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import torch

from modules import prompt_parser, devices

from modules import prompt_parser, devices, sd_hijack

from modules.shared import opts

PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])

"""This is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt chunk"""

"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt

chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally

are applied by sd_hijack.EmbeddingsWithFixes's forward function."""

class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):

    """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to

    have unlimited prompt length and assign weights to tokens in prompt.

    """

    def __init__(self, wrapped, hijack):

        super().__init__()

        self.wrapped = wrapped

        self.hijack = hijack

        """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,

        depending on model."""

        self.hijack: sd_hijack.StableDiffusionModelHijack = hijack

        self.chunk_length = 75

    def empty_chunk(self):

        converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;

        All python lists with tokens are assumed to have same length, usually 77.

        if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on

        model - can be 768 and 1024

        model - can be 768 and 1024.

        Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).

        """

        raise NotImplementedError

                    last_comma = len(chunk.tokens)

                # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack

                # is a setting that specifies that is there is a comma nearby, the text after comma should be moved out of this chunk and into the next.

                # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.

                elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:

                    break_location = last_comma + 1