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            from datasets import load_dataset
         
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            from collections import Counter, defaultdict
         
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            from random import sample, shuffle
         
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            import datasets
         
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            from pandas import DataFrame
         
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            from huggingface_hub import list_datasets
         
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            import os
         
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            import gradio as gr
         
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            import secrets
         
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            from traitlets import default
         
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            parti_prompt_results = []
         
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            ORG = "diffusers-parti-prompts"
         
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            SUBMISSIONS = {
         
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                "sd-v1-5": None,
         
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                "sd-v2-1": None,
         
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                "if-v1-0": None,
         
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                "karlo": None,
         
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            }
         
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            LINKS = {
         
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                "sd-v1-5": "https://huggingface.co/runwayml/stable-diffusion-v1-5",
         
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                "sd-v2-1": "https://huggingface.co/stabilityai/stable-diffusion-2-1",
         
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                "if-v1-0": "https://huggingface.co/DeepFloyd/IF-I-XL-v1.0",
         
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                "karlo": "https://huggingface.co/kakaobrain/karlo-v1-alpha",
         
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            }
         
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            MODEL_KEYS = "-".join(SUBMISSIONS.keys())
         
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            SUBMISSION_ORG = f"results-{MODEL_KEYS}"
         
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            submission_names = list(SUBMISSIONS.keys())
         
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            parti_prompt_categories = load_dataset(os.path.join(ORG, "sd-v1-5"))["train"]["Category"]
         
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            parti_prompt_challenge = load_dataset(os.path.join(ORG, "sd-v1-5"))["train"]["Challenge"]
         
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            def load_submissions():
         
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                all_datasets = list_datasets(author=SUBMISSION_ORG)
         
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                relevant_ids = [d.id for d in all_datasets]
         
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                ids = defaultdict(list)
         
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                challenges = defaultdict(list)
         
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                categories = defaultdict(list)
         
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                for _id in relevant_ids[:2]:
         
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                    ds = load_dataset(_id)["train"]
         
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                    for result, image_id in zip(ds["result"], ds["id"]):
         
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                        ids[result].append(image_id)
         
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                        challenges[parti_prompt_challenge[image_id]].append(result)
         
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                        categories[parti_prompt_categories[image_id]].append(result)
         
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                main_dict = {k: len(v) for k, v in ids.item()}
         
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                challenges = {k: Counter(v) for k, v in challenges.item()}
         
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                categories = {k: Counter(v) for k, v in categories.item()}
         
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                return main_dict, challenges, categories
         
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            def get_dataframe_all():
         
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                main, challanges, categories= load_submissions()
         
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                import ipdb; ipdb.set_trace()
         
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            TITLE = "# Community Parti Prompts - Who is your open-source genAI model?"
         
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            DESCRIPTION = """
         
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            *This is an interactive game in which you click through pre-generated images from SD-v1-5, SD-v2.1, Karlo, and IF 
         
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            using [Parti Prompts](https://huggingface.co/datasets/nateraw/parti-prompts) prompts.* \n
         
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            *You choices will go into the public community [genAI leaderboard](TODO).*
         
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            """
         
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            EXPLANATION = """\n\n
         
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            ## How it works 📖 \n\n
         
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            1. Click on 'Start'
         
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            2. A prompt and 4 different images are displayed
         
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            3. Select your favorite image
         
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            4. After 10 rounds your favorite diffusion model is displayed
         
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            """
         
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            GALLERY_COLUMN_NUM = len(SUBMISSIONS)
         
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            with gr.Blocks() as demo:
         
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                with gr.Column(visible=True) as intro_view:
         
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                    gr.Markdown(TITLE)
         
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                    gr.Markdown(DESCRIPTION)
         
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                    gr.Markdown(EXPLANATION)
         
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                headers = list(SUBMISSIONS.keys())
         
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                datatype = "str"
         
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                dataframes = get_dataframe_all()
         
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                import ipdb; ipdb.set_trace()
         
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                main_dataframe = gr.Dataframe(
         
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                    headers=headers,
         
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                    datatype=datatype,
         
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                    row_count=1,
         
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                    col_count=(len(SUBMISSIONS)),
         
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                    interactive=False,
         
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                )
         
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            demo.launch()
         
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