from typing import Optional, Union, List, Tuple, Sequence

import numpy as np
import torch
from ...tensors import TextTensor
from langtorch.api.call import chat, auth, File
from langtorch import ctx
import logging
from langtorch.decorators import set_defaults_from_ctx


# Add abstract Activation class

class Activation(torch.nn.Module):
    @set_defaults_from_ctx
    def __new__(cls, model: str = "default",
                system_message: str = "default",
                backward_prompt: Union[str, TextTensor] = "default",
                T: float = 0.8,
                provider: str = 'auto',
                *args, **kwargs):
        if any([arg == "default" for arg in [model, system_message, backward_prompt]]):
            print(f"model = {model}, system_message = {system_message}, backward_prompt = {backward_prompt}")
            raise ValueError("Activation requires a model, system_message, and backward_prompt, but these were not loaded from defaults or passed.")
        if cls is Activation:
            # Return an instance of the OpenAI subclass instead of Activation
            return OpenAI(*args, **kwargs)
        else:
            # If a subclass of Activation is being instantiated, proceed as normal
            return super().__new__(cls)


class ActivationOpenAI(torch.autograd.Function):
    """TextModule * TextModule"""

    @staticmethod
    def forward(ctx, input):
        # Perform the forward pass computation
        ctx.save_for_backward(input)

    @staticmethod
    def backward(ctx, grad_output):
        # Perform the backward pass computation
        input, = ctx.saved_tensors
        return input


class LLM(torch.nn.Module):
    def __init__(self, system_message: Union[
        str, TextTensor] = "You are an expert, who answers only with the requested texts. Keep it short.",
                 model: Union[str, TextTensor] = "gpt-3.5-turbo", T: float = 0.8, *args, **kwargs):
        super(LLM, self).__init__()
        self.system_message = str(system_message)
        self.model = model
        if not 'temperature' in kwargs:
            kwargs['temperature'] = T
        self.n = kwargs.get('n',1)
        self.kwargs = kwargs

    def forward(self, input):
        if isinstance(input, list):
            shape = (-1,self.n) if self.n>1 else -1
            input = TextTensor(input)
        elif isinstance(input, TextTensor):
            shape = tuple([m for m in input.shape]+[self.n]) if self.n>1 else tuple(input.shape)
        else:
            raise TypeError("Activation handles only lists and TextTensors")

        result = chat(input, self.system_message, model=self.model, as_str=True, **self.kwargs)
        return TextTensor(result).reshape(shape)


class OpenAI(torch.nn.Module):
    def __init__(self, system_message: Union[str, TextTensor] = "You are an expert, who answers only with the requested texts. Keep it short.",
                 model: Union[str, TextTensor] = "gpt-3.5-turbo",
                 parse = False,
                 T: float = 0.8, *args, **kwargs):
        super(OpenAI, self).__init__()
        self.system_message = str(system_message)
        self.model = model
        if not 'temperature' in kwargs:
            kwargs['temperature'] = T
        self.kwargs = kwargs
        self.n = kwargs.get('n',1)
        self.parse = parse

    def forward(self, input: TextTensor):
        if isinstance(input, list):
            shape = (-1,self.n) if self.n>1 else -1
            input = TextTensor(input)
        elif isinstance(input, TextTensor):
            shape = tuple([m for m in input.shape]+[self.n]) if self.n>1 else tuple(input.shape)
        else:
            raise TypeError("Activation handles only lists and TextTensors")

        input = list(input.flat)
        for i, m in enumerate(input):
            if np.all([key in ["user", "assistant"] for key in m.keys()]):
                input[i] = m.items()
            else:
                input[i] = str(m)
        logging.debug(input)
        result = chat(input, self.system_message, model=self.model, as_str=True, api_key = ctx.cfg.api.openai.api_key, **self.kwargs)
        assert result is not None
        return TextTensor(result, parse = self.parse).reshape(shape)

ActivationGPT, ChatGPT, GPT = OpenAI, OpenAI, OpenAI

ChatGPT3 = lambda *args,**kwargs: Activation("gpt-3.5-turbo",*args,**kwargs)
GPT4 = lambda *args,**kwargs: Activation("gpt-4",*args,**kwargs)



class MultiheadAttention(torch.nn.Module):
    r"""Allows the model to jointly attend to information
    from different representation subspaces as described in the paper:
    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.

    Multi-Head Attention is defined as:

    .. math::
        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O

    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.

    ``forward()`` will use the optimized implementation described in
    `FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness`_ if all of the following
    conditions are met:

    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensors. This
      restriction will be loosened in the future.)
    - inputs are batched (3D) with ``batch_first==True``
    - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensors argument ``requires_grad``
    - training is disabled (using ``.eval()``)
    - ``add_bias_kv`` is ``False``
    - ``add_zero_attn`` is ``False``
    - ``batch_first`` is ``True`` and the input is batched
    - ``kdim`` and ``vdim`` are equal to ``embed_dim``
    - if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
      nor ``attn_mask`` is passed
    - autocast is disabled

    If the optimized implementation is in use, a
    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
    ``query``/``key``/``value`` to represent padding more efficiently than using a
    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
    will be returned, and an additional speedup proportional to the fraction of the input
    that is padding can be expected.

