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# Copyright 2024 EPFL and Apple Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
def convert_samples_to_mod_dict(samples, input_mod, target_mod, num_input_tokens, num_target_tokens):
"""Converts a sample (e.g. a batch of RGB images) to a mod dict that can be passed directly to FourM.
Assumes both the input modality and target modality are dense tasks.
"""
B = samples.shape[0]
device = samples.device
if input_mod == target_mod:
assert(num_input_tokens == num_target_tokens)
mod_dict = {
input_mod: {
'tensor': samples,
'input_mask': torch.zeros((B, num_input_tokens), dtype=torch.bool, device=device),
'target_mask': torch.zeros((B, num_target_tokens), dtype=torch.bool, device=device),
'decoder_attention_mask': torch.zeros((B, num_target_tokens), dtype=torch.int, device=device),
},
}
mod_dict[input_mod]['decoder_attention_mask'][:, 0] = num_target_tokens
else:
mod_dict = {
input_mod: {
'tensor': samples,
'input_mask': torch.zeros((B, num_input_tokens), dtype=torch.bool, device=samples.device),
'target_mask': torch.ones((B, num_input_tokens), dtype=torch.bool, device=samples.device),
'decoder_attention_mask': torch.zeros((B, num_input_tokens), dtype=torch.int, device=samples.device),
},
target_mod: {
'tensor': torch.zeros((B, num_target_tokens), dtype=torch.long, device=samples.device),
'input_mask': torch.ones((B, num_target_tokens), dtype=torch.bool, device=samples.device),
'target_mask': torch.zeros((B, num_target_tokens), dtype=torch.bool, device=samples.device),
'decoder_attention_mask': torch.ones((B, num_target_tokens), dtype=torch.int, device=samples.device),
},
}
mod_dict[target_mod]['decoder_attention_mask'][:, 0] = num_target_tokens
return mod_dict
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