Probably irrelevant, cleanup
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@@ -410,28 +410,22 @@ class TextEncoder(nn.Module):
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else:
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last_hidden_state = outputs[self.output_key]
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if prompt_template is not None:
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if data_type == 'I2V_image':
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crop_start = prompt_template.get("crop_start", -1)
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crop_end = prompt_template.get('assistant_emb_start', -1)
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elif data_type == 'I2V_video':
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crop_start = prompt_template.get("crop_start", -1)
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text_crop_start = crop_start - 1 + prompt_template.get("image_emb_len", 576)
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image_crop_start = prompt_template.get("image_emb_start", 5)
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image_crop_end = prompt_template.get('image_emb_end', 581)
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batch_indices, last_double_return_token_indices = torch.where(
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batch_encoding["input_ids"] == prompt_template.get('double_return_token_id', 271))
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last_double_return_token_indices = last_double_return_token_indices.reshape(
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batch_encoding["input_ids"].shape[0], -1)[:, -1]
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batch_indices = batch_indices.reshape(batch_encoding["input_ids"].shape[0], -1)[:, -1]
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assistant_crop_start = last_double_return_token_indices - 1 + prompt_template.get(
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"image_emb_len", 576) - 4
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assistant_crop_end = last_double_return_token_indices - 1 + prompt_template.get(
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"image_emb_len", 576)
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crop_start = prompt_template.get("crop_start", -1)
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text_crop_start = crop_start - 1 + prompt_template.get("image_emb_len", 576)
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image_crop_start = prompt_template.get("image_emb_start", 5)
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image_crop_end = prompt_template.get('image_emb_end', 581)
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batch_indices, last_double_return_token_indices = torch.where(
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batch_encoding["input_ids"] == prompt_template.get('double_return_token_id', 271))
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last_double_return_token_indices = last_double_return_token_indices.reshape(
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batch_encoding["input_ids"].shape[0], -1)[:, -1]
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batch_indices = batch_indices.reshape(batch_encoding["input_ids"].shape[0], -1)[:, -1]
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assistant_crop_start = last_double_return_token_indices - 1 + prompt_template.get(
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"image_emb_len", 576) - 4
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assistant_crop_end = last_double_return_token_indices - 1 + prompt_template.get(
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"image_emb_len", 576)
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attention_mask_assistant_crop_start = last_double_return_token_indices - 4
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attention_mask_assistant_crop_end = last_double_return_token_indices
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else:
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raise ValueError(f"Unsupported data type: {data_type}")
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attention_mask_assistant_crop_start = last_double_return_token_indices - 4
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attention_mask_assistant_crop_end = last_double_return_token_indices
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text_last_hidden_state = []
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text_attention_mask = []
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