    Args:
        embed_dim: Total dimension of the model.
        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
            Default: ``False``.
        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).

    Examples::

        >>> # xdoctest: +SKIP
        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)

    .. _`FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness`:
         https://arxiv.org/abs/2205.14135

    """
    __constants__ = ['batch_first']
    bias_k: Optional[torch.Tensor]
    bias_v: Optional[torch.Tensor]

    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False,
                 kdim=None, vdim=None, batch_first=False, device=None, dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim

        self.num_heads = num_heads
        self.dropout = dropout
        self.batch_first = batch_first
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"

        if not self._qkv_same_embed_dim:
            self.q_proj_weight = Parameter(torch.empty((embed_dim, embed_dim), **factory_kwargs))
            self.k_proj_weight = Parameter(torch.empty((embed_dim, self.kdim), **factory_kwargs))
            self.v_proj_weight = Parameter(torch.empty((embed_dim, self.vdim), **factory_kwargs))
            self.register_parameter('in_proj_weight', None)
        else:
            self.in_proj_weight = Parameter(torch.empty((3 * embed_dim, embed_dim), **factory_kwargs))
            self.register_parameter('q_proj_weight', None)
            self.register_parameter('k_proj_weight', None)
            self.register_parameter('v_proj_weight', None)

        if bias:
            self.in_proj_bias = Parameter(torch.empty(3 * embed_dim, **factory_kwargs))
        else:
            self.register_parameter('in_proj_bias', None)
        self.out_proj = NonDynamicallyQuantizableLinear(embed_dim, embed_dim, bias=bias, **factory_kwargs)

        if add_bias_kv:
            self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
            self.bias_v = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
        else:
            self.bias_k = self.bias_v = None

        self.add_zero_attn = add_zero_attn

        self._reset_parameters()

    def _reset_parameters(self):
        if self._qkv_same_embed_dim:
            xavier_uniform_(self.in_proj_weight)
        else:
            xavier_uniform_(self.q_proj_weight)
            xavier_uniform_(self.k_proj_weight)
            xavier_uniform_(self.v_proj_weight)

        if self.in_proj_bias is not None:
            constant_(self.in_proj_bias, 0.)
            constant_(self.out_proj.bias, 0.)
        if self.bias_k is not None:
            xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            xavier_normal_(self.bias_v)

    def __setstate__(self, state):
        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
        if '_qkv_same_embed_dim' not in state:
            state['_qkv_same_embed_dim'] = True

        super().__setstate__(state)

    def forward(
            self,
            query: TextTensor,
            key: TextTensor,
            value: TextTensor,
            key_padding_mask: Optional[torch.Tensor] = None,
            need_weights: bool = True,
            attn_mask: Optional[torch.Tensor] = None,
            average_attn_weights: bool = True,
            is_causal : bool = False) -> Tuple[TextTensor, Optional[torch.Tensor]]:
        r"""
    Args:
        query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
            or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
            :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
            Queries are compared against key-value pairs to produce the output.
            See "Attention Is All You Need" for more details.
        key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
            or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
            :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
            See "Attention Is All You Need" for more details.
        value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
            ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
            sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
            See "Attention Is All You Need" for more details.
        key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
            to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
            Binary and float masks are supported.
            For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
            the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
        need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
            Default: ``True``.
        attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
            :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
            :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
            broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
            Binary and float masks are supported. For a binary mask, a ``True`` value indicates that the
            corresponding position is not allowed to attend. For a float mask, the mask values will be added to
            the attention weight.
            If both attn_mask and key_padding_mask are supplied, their types should match.
        is_causal: If specified, applies a causal mask as attention mask. Mutually exclusive with providing attn_mask.
            Default: ``False``.
        average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
            heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
            effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)

    Outputs:
        - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
          :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
          where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
          embedding dimension ``embed_dim``.
        - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
          returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
          :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
          :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
          head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.

        .. note::
            `batch_first` argument is ignored for unbatched inputs.
        """
        if attn_mask is not None and is_causal:
            raise AssertionError("Only allow causal mask or attn_mask")

        is_batched = query.dim() == 3

        key_padding_mask = F._canonical_mask(
            mask=key_padding_mask,
            mask_name="key_padding_mask",
            other_type=F._none_or_dtype(attn_mask),
            other_name="attn_mask",
            target_type=query.dtype
        )

