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7e976d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
"""Remote text encoder API client (Gradio) for motion generation."""
import logging
import numpy as np
import torch
from gradio_client import Client
# Suppress the [httpx] logs (GET requests)
logging.getLogger("httpx").setLevel(logging.WARNING)
# Suppress internal gradio_client logs
logging.getLogger("gradio_client").setLevel(logging.WARNING)
class TextEncoderAPI:
"""Text encoder API client for motion generation."""
def __init__(self, url: str):
self.client = Client(url, verbose=False)
self.device = "cpu"
self.dtype = torch.float
def _create_np_random_name(self):
import uuid
return str(uuid.uuid4()) + ".npy"
def to(self, device=None, dtype=None):
if device is not None:
self.device = device
if dtype is not None:
self.dtype = dtype
return self
def __call__(self, texts):
"""Encode text prompts into tensors.
Args:
texts (str | list[str]): text prompts to encode
Returns:
tuple[torch.Tensor, list[int]]: encoded text tensors and their lengths
"""
if isinstance(texts, str):
texts = [texts]
tensors = []
lengths = []
for text in texts:
filename = self._create_np_random_name()
result = self.client.predict(
text=text,
filename=filename,
api_name="/DemoWrapper",
)
path = result[0]["value"]
tensor = np.load(path)
length = tensor.shape[0]
tensors.append(tensor)
lengths.append(length)
padded_tensor = np.zeros((len(lengths), max(lengths), tensors[0].shape[-1]), dtype=tensors[0].dtype)
for idx, (tensor, length) in enumerate(zip(tensors, lengths)):
padded_tensor[idx, :length] = tensor
padded_tensor = torch.from_numpy(padded_tensor)
padded_tensor = padded_tensor.to(device=self.device, dtype=self.dtype)
return padded_tensor, lengths
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