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import gradio as gr |
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import torch |
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EXAMPLE_MD = """ |
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```python |
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import torch |
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t1 = torch.arange({n1}).view({dim1}) |
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t2 = torch.arange({n2}).view({dim2}) |
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(t1 @ t2).shape = {out_shape} |
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``` |
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""" |
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def generate_example(dim1: list, dim2: list): |
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n1 = 1 |
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n2 = 1 |
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for i in dim1: |
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n1 *= i |
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for i in dim2: |
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n2 *= i |
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t1 = torch.arange(n1).view(dim1) |
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t2 = torch.arange(n2).view(dim2) |
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try: |
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out_shape = list((t1 @ t2).shape) |
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except RuntimeError: |
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out_shape = "error" |
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return n1,dim1,n2,dim2,out_shape |
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def sanitize_dimention(dim): |
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if dim is None: |
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gr.Error("one of the dimentions is empty, please fill it") |
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if "[" in dim: |
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dim = dim.replace("[", "") |
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if "]" in dim: |
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dim = dim.replace("]", "") |
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if "," in dim: |
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dim = dim.replace(",", " ").strip() |
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out = [int(i.strip()) for i in dim.split()] |
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else: |
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out = [int(dim.strip())] |
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if 0 in out: |
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gr.Error( |
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"Found the number 0 in one of the dimensions which is not allowed, consider using 1 instead" |
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) |
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return out |
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def predict(dim1, dim2): |
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dim1 = sanitize_dimention(dim1) |
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dim2 = sanitize_dimention(dim2) |
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n1,dim1,n2,dim2,out_shape = generate_example(dim1, dim2) |
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return EXAMPLE_MD.format( |
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n1=str(n1), dim1=str(dim1), s2=str(n2), dim2=str(dim2), out_shape=str(out_shape) |
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) |
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demo = gr.Interface( |
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predict, |
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inputs=["text", "text"], |
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outputs=["markdown"], |
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examples=[["1,2,3", "5,3,7"], ["1,2,3", "5,2,7"]], |
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) |
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demo.launch(debug=True) |
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