        why_not_fast_path = ''
        if not is_batched:
            why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
        elif query is not key or key is not value:
            # When lifting this restriction, don't forget to either
            # enforce that the dtypes all match or test cases where
            # they don't!
            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
        elif self.in_proj_bias is not None and query.dtype != self.in_proj_bias.dtype:
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
        elif self.in_proj_weight is not None and query.dtype != self.in_proj_weight.dtype:
            # this case will fail anyway, but at least they'll get a useful error message.
            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
        elif self.training:
            why_not_fast_path = "training is enabled"
        elif not self.batch_first:
            why_not_fast_path = "batch_first was not True"
        elif self.bias_k is not None:
            why_not_fast_path = "self.bias_k was not None"
        elif self.bias_v is not None:
            why_not_fast_path = "self.bias_v was not None"
        elif self.add_zero_attn:
            why_not_fast_path = "add_zero_attn was enabled"
        elif not self._qkv_same_embed_dim:
            why_not_fast_path = "_qkv_same_embed_dim was not True"
        elif query.is_nested and (key_padding_mask is not None or attn_mask is not None):
            why_not_fast_path = "supplying both src_key_padding_mask and src_mask at the same time \
                                 is not supported with NestedTensor input"
        elif torch.is_autocast_enabled():
            why_not_fast_path = "autocast is enabled"

        if not why_not_fast_path:
            tensor_args = (
                query,
                key,
                value,
                self.in_proj_weight,
                self.in_proj_bias,
                self.out_proj.weight,
                self.out_proj.bias,
            )
            # We have to use list comprehensions below because TorchScript does not support
            # generator expressions.
            if torch.overrides.has_torch_function(tensor_args):
                why_not_fast_path = "some Tensor argument has_torch_function"
            elif not all([(x is None or x.is_cuda or 'cpu' in str(x.device)) for x in tensor_args]):
                why_not_fast_path = "some Tensor argument is neither CUDA nor CPU"
            elif torch.is_grad_enabled() and any([x is not None and x.requires_grad for x in tensor_args]):
                why_not_fast_path = ("grad is enabled and at least one of query or the "
                                     "input/output projection weights or biases requires_grad")
            if not why_not_fast_path:
                merged_mask, mask_type = self.merge_masks(attn_mask, key_padding_mask, query)

                return torch._native_multi_head_attention(
                    query,
                    key,
                    value,
                    self.embed_dim,
                    self.num_heads,
                    self.in_proj_weight,
                    self.in_proj_bias,
                    self.out_proj.weight,
                    self.out_proj.bias,
                    merged_mask,
                    need_weights,
                    average_attn_weights,
                    mask_type)

        any_nested = query.is_nested or key.is_nested or value.is_nested
        assert not any_nested, ("MultiheadAttention does not support NestedTensor outside of its fast path. " +
                                f"The fast path was not hit because {why_not_fast_path}")

        if self.batch_first and is_batched:
            # make sure that the transpose op does not affect the "is" property
            if key is value:
                if query is key:
                    query = key = value = query.transpose(1, 0)
                else:
                    query, key = [x.transpose(1, 0) for x in (query, key)]
                    value = key
            else:
                query, key, value = [x.transpose(1, 0) for x in (query, key, value)]

        if not self._qkv_same_embed_dim:
            attn_output, attn_output_weights = F.multi_head_attention_forward(
                query, key, value, self.embed_dim, self.num_heads,
                self.in_proj_weight, self.in_proj_bias,
                self.bias_k, self.bias_v, self.add_zero_attn,
                self.dropout, self.out_proj.weight, self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask, need_weights=need_weights,
                attn_mask=attn_mask,
                use_separate_proj_weight=True,
                q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
                v_proj_weight=self.v_proj_weight,
                average_attn_weights=average_attn_weights,
                is_causal=is_causal)
        else:
            attn_output, attn_output_weights = F.multi_head_attention_forward(
                query, key, value, self.embed_dim, self.num_heads,
                self.in_proj_weight, self.in_proj_bias,
                self.bias_k, self.bias_v, self.add_zero_attn,
                self.dropout, self.out_proj.weight, self.out_proj.bias,
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
                average_attn_weights=average_attn_weights,
                is_causal=is_causal)
        if self.batch_first and is_batched:
            return attn_output.transpose(1, 0), attn_output_weights
        else:
            return attn_output, attn_output_weights

    def merge_masks(self, attn_mask: Optional[torch.Tensor], key_padding_mask: Optional[torch.Tensor],
                    query: torch.Tensor) -> Tuple[Optional[torch.Tensor], Optional[int]]:
        r"""
        Determine mask type and combine masks if necessary. If only one mask is provided, that mask
        and the corresponding mask type will be returned. If both masks are provided, they will be both
        expanded to shape ``(batch_size, num_heads, seq_len, seq_len)``, combined with logical ``or``
        and mask type 2 will be returned
        Args:
            attn_mask: attention mask of shape ``(seq_len, seq_len)``, mask type 0
            key_padding_mask: padding mask of shape ``(batch_size, seq_len)``, mask type 1
            query: query embeddings of shape ``(batch_size, seq_len, embed_dim)``
        Returns:
            merged_mask: merged mask
            mask_type: merged mask type (0, 1, or 2)
        """
        pass