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Agile parkour in unstructured environments poses significant challenges for quadruped robots, requiring both dynamic motion generation and terrain adaptability. Recent advances such as Adversarial Motion Priors (AMP) have shown promise in learning dynamic behaviors through motion imitation, but the resulting policies are typically specialized and struggle to generalize across varying terrains. However, existing AMP-based approaches largely lack explicit environmental awareness, leading to limited adaptability and revealing a clear gap in achieving general agile locomotion. To address this limitation, we propose a novel terrain-conditional AMP framework that extends adversarial motion priors by conditioning the discriminator on explicit terrain features, enabling the learning of terrain-aware motion representations adaptable to diverse environments. To improve practical applicability, we further leverage a vision-based policy distillation scheme, where a teacher policy with privileged terrain height information supervises a student policy operating only on forward-looking depth images. This enables agile, perception-driven locomotion in real time. To the best of our knowledge, this is the first work to integrate environmental information into adversarial motion priors and jointly learn a vision-based policy through policy distillation for agile quadruped locomotion. Experiments on terrains such as platforms, gaps, stairs, slopes, and debris show that the proposed method achieves more efficient training convergence and higher success rates compared to pure AMP-based and RL-based methods. These results highlight the effectiveness of the proposed framework and represent a step toward perception-driven agile locomotion for quadruped robots in complex environments.