File size: 20,830 Bytes
fb9884b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# 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.
"""Convert SigLIP checkpoints from the original repository.

URL: https://github.com/google-research/big_vision/tree/main
"""


import argparse
import collections
from pathlib import Path

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from numpy import load
from PIL import Image

from transformers import SiglipConfig, SiglipImageProcessor, SiglipModel, SiglipProcessor, SiglipTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
logger = logging.get_logger(__name__)


model_name_to_checkpoint = {
    # base checkpoints
    "siglip-base-patch16-224": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_224_63724782.npz",
    "siglip-base-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_256_60500360.npz",
    "siglip-base-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_384_68578854.npz",
    "siglip-base-patch16-512": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_512_68580893.npz",
    # large checkpoints
    "siglip-large-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_256_60552751.npz",
    "siglip-large-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_384_63634585.npz",
    # multilingual checkpoint
    "siglip-base-patch16-256-i18n": "/Users/nielsrogge/Documents/SigLIP/webli_i18n_b16_256_66117334.npz",
    # so400m checkpoints
    "siglip-so400m-patch14-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_so400m_384_58765454.npz",
}

model_name_to_image_size = {
    "siglip-base-patch16-224": 224,
    "siglip-base-patch16-256": 256,
    "siglip-base-patch16-384": 384,
    "siglip-base-patch16-512": 512,
    "siglip-large-patch16-256": 256,
    "siglip-large-patch16-384": 384,
    "siglip-base-patch16-256-i18n": 256,
    "siglip-so400m-patch14-384": 384,
}


def get_siglip_config(model_name):
    config = SiglipConfig()

    vocab_size = 250000 if "i18n" in model_name else 32000
    image_size = model_name_to_image_size[model_name]
    patch_size = 16 if "patch16" in model_name else 14

    # size of the architecture
    config.vision_config.image_size = image_size
    config.vision_config.patch_size = patch_size
    config.text_config.vocab_size = vocab_size

    if "base" in model_name:
        pass
    elif "large" in model_name:
        config.text_config.hidden_size = 1024
        config.text_config.intermediate_size = 4096
        config.text_config.num_hidden_layers = 24
        config.text_config.num_attention_heads = 16
        config.vision_config.hidden_size = 1024
        config.vision_config.intermediate_size = 4096
        config.vision_config.num_hidden_layers = 24
        config.vision_config.num_attention_heads = 16
    elif "so400m" in model_name:
        config.text_config.hidden_size = 1152
        config.text_config.intermediate_size = 4304
        config.text_config.num_hidden_layers = 27
        config.text_config.num_attention_heads = 16
        config.vision_config.hidden_size = 1152
        config.vision_config.intermediate_size = 4304
        config.vision_config.num_hidden_layers = 27
        config.vision_config.num_attention_heads = 16
    else:
        raise ValueError("Model not supported")

    return config


def create_rename_keys(config):
    rename_keys = []
    # fmt: off

    # vision encoder

    rename_keys.append(("params/img/embedding/kernel", "vision_model.embeddings.patch_embedding.weight"))
    rename_keys.append(("params/img/embedding/bias", "vision_model.embeddings.patch_embedding.bias"))
    rename_keys.append(("params/img/pos_embedding", "vision_model.embeddings.position_embedding.weight"))

    for i in range(config.vision_config.num_hidden_layers):
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/scale", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/scale", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"vision_model.encoder.layers.{i}.self_attn.k_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"vision_model.encoder.layers.{i}.self_attn.k_proj.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"vision_model.encoder.layers.{i}.self_attn.v_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"vision_model.encoder.layers.{i}.self_attn.v_proj.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"vision_model.encoder.layers.{i}.self_attn.q_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"vision_model.encoder.layers.{i}.self_attn.q_proj.bias"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"vision_model.encoder.layers.{i}.self_attn.out_proj.weight"))
        rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"vision_model.encoder.layers.{i}.self_attn.out_proj.bias"))

    rename_keys.append(("params/img/Transformer/encoder_norm/scale", "vision_model.post_layernorm.weight"))
    rename_keys.append(("params/img/Transformer/encoder_norm/bias", "vision_model.post_layernorm.bias"))

    rename_keys.append(("params/img/MAPHead_0/probe", "vision_model.head.probe"))
    rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/scale", "vision_model.head.layernorm.weight"))
    rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/bias", "vision_model.head.layernorm.bias"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/kernel", "vision_model.head.mlp.fc1.weight"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/bias", "vision_model.head.mlp.fc1.bias"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/kernel", "vision_model.head.mlp.fc2.weight"))
    rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/bias", "vision_model.head.mlp.fc2.bias"))
    rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/kernel", "vision_model.head.attention.out_proj.weight"))
    rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/bias", "vision_model.head.attention.out_proj.bias"))

