from typing import Literal, Optional, Union, List
from pydantic import BaseModel, Field
import re
import torch
from compel import Compel
from compel.prompt_parser import (Blend, Conjunction,
                                  CrossAttentionControlSubstitute,
                                  FlattenedPrompt, Fragment)
from ...backend.util.devices import torch_dtype
from ...backend.model_management import ModelType
from ...backend.model_management.models import ModelNotFoundException
from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
                             InvocationConfig, InvocationContext)
from .model import ClipField


class ConditioningField(BaseModel):
    conditioning_name: Optional[str] = Field(
        default=None, description="The name of conditioning data")

    class Config:
        schema_extra = {"required": ["conditioning_name"]}


class CompelOutput(BaseInvocationOutput):
    """Compel parser output"""

    #fmt: off
    type: Literal["compel_output"] = "compel_output"

    conditioning: ConditioningField = Field(default=None, description="Conditioning")
    #fmt: on


class CompelInvocation(BaseInvocation):
    """Parse prompt using compel package to conditioning."""

    type: Literal["compel"] = "compel"

    prompt: str = Field(default="", description="Prompt")
    clip: ClipField = Field(None, description="Clip to use")

    # Schema customisation
    class Config(InvocationConfig):
        schema_extra = {
            "ui": {
                "title": "Prompt (Compel)",
                "tags": ["prompt", "compel"],
                "type_hints": {
                    "model": "model"
                }
            },
        }

    @torch.no_grad()
    def invoke(self, context: InvocationContext) -> CompelOutput:
        tokenizer_info = context.services.model_manager.get_model(
            **self.clip.tokenizer.dict(),
        )
        text_encoder_info = context.services.model_manager.get_model(
            **self.clip.text_encoder.dict(),
        )

        def _lora_loader():
            for lora in self.clip.loras:
                lora_info = context.services.model_manager.get_model(
                    **lora.dict(exclude={"weight"}))
                yield (lora_info.context.model, lora.weight)
                del lora_info
            return

        #loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]

        ti_list = []
        for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
            name = trigger[1:-1]
            try:
                ti_list.append(
                    context.services.model_manager.get_model(
                        model_name=name,
                        base_model=self.clip.text_encoder.base_model,
                        model_type=ModelType.TextualInversion,
                    ).context.model
                )
            except ModelNotFoundException:
                # print(e)
                #import traceback
                #print(traceback.format_exc())
                print(f"Warn: trigger: \"{trigger}\" not found")

        with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
                ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
                ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),\
                text_encoder_info as text_encoder:

            compel = Compel(
                tokenizer=tokenizer,
                text_encoder=text_encoder,
                textual_inversion_manager=ti_manager,
                dtype_for_device_getter=torch_dtype,
                truncate_long_prompts=True,  # TODO:
            )

            conjunction = Compel.parse_prompt_string(self.prompt)
            prompt: Union[FlattenedPrompt, Blend] = conjunction.prompts[0]

            if context.services.configuration.log_tokenization:
                log_tokenization_for_prompt_object(prompt, tokenizer)

            c, options = compel.build_conditioning_tensor_for_prompt_object(
                prompt)

            # TODO: long prompt support
            # if not self.truncate_long_prompts:
            #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
            ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
                tokens_count_including_eos_bos=get_max_token_count(
                    tokenizer, conjunction),
                cross_attention_control_args=options.get(
                    "cross_attention_control", None),)

        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"

        # TODO: hacky but works ;D maybe rename latents somehow?
        context.services.latents.save(conditioning_name, (c, ec))

        return CompelOutput(
            conditioning=ConditioningField(
                conditioning_name=conditioning_name,
            ),
        )

class ClipSkipInvocationOutput(BaseInvocationOutput):
    """Clip skip node output"""
    type: Literal["clip_skip_output"] = "clip_skip_output"
    clip: ClipField = Field(None, description="Clip with skipped layers")

class ClipSkipInvocation(BaseInvocation):
    """Skip layers in clip text_encoder model."""
    type: Literal["clip_skip"] = "clip_skip"

    clip: ClipField = Field(None, description="Clip to use")
    skipped_layers: int = Field(0, description="Number of layers to skip in text_encoder")

