94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
import html
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from ldm.modules.encoders.modules import FrozenCLIPEmbedder
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from modules import script_callbacks, shared
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import gradio as gr
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css = """
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.tokenizer-token{
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cursor: pointer;
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}
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.tokenizer-token-0 {background: rgba(255, 0, 0, 0.05);}
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.tokenizer-token-0:hover {background: rgba(255, 0, 0, 0.15);}
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.tokenizer-token-1 {background: rgba(0, 255, 0, 0.05);}
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.tokenizer-token-1:hover {background: rgba(0, 255, 0, 0.15);}
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.tokenizer-token-2 {background: rgba(0, 0, 255, 0.05);}
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.tokenizer-token-2:hover {background: rgba(0, 0, 255, 0.15);}
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.tokenizer-token-3 {background: rgba(255, 156, 0, 0.05);}
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.tokenizer-token-3:hover {background: rgba(255, 156, 0, 0.15);}
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"""
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def tokenize(text):
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clip: FrozenCLIPEmbedder = shared.sd_model.cond_stage_model.wrapped
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tokens = clip.tokenizer(text, truncation=False, add_special_tokens=False)["input_ids"]
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vocab = {v: k for k, v in clip.tokenizer.get_vocab().items()}
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code = ''
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ids = []
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current_ids = []
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class_index = 0
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def dump():
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nonlocal code, ids, current_ids, class_index
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words = [vocab.get(x, "") for x in current_ids]
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try:
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word = bytearray([clip.tokenizer.byte_decoder[x] for x in ''.join(words)]).decode("utf-8")
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except UnicodeDecodeError:
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return
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word = word.replace("</w>", " ")
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code += f"""<span class='tokenizer-token tokenizer-token-{class_index%4}' title='{html.escape(", ".join([str(x) for x in current_ids]))}'>{html.escape(word)}</span>"""
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ids += current_ids
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class_index += 1
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current_ids = []
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for token in tokens:
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token = int(token)
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current_ids.append(token)
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dump()
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dump()
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return code, ids
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def add_tab():
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with gr.Blocks(analytics_enabled=False, css=css) as ui:
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gr.HTML(f"""
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<style>{css}</style>
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<p>
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Before your text is sent to the neural network, it gets turned into numbers in a process called tokenization. These tokens are how the neural network reads and interprets text. Thanks to our great friends at Shousetsu愛 for inspiration for this feature.
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</p>
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""")
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prompt = gr.Textbox(label="Prompt", elem_id="tokenizer_prompt", show_label=False, lines=8, placeholder="Prompt for tokenization")
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go = gr.Button(value="Tokenize", variant="primary")
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with gr.Tabs():
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with gr.Tab("Text"):
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tokenized_text = gr.HTML(elem_id="tokenized_text")
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with gr.Tab("Tokens"):
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tokens = gr.Text(elem_id="tokenized_tokens", show_label=False)
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go.click(
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fn=tokenize,
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inputs=[prompt],
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outputs=[tokenized_text, tokens],
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)
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return [(ui, "Tokenizer", "tokenizer")]
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script_callbacks.on_ui_tabs(add_tab)
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