    # text encoder

    rename_keys.append(("params/txt/Embed_0/embedding", "text_model.embeddings.token_embedding.weight"))
    rename_keys.append(("params/txt/pos_embedding", "text_model.embeddings.position_embedding.weight"))

    for i in range(config.text_config.num_hidden_layers):
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/scale", f"text_model.encoder.layers.{i}.layer_norm1.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/bias", f"text_model.encoder.layers.{i}.layer_norm1.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/scale", f"text_model.encoder.layers.{i}.layer_norm2.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/bias", f"text_model.encoder.layers.{i}.layer_norm2.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"text_model.encoder.layers.{i}.mlp.fc1.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"text_model.encoder.layers.{i}.mlp.fc1.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"text_model.encoder.layers.{i}.mlp.fc2.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"text_model.encoder.layers.{i}.mlp.fc2.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"text_model.encoder.layers.{i}.self_attn.k_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"text_model.encoder.layers.{i}.self_attn.k_proj.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"text_model.encoder.layers.{i}.self_attn.v_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"text_model.encoder.layers.{i}.self_attn.v_proj.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"text_model.encoder.layers.{i}.self_attn.q_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"text_model.encoder.layers.{i}.self_attn.q_proj.bias"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"text_model.encoder.layers.{i}.self_attn.out_proj.weight"))
        rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"text_model.encoder.layers.{i}.self_attn.out_proj.bias"))

    rename_keys.append(("params/txt/Encoder_0/encoder_norm/scale", "text_model.final_layer_norm.weight"))
    rename_keys.append(("params/txt/Encoder_0/encoder_norm/bias", "text_model.final_layer_norm.bias"))
    rename_keys.append(("params/txt/head/kernel", "text_model.head.weight"))
    rename_keys.append(("params/txt/head/bias", "text_model.head.bias"))

    # learned temperature and bias
    rename_keys.append(("params/t", "logit_scale"))
    rename_keys.append(("params/b", "logit_bias"))

    # fmt: on
    return rename_keys


def rename_key(dct, old, new, config):
    val = dct.pop(old)

    if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "vision" in new:
        val = val.reshape(-1, config.vision_config.hidden_size)
    if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "text" in new:
        val = val.reshape(-1, config.text_config.hidden_size)

    if "patch_embedding.weight" in new:
        val = val.transpose(3, 2, 0, 1)
    elif new.endswith("weight") and "position_embedding" not in new and "token_embedding" not in new:
        val = val.T

    if "position_embedding" in new and "vision" in new:
        val = val.reshape(-1, config.vision_config.hidden_size)
    if "position_embedding" in new and "text" in new:
        val = val.reshape(-1, config.text_config.hidden_size)

    if new.endswith("bias"):
        val = val.reshape(-1)

    dct[new] = torch.from_numpy(val)


def read_in_q_k_v_head(state_dict, config):
    # read in individual input projection layers
    key_proj_weight = (
        state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/kernel")
        .reshape(-1, config.vision_config.hidden_size)
        .T
    )
    key_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/bias").reshape(-1)
    value_proj_weight = (
        state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/kernel")
        .reshape(-1, config.vision_config.hidden_size)
        .T
    )
    value_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/bias").reshape(-1)
    query_proj_weight = (
        state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/kernel")
        .reshape(-1, config.vision_config.hidden_size)
        .T
    )
    query_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/bias").reshape(-1)

    # next, add them to the state dict as a single matrix + vector
    state_dict["vision_model.head.attention.in_proj_weight"] = torch.from_numpy(
        np.concatenate([query_proj_weight, key_proj_weight, value_proj_weight], axis=0)
    )
    state_dict["vision_model.head.attention.in_proj_bias"] = torch.from_numpy(
        np.concatenate([query_proj_bias, key_proj_bias, value_proj_bias], axis=0)
    )


# We will verify our results on an image of cute cats
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    return image


def flatten_nested_dict(params, parent_key="", sep="/"):
    items = []

    for k, v in params.items():
        new_key = parent_key + sep + k if parent_key else k

        if isinstance(v, collections.abc.MutableMapping):
            items.extend(flatten_nested_dict(v, new_key, sep=sep).items())
        else:
            items.append((new_key, v))
    return dict(items)


@torch.no_grad()
def convert_siglip_checkpoint(model_name, pytorch_dump_folder_path, verify_logits=True, push_to_hub=False):
    """
    Copy/paste/tweak model's weights to our SigLIP structure.
    """

    # define default SigLIP configuration
    config = get_siglip_config(model_name)

    # get checkpoint
    checkpoint = model_name_to_checkpoint[model_name]

    # get vocab file
    if "i18n" in model_name:
        vocab_file = "/Users/nielsrogge/Documents/SigLIP/multilingual_vocab/sentencepiece.model"
    else:
        vocab_file = "/Users/nielsrogge/Documents/SigLIP/english_vocab/sentencepiece.model"

