# Copyright (c) Alibaba, Inc. and its affiliates.
# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
#
# 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.
import copy
import warnings
from typing import Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.utils.model_parallel_utils import (assert_device_map,
                                                     get_device_map)

from modelscope.metainfo import Models
from modelscope.models.builder import MODELS
from modelscope.outputs import (AttentionBackboneModelOutput, Seq2SeqLMOutput,
                                TokenGeneratorOutput)
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
from .backbone import T5PreTrainedModel, T5Stack
from .configuration import T5Config

logger = get_logger()

# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
__HEAD_MASK_WARNING_MSG = """
The input argument `head_mask` was split into two arguments `head_mask` and
`decoder_head_mask`. Currently, `decoder_head_mask` is set to copy `head_mask`,
but this feature is deprecated and will be removed in future versions. If you do
not want to use any `decoder_head_mask` now, please set `decoder_head_mask =
torch.ones(num_layers, num_heads)`.
"""


@MODELS.register_module(
    group_key=Tasks.text2text_generation,
    module_name=Models.T5,
)
class T5ForConditionalGeneration(T5PreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r'encoder\.embed_tokens\.weight',
        r'decoder\.embed_tokens\.weight',
        r'lm_head\.weight',
    ]
    _keys_to_ignore_on_load_unexpected = [
        r'decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight',
    ]

    def __init__(self, config: T5Config, **kwargs):
        super().__init__(config)
        self.model_dim = config.d_model

        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = T5Stack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = T5Stack(decoder_config, self.shared)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

        # Model parallel
        self.model_parallel = False
        self.device_map = None

    def parallelize(self, device_map=None):
        self.device_map = (
            get_device_map(
                len(self.encoder.block), range(torch.cuda.device_count()))
            if device_map is None else device_map)
        assert_device_map(self.device_map, len(self.encoder.block))
        self.encoder.parallelize(self.device_map)
        self.decoder.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.decoder.first_device)
        self.model_parallel = True

    def deparallelize(self):
        self.encoder.deparallelize()
        self.decoder.deparallelize()
        self.encoder = self.encoder.to('cpu')
        self.decoder = self.decoder.to('cpu')
        self.lm_head = self.lm_head.to('cpu')
        self.model_parallel = False
        self.device_map = None
        torch.cuda.empty_cache()

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def get_output_embeddings(self):
        return self.lm_head

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def forward(self,
                input_ids: Optional[torch.LongTensor] = None,
                attention_mask: Optional[torch.FloatTensor] = None,
                decoder_input_ids: Optional[torch.LongTensor] = None,
                decoder_attention_mask: Optional[torch.BoolTensor] = None,
                head_mask: Optional[torch.FloatTensor] = None,
                decoder_head_mask: Optional[torch.FloatTensor] = None,
                cross_attn_head_mask: Optional[torch.Tensor] = None,
                encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
                past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
                inputs_embeds: Optional[torch.FloatTensor] = None,
                decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
                labels: Optional[torch.LongTensor] = None,
                use_cache: Optional[bool] = None,
                output_attentions: Optional[bool] = None,
                output_hidden_states: Optional[bool] = None,
                return_dict: Optional[bool] = None,
                **kwargs) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
        r"""
        Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. T5 is a model
            with relative position embeddings so you should be able to pad the
            inputs on both the right and the left.

            Indices can be obtained using [`T5Tokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
            for detail.

            [What are input IDs?](../glossary#input-ids)

            To know more on how to prepare `input_ids` for pretraining take a
            look a [T5 Training](./t5#training).
        attention_mask (`torch.FloatTensor` of shape `(batch_size,
        sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask
            values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size,
        target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`T5Tokenizer`]. See
            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`]
            for details.

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            T5 uses the `pad_token_id` as the starting token for
            `decoder_input_ids` generation. If `past_key_values` is used,
            optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            To know more on how to prepare `decoder_input_ids` for pretraining
            take a look at [T5 Training](./t5#training).
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size,
        target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in
            `decoder_input_ids`. Causal mask will also be used by default.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers,
        num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the
            encoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or
        `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the
            decoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or
        `(num_layers, num_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules in
                the decoder. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*,
            `optional`: *attentions*) `last_hidden_state` of shape `(batch_size,
            sequence_length, hidden_size)` is a sequence of hidden states at the
            output of the last layer of the encoder. Used in the cross-attention
            of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length
        `config.n_layers` with each tuple having 4 tensors of shape
        `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention
            blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only
            the last `decoder_input_ids` (those that don't have their past key
            value states given to this model) of shape `(batch_size, 1)` instead
            of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size,
        sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to
            directly pass an embedded representation. This is useful if you want
            more control over how to convert `input_ids` indices into associated
            vectors than the model's internal embedding lookup matrix.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size,
        target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to
            directly pass an embedded representation. If `past_key_values` is
            used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more
            control over how to convert `decoder_input_ids` indices into
            associated vectors than the model's internal embedding lookup
            matrix.