    def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
        self.clip.skipped_layers += self.skipped_layers
        return ClipSkipInvocationOutput(
            clip=self.clip,
        )


def get_max_token_count(
        tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction],
        truncate_if_too_long=False) -> int:
    if type(prompt) is Blend:
        blend: Blend = prompt
        return max(
            [
                get_max_token_count(tokenizer, p, truncate_if_too_long)
                for p in blend.prompts
            ]
        )
    elif type(prompt) is Conjunction:
        conjunction: Conjunction = prompt
        return sum(
            [
                get_max_token_count(tokenizer, p, truncate_if_too_long)
                for p in conjunction.prompts
            ]
        )
    else:
        return len(
            get_tokens_for_prompt_object(
                tokenizer, prompt, truncate_if_too_long))


def get_tokens_for_prompt_object(
    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
) -> List[str]:
    if type(parsed_prompt) is Blend:
        raise ValueError(
            "Blend is not supported here - you need to get tokens for each of its .children"
        )

    text_fragments = [
        x.text
        if type(x) is Fragment
        else (
            " ".join([f.text for f in x.original])
            if type(x) is CrossAttentionControlSubstitute
            else str(x)
        )
        for x in parsed_prompt.children
    ]
    text = " ".join(text_fragments)
    tokens = tokenizer.tokenize(text)
    if truncate_if_too_long:
        max_tokens_length = tokenizer.model_max_length - 2  # typically 75
        tokens = tokens[0:max_tokens_length]
    return tokens


def log_tokenization_for_conjunction(
    c: Conjunction, tokenizer, display_label_prefix=None
):
    display_label_prefix = display_label_prefix or ""
    for i, p in enumerate(c.prompts):
        if len(c.prompts) > 1:
            this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
        else:
            this_display_label_prefix = display_label_prefix
        log_tokenization_for_prompt_object(
            p,
            tokenizer,
            display_label_prefix=this_display_label_prefix
        )


def log_tokenization_for_prompt_object(
    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
):
    display_label_prefix = display_label_prefix or ""
    if type(p) is Blend:
        blend: Blend = p
        for i, c in enumerate(blend.prompts):
            log_tokenization_for_prompt_object(
                c,
                tokenizer,
                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
            )
    elif type(p) is FlattenedPrompt:
        flattened_prompt: FlattenedPrompt = p
        if flattened_prompt.wants_cross_attention_control:
            original_fragments = []
            edited_fragments = []
            for f in flattened_prompt.children:
                if type(f) is CrossAttentionControlSubstitute:
                    original_fragments += f.original
                    edited_fragments += f.edited
                else:
                    original_fragments.append(f)
                    edited_fragments.append(f)

            original_text = " ".join([x.text for x in original_fragments])
            log_tokenization_for_text(
                original_text,
                tokenizer,
                display_label=f"{display_label_prefix}(.swap originals)",
            )
            edited_text = " ".join([x.text for x in edited_fragments])
            log_tokenization_for_text(
                edited_text,
                tokenizer,
                display_label=f"{display_label_prefix}(.swap replacements)",
            )
        else:
            text = " ".join([x.text for x in flattened_prompt.children])
            log_tokenization_for_text(
                text, tokenizer, display_label=display_label_prefix
            )


def log_tokenization_for_text(
        text, tokenizer, display_label=None, truncate_if_too_long=False):
    """shows how the prompt is tokenized
    # usually tokens have '</w>' to indicate end-of-word,
    # but for readability it has been replaced with ' '
    """
    tokens = tokenizer.tokenize(text)
    tokenized = ""
    discarded = ""
    usedTokens = 0
    totalTokens = len(tokens)

    for i in range(0, totalTokens):
        token = tokens[i].replace("</w>", " ")
        # alternate color
        s = (usedTokens % 6) + 1
        if truncate_if_too_long and i >= tokenizer.model_max_length:
            discarded = discarded + f"\x1b[0;3{s};40m{token}"
        else:
            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
            usedTokens += 1

    if usedTokens > 0:
        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
        print(f"{tokenized}\x1b[0m")

    if discarded != "":
        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
        print(f"{discarded}\x1b[0m")