    # load original state dict
    data = load(checkpoint)
    state_dict = flatten_nested_dict(data)

    # remove and rename some keys
    rename_keys = create_rename_keys(config)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest, config)

    # qkv matrices of attention pooling head need special treatment
    read_in_q_k_v_head(state_dict, config)

    # load HuggingFace model
    model = SiglipModel(config).eval()
    model.load_state_dict(state_dict)

    # create processor
    # important: make tokenizer not return attention_mask since original one doesn't require it
    image_size = config.vision_config.image_size
    size = {"height": image_size, "width": image_size}
    image_processor = SiglipImageProcessor(size=size)
    tokenizer = SiglipTokenizer(vocab_file=vocab_file, model_input_names=["input_ids"])
    processor = SiglipProcessor(image_processor=image_processor, tokenizer=tokenizer)

    # verify on dummy images and texts
    url_1 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-ipod.jpg"
    image_1 = Image.open(requests.get(url_1, stream=True).raw).convert("RGB")
    url_2 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-blank.jpg"
    image_2 = Image.open(requests.get(url_2, stream=True).raw).convert("RGB")
    texts = ["an apple", "a picture of an apple"]

    inputs = processor(images=[image_1, image_2], text=texts, return_tensors="pt", padding="max_length")

    # verify input_ids against original ones
    if image_size == 224:
        filename = "siglip_pixel_values.pt"
    elif image_size == 256:
        filename = "siglip_pixel_values_256.pt"
    elif image_size == 384:
        filename = "siglip_pixel_values_384.pt"
    elif image_size == 512:
        filename = "siglip_pixel_values_512.pt"
    else:
        raise ValueError("Image size not supported")

    filepath = hf_hub_download(repo_id="nielsr/test-image", filename=filename, repo_type="dataset")
    original_pixel_values = torch.load(filepath)
    filepath = hf_hub_download(repo_id="nielsr/test-image", filename="siglip_input_ids.pt", repo_type="dataset")
    original_input_ids = torch.load(filepath)

    if "i18n" not in model_name:
        assert inputs.input_ids.tolist() == original_input_ids.tolist()

    print("Mean of original pixel values:", original_pixel_values.mean())
    print("Mean of new pixel values:", inputs.pixel_values.mean())

    # note: we're testing with original pixel values here since we don't have exact pixel values
    with torch.no_grad():
        outputs = model(input_ids=inputs.input_ids, pixel_values=original_pixel_values)

    # with torch.no_grad():
    #     outputs = model(input_ids=inputs.input_ids, pixel_values=inputs.pixel_values)

    print(outputs.logits_per_image[:3, :3])

    probs = torch.sigmoid(outputs.logits_per_image)  # these are the probabilities
    print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
    print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'")

    if verify_logits:
        if model_name == "siglip-base-patch16-224":
            expected_slice = torch.tensor(
                [[-2.9621, -2.1672], [-0.2713, 0.2910]],
            )
        elif model_name == "siglip-base-patch16-256":
            expected_slice = torch.tensor(
                [[-3.1146, -1.9894], [-0.7312, 0.6387]],
            )
        elif model_name == "siglip-base-patch16-384":
            expected_slice = torch.tensor(
                [[-2.8098, -2.1891], [-0.4242, 0.4102]],
            )
        elif model_name == "siglip-base-patch16-512":
            expected_slice = torch.tensor(
                [[-2.7899, -2.2668], [-0.4295, -0.0735]],
            )
        elif model_name == "siglip-large-patch16-256":
            expected_slice = torch.tensor(
                [[-1.5827, -0.5801], [-0.9153, 0.1363]],
            )
        elif model_name == "siglip-large-patch16-384":
            expected_slice = torch.tensor(
                [[-2.1523, -0.2899], [-0.2959, 0.7884]],
            )
        elif model_name == "siglip-so400m-patch14-384":
            expected_slice = torch.tensor([[-1.2441, -0.6649], [-0.7060, 0.7374]])
        elif model_name == "siglip-base-patch16-256-i18n":
            expected_slice = torch.tensor(
                [[-0.9064, 0.1073], [-0.0299, 0.5304]],
            )

        assert torch.allclose(outputs.logits_per_image[:3, :3], expected_slice, atol=1e-4)
        print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        print(f"Saving processor to {pytorch_dump_folder_path}")
        processor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        model.push_to_hub(f"nielsr/{model_name}")
        processor.push_to_hub(f"nielsr/{model_name}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="siglip-base-patch16-224",
        type=str,
        choices=model_name_to_checkpoint.keys(),
        help="Name of the model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--verify_logits",
        action="store_false",
        help="Whether to verify logits against the original implementation.",
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    args = parser.parse_args()
    convert_siglip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)