





"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False							)		->       Tuple:






       """simple docstring"""




       _UpperCAmelCase     =     []
       for i in range(config.num_hidden_layers							):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")							)
              rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")							)
              rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")							)
              rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")							)
              rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")							)
              rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")							)
              rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")							)
              rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")							)
              rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")							)
              rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")							)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ('cls_token', 'vit.embeddings.cls_token'),
               ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
               ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
               ('pos_embed', 'vit.embeddings.position_embeddings'),
           ]							)

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ('norm.weight', 'layernorm.weight'),
                      ('norm.bias', 'layernorm.bias'),
                      ('pre_logits.fc.weight', 'pooler.dense.weight'),
                      ('pre_logits.fc.bias', 'pooler.dense.bias'),
                  ]							)

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              _UpperCAmelCase     =     [(pair[0], pair[1][4:]) if pair[1].startswith('vit'							) else pair for pair in rename_keys]
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ('norm.weight', 'vit.layernorm.weight'),
                      ('norm.bias', 'vit.layernorm.bias'),
                      ('head.weight', 'classifier.weight'),
                      ('head.bias', 'classifier.bias'),
                  ]							)

       return rename_keys

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False							)		->       Optional[Any]:






       """simple docstring"""




       for i in range(config.num_hidden_layers							):
              if base_model:
                     _UpperCAmelCase     =     ''
              else:
                     _UpperCAmelCase     =     'vit.'
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              _UpperCAmelCase     =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""							)
              _UpperCAmelCase     =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""							)
              # next, add query, keys and values (in that order) to the state dict
              _UpperCAmelCase     =     in_proj_weight[
                  : config.hidden_size, :
              ]
              _UpperCAmelCase     =     in_proj_bias[: config.hidden_size]
              _UpperCAmelCase     =     in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _UpperCAmelCase     =     in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              _UpperCAmelCase     =     in_proj_weight[
                  -config.hidden_size :, :
              ]
              _UpperCAmelCase     =     in_proj_bias[-config.hidden_size :]

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     ['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     dct.pop(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     val

def       __lowerCamelCase    (       )		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     'http://images.cocodataset.org/val2017/000000039769.jpg'
       _UpperCAmelCase     =     Image.open(requests.get(SCREAMING_SNAKE_CASE,stream=SCREAMING_SNAKE_CASE							).raw							)
       return im

@torch.no_grad()
def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




       _UpperCAmelCase     =     ViTConfig()
       _UpperCAmelCase     =     False
       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       if vit_name[-5:] == "in21k":
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     int(vit_name[-12:-10]							)
              _UpperCAmelCase     =     int(vit_name[-9:-6]							)
       else:
              _UpperCAmelCase     =     1000
              _UpperCAmelCase     =     'huggingface/label-files'
              _UpperCAmelCase     =     'imagenet-1k-id2label.json'
              _UpperCAmelCase     =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,repo_type='dataset'							),'r'							)							)
              _UpperCAmelCase     =     {int(SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
              _UpperCAmelCase     =     idalabel
              _UpperCAmelCase     =     {v: k for k, v in idalabel.items()}
              _UpperCAmelCase     =     int(vit_name[-6:-4]							)
              _UpperCAmelCase     =     int(vit_name[-3:]							)
       # size of the architecture
       if "deit" in vit_name:
              if vit_name[9:].startswith('tiny'							):
                     _UpperCAmelCase     =     192
                     _UpperCAmelCase     =     768
                     _UpperCAmelCase     =     12
                     _UpperCAmelCase     =     3
              elif vit_name[9:].startswith('small'							):
                     _UpperCAmelCase     =     384
                     _UpperCAmelCase     =     1536
                     _UpperCAmelCase     =     12
                     _UpperCAmelCase     =     6
              else:
                     pass
       else:
              if vit_name[4:].startswith('small'							):
                     _UpperCAmelCase     =     768
                     _UpperCAmelCase     =     2304
                     _UpperCAmelCase     =     8
                     _UpperCAmelCase     =     8
              elif vit_name[4:].startswith('base'							):
                     pass
              elif vit_name[4:].startswith('large'							):
                     _UpperCAmelCase     =     1024
                     _UpperCAmelCase     =     4096
                     _UpperCAmelCase     =     24
                     _UpperCAmelCase     =     16
              elif vit_name[4:].startswith('huge'							):
                     _UpperCAmelCase     =     1280
                     _UpperCAmelCase     =     5120
                     _UpperCAmelCase     =     32
                     _UpperCAmelCase     =     16

    # load original model from timm
       _UpperCAmelCase     =     timm.create_model(SCREAMING_SNAKE_CASE,pretrained=SCREAMING_SNAKE_CASE							)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       _UpperCAmelCase     =     timm_model.state_dict()
       if base_model:
              remove_classification_head_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     create_rename_keys(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       for src, dest in rename_keys:
              rename_key(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       read_in_q_k_v(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              _UpperCAmelCase     =     ViTModel(SCREAMING_SNAKE_CASE							).eval()
       else:
              _UpperCAmelCase     =     ViTForImageClassification(SCREAMING_SNAKE_CASE							).eval()
       model.load_state_dict(SCREAMING_SNAKE_CASE							)

       # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
       if "deit" in vit_name:
              _UpperCAmelCase     =     DeiTImageProcessor(size=config.image_size							)
       else:
              _UpperCAmelCase     =     ViTImageProcessor(size=config.image_size							)
       _UpperCAmelCase     =     image_processor(images=prepare_img(),return_tensors='pt'							)
       _UpperCAmelCase     =     encoding['pixel_values']
       _UpperCAmelCase     =     model(SCREAMING_SNAKE_CASE							)

       if base_model:
              _UpperCAmelCase     =     timm_model.forward_features(SCREAMING_SNAKE_CASE							)
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(SCREAMING_SNAKE_CASE,outputs.pooler_output,atol=1E-3							)
       else:
              _UpperCAmelCase     =     timm_model(SCREAMING_SNAKE_CASE							)
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(SCREAMING_SNAKE_CASE,outputs.logits,atol=1E-3							)

       Path(SCREAMING_SNAKE_CASE							).mkdir(exist_ok=SCREAMING_SNAKE_CASE							)
       print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""							)
       model.save_pretrained(SCREAMING_SNAKE_CASE							)
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
    lowerCAmelCase_			=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--vit_name''',
        default='''vit_base_patch16_224''',
        type=str,
        help='''Name of the ViT timm 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.'''
    )

    lowerCAmelCase_			=							parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)













"""simple docstring"""






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_			=							{'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_			=							['''TimmBackbone''']


if TYPE_CHECKING:
    from .configuration_timm_backbone import TimmBackboneConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timm_backbone import TimmBackbone

else:
    import sys

    lowerCAmelCase_			=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