            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset,
            `decoder_inputs_embeds` takes the value of `inputs_embeds`.

        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned
            and can be used to speed up decoding (see `past_key_values`).

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention
            layers. See `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See
            `hidden_states` under returned tensors for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain
            tuple.
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss.
            Indices should be in `[-100, 0, ..., config.vocab_size - 1]`. All
            labels set to `-100` are ignored (masked), the loss is only computed
            for labels in `[0, ..., config.vocab_size]`

        Returns:

        Examples:

        ```python >>> from transformers import T5Tokenizer,
        T5ForConditionalGeneration

        >>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
        >>> model = T5ForConditionalGeneration.from_pretrained("t5-small")

        >>> # training
        >>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
        >>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
        >>> outputs = model(input_ids=input_ids, labels=labels)
        >>> loss = outputs.loss
        >>> logits = outputs.logits

        >>> # inference
        >>> input_ids = tokenizer(
        ...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
        >>> ).input_ids  # Batch size 1
        >>> outputs = model.generate(input_ids)
        >>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
        >>> # studies have shown that owning a dog is good for you.
        ```"""
        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

        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
        if head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            # Convert encoder inputs in embeddings if needed
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        elif return_dict and not isinstance(encoder_outputs,
                                            AttentionBackboneModelOutput):
            encoder_outputs = AttentionBackboneModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1]
                if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2]
                if len(encoder_outputs) > 2 else None,
            )

        hidden_states = encoder_outputs[0]

        if self.model_parallel:
            torch.cuda.set_device(self.decoder.first_device)

        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
            # get decoder inputs from shifting lm labels to the right
            decoder_input_ids = self._shift_right(labels)

        # Set device for model parallelism
        if self.model_parallel:
            torch.cuda.set_device(self.decoder.first_device)
            hidden_states = hidden_states.to(self.decoder.first_device)
            if decoder_input_ids is not None:
                decoder_input_ids = decoder_input_ids.to(
                    self.decoder.first_device)
            if attention_mask is not None:
                attention_mask = attention_mask.to(self.decoder.first_device)
            if decoder_attention_mask is not None:
                decoder_attention_mask = decoder_attention_mask.to(
                    self.decoder.first_device)

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_values=past_key_values,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = decoder_outputs[0]

        # Set device for model parallelism
        if self.model_parallel:
            torch.cuda.set_device(self.encoder.first_device)
            self.lm_head = self.lm_head.to(self.encoder.first_device)
            sequence_output = sequence_output.to(self.lm_head.weight.device)

        if self.config.tie_word_embeddings:
            # Rescale output before projecting on vocab See
            # https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
            sequence_output = sequence_output * (self.model_dim**-0.5)

        lm_logits = self.lm_head(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(
                lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
            # TODO(thom): Add z_loss
            # https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666

        if not return_dict:
            output = (lm_logits, ) + decoder_outputs[1:] + encoder_outputs
            return ((loss, ) + output) if loss is not None else output

        return Seq2SeqLMOutput(
            loss=loss,
            logits=lm_logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

    def prepare_inputs_for_generation(self,
                                      input_ids,
                                      past=None,
                                      attention_mask=None,
                                      head_mask=None,
                                      decoder_head_mask=None,
                                      cross_attn_head_mask=None,
                                      use_cache=None,
                                      encoder_outputs=None,
                                      **kwargs):

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            'decoder_input_ids': input_ids,
            'past_key_values': past,
            'encoder_outputs': encoder_outputs,
            'attention_mask': attention_mask,
            'head_mask': head_mask,
            'decoder_head_mask': decoder_head_mask,
            'cross_attn_head_mask': cross_attn_head_mask,
            'use_cache': use_cache,
        }

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return self._shift_right(labels)

    def generate(
        self,
        *args,
        **kwargs,
    ):
        output = super().generate(*args, **kwargs)
        return TokenGeneratorOutput(
            sequences=output if isinstance(output, torch.Tensor) else output[0]
        )

    def _reorder_cache(self, past, beam_idx):
        # if decoder past is not included in output
        # speedy decoding is disabled and no need to reorder
        if past is None:
            logger.warning(
                'You might want to consider setting `use_cache=True` to speed up decoding'
            )
            return past

        reordered_decoder_past = ()
        for layer_past_states in past:
            # get the correct batch idx from layer past batch dim
            # batch dim of `past` is at 2nd position
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states:
                # need to set correct `past` for each of the four key / value states
                reordered_layer_past_states = reordered_layer_past_states + (
                    layer_past_state.index_select(
                        0, beam_idx.to(layer_past_state.device)), )

            assert reordered_layer_past_states[0].shape == layer_past_states[
                0].shape
            assert len(reordered_layer_past_states) == len(layer_past_states)

            reordered_decoder_past = reordered_decoder_past + (
                reordered_layer_past_states, )
        return reordered_decoder_past
