidden_states

        attention_input = hidden_states
        if self.do_layer_norm_before:
            attention_input = self.input_layernorm(hidden_states)

        # At this point the hidden_states object must be a Tensor.
        assert isinstance(attention_input, Tensor)

        attention_output = self.attention(attention_input,
                                          attention_mask=attention_mask,
                                          use_cache=use_cache,
                                          kv_cache_params=kv_cache_params,
                                          attention_params=attention_params)
        if use_cache:
            attention_output, presents = attention_output

        hidden_states = residual + attention_output
        if not self.do_layer_norm_before:
            hidden_states = self.input_layernorm(hidden_states)

        residual = hidden_states
        if self.do_layer_norm_before:
            hidden_states = self.post_layernorm(hidden_states)

        hidden_states = self.mlp(hidden_states)

        hidden_states = residual + hidden_states

        if not self.do_layer_norm_before:
            hidden_states = self.post_layernorm(hidden_states)

        if use_cache:
            return (hidden_states, presents)
        return hidden_states


class OPTModel(Module):

    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.do_layer_norm_before = config.do_layer_norm_before

        self.vocab_embedding = Embedding(config.vocab_size,
                                         config.hidden_size,
                                         dtype=config.dtype)
        self.position_embedding = Embedding(config.max_position_embeddings,
                                            config.hidden_size,
                                            dtype=config.dtype)

        self.layers = DecoderLayerList(OPTDecoderLayer, config)

        if self.do_layer_norm_before:
            self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
                                  dtype=config.dtype)

    def forward(self,
                input_ids: Tensor,
                position_ids=None,
                use_cache=False,
                attention_mask=None,
                kv_cache_params=None,
                attention_params=None,
                prompt_embedding_table=None,
                prompt_tasks=None,
                prompt_vocab_size=None,
                **kwargs):

        args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size
                ] if prompt_embedding_table is not None else []
        hidden_states = self.vocab_embedding(input_ids, *args)
        hidden_states = hidden_states + self.position_embedding(position_ids)

        hidden_states = self.layers(hidden_states,
                                    use_cache=use_cache,
                                    attention_mask=attention_mask,
                                    kv_cache_params=kv_cache_params,
                                    attention_params=attention_params)

        if use_cache:
            hidden_states, presents = hidden_states

        if self.do_layer_norm_before:
            hidden_states = self.ln_f(hidden_states)

        if use_cache:
            return (hidden_states, tuple(presents))
        return hidden_states


class OPTForCausalLM(DecoderModelForCausalLM):

    def __init__(self, config: PretrainedConfig):
        self.check_config(config)
        transformer = OPTModel(config)
        vocab_size_padded = pad_vocab_size(config.vocab_size,
                                           config.mapping.tp_size)

        lm_head = ColumnLinear(config.hidden_size,
                               vocab_size_padded,
                               bias=False,
                               dtype=config.dtype,
                               tp_group=config.mapping.tp_group,
                               tp_size=config.mapping.tp_size,
                               gather_output=True)

        super().__init__(config, transformer, lm_head)

    def check_config(self, config):
        config.set_if_not_exist('do_layer_norm_before', False)