


'''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     :  int											=    logging.get_logger(__name__)






def       lowercase					(_A ,    _A=False       ):







       """simple docstring"""







       _lowerCAmelCase     :				str       =							[]
       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"
              _lowerCAmelCase     :				Optional[Any]       =							[(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       lowercase					(_A ,    _A ,    _A=False       ):







       """simple docstring"""







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






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(__lowerCAmelCase ,    __lowerCAmelCase       )






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							dct.pop(__lowerCAmelCase       )
       _lowerCAmelCase     :				List[str]       =							val






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				List[str]       =							'http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCAmelCase     :				Optional[Any]       =							Image.open(requests.get(__lowerCAmelCase ,    stream=__lowerCAmelCase       ).raw       )
       return im






@torch.no_grad()
def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							ViTConfig()
       _lowerCAmelCase     :				Any       =							False
       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       if vit_name[-5:] == "in21k":
              _lowerCAmelCase     :				List[str]       =							True
              _lowerCAmelCase     :				List[str]       =							int(vit_name[-1_2:-1_0]       )
              _lowerCAmelCase     :				List[str]       =							int(vit_name[-9:-6]       )
       else:
              _lowerCAmelCase     :				List[Any]       =							1_0_0_0
              _lowerCAmelCase     :				Any       =							'huggingface/label-files'
              _lowerCAmelCase     :				Tuple       =							'imagenet-1k-id2label.json'
              _lowerCAmelCase     :				List[str]       =							json.load(open(hf_hub_download(__lowerCAmelCase ,    __lowerCAmelCase ,    repo_type='dataset'       ) ,    'r'       )       )
              _lowerCAmelCase     :				List[str]       =							{int(__lowerCAmelCase       ): v for k, v in idalabel.items()}
              _lowerCAmelCase     :				List[str]       =							idalabel
              _lowerCAmelCase     :				List[str]       =							{v: k for k, v in idalabel.items()}
              _lowerCAmelCase     :				int       =							int(vit_name[-6:-4]       )
              _lowerCAmelCase     :				Dict       =							int(vit_name[-3:]       )
       # size of the architecture
       if "deit" in vit_name:
              if vit_name[9:].startswith('tiny'       ):
                     _lowerCAmelCase     :				Tuple       =							1_9_2
                     _lowerCAmelCase     :				Dict       =							7_6_8
                     _lowerCAmelCase     :				Any       =							1_2
                     _lowerCAmelCase     :				List[str]       =							3
              elif vit_name[9:].startswith('small'       ):
                     _lowerCAmelCase     :				str       =							3_8_4
                     _lowerCAmelCase     :				int       =							1_5_3_6
                     _lowerCAmelCase     :				List[Any]       =							1_2
                     _lowerCAmelCase     :				Optional[Any]       =							6
              else:
                     pass
       else:
              if vit_name[4:].startswith('small'       ):
                     _lowerCAmelCase     :				str       =							7_6_8
                     _lowerCAmelCase     :				int       =							2_3_0_4
                     _lowerCAmelCase     :				Any       =							8
                     _lowerCAmelCase     :				Any       =							8
              elif vit_name[4:].startswith('base'       ):
                     pass
              elif vit_name[4:].startswith('large'       ):
                     _lowerCAmelCase     :				int       =							1_0_2_4
                     _lowerCAmelCase     :				Optional[Any]       =							4_0_9_6
                     _lowerCAmelCase     :				Union[str, Any]       =							2_4
                     _lowerCAmelCase     :				Any       =							1_6
              elif vit_name[4:].startswith('huge'       ):
                     _lowerCAmelCase     :				Optional[Any]       =							1_2_8_0
                     _lowerCAmelCase     :				Dict       =							5_1_2_0
                     _lowerCAmelCase     :				Any       =							3_2
                     _lowerCAmelCase     :				Tuple       =							1_6

              # load original model from timm
       _lowerCAmelCase     :				Union[str, Any]       =							timm.create_model(__lowerCAmelCase ,    pretrained=__lowerCAmelCase       )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       _lowerCAmelCase     :				Optional[Any]       =							timm_model.state_dict()
       if base_model:
              remove_classification_head_(__lowerCAmelCase       )
       _lowerCAmelCase     :				Dict       =							create_rename_keys(__lowerCAmelCase ,    __lowerCAmelCase       )
       for src, dest in rename_keys:
              rename_key(__lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase       )
       read_in_q_k_v(__lowerCAmelCase ,    __lowerCAmelCase ,    __lowerCAmelCase       )

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

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

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

       Path(__lowerCAmelCase       ).mkdir(exist_ok=__lowerCAmelCase       )
       print(f'Saving model {vit_name} to {pytorch_dump_folder_path}'       )
       model.save_pretrained(__lowerCAmelCase       )
       print(f'Saving image processor to {pytorch_dump_folder_path}'       )
       image_processor.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
       lowerCAmelCase     :  Tuple											=    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     :  Optional[int]											=    parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__magic_name__  :  Optional[Any]					=       {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InformerForPrediction""",
          """InformerModel""",
          """InformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_informer import (
                INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                InformerForPrediction,
                InformerModel,
                InformerPreTrainedModel,
            )

else:
      import sys

      __magic_name__  :  Any					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

