


"""simple docstring"""



import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  str=False			)     ->						Optional[int]:


    """simple docstring"""





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

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

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ("module.norm.weight", "layernorm.weight"),
                ("module.norm.bias", "layernorm.bias"),
            ]			)

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        snake_case   :				Optional[int]				 =   [(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						(						lowercase  :  List[Any]						,							lowercase  :  Optional[Any]						,							lowercase  :  List[Any]=False			)     ->						List[str]:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]			)     ->						Tuple:


    """simple docstring"""





    snake_case   :				Optional[int]				 =   ["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(lowercase						,							lowercase			)







def   __lowerCAmelCase						(						lowercase  :  Tuple			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				List[str]				 =   [
        "module.fc.fc1.weight",
        "module.fc.fc1.bias",
        "module.fc.bn1.weight",
        "module.fc.bn1.bias",
        "module.fc.bn1.running_mean",
        "module.fc.bn1.running_var",
        "module.fc.bn1.num_batches_tracked",
        "module.fc.fc2.weight",
        "module.fc.fc2.bias",
        "module.fc.bn2.weight",
        "module.fc.bn2.bias",
        "module.fc.bn2.running_mean",
        "module.fc.bn2.running_var",
        "module.fc.bn2.num_batches_tracked",
        "module.fc.fc3.weight",
        "module.fc.fc3.bias",
    ]
    for k in ignore_keys:
        state_dict.pop(lowercase						,							lowercase			)







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Any						,							lowercase  :  Any			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				List[Any]				 =   dct.pop(lowercase			)
    snake_case   :				Dict				 =   val







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Tuple			)     ->						List[str]:


    """simple docstring"""





    snake_case   :				Optional[int]				 =   ViTMSNConfig()
    snake_case   :				Any				 =   1000

    snake_case   :				Any				 =   "datasets/huggingface/label-files"
    snake_case   :				List[str]				 =   "imagenet-1k-id2label.json"
    snake_case   :				Optional[int]				 =   json.load(open(hf_hub_download(lowercase						,							lowercase			)						,							"r"			)			)
    snake_case   :				Any				 =   {int(lowercase			): v for k, v in idalabel.items()}
    snake_case   :				List[Any]				 =   idalabel
    snake_case   :				List[Any]				 =   {v: k for k, v in idalabel.items()}

    if "s16" in checkpoint_url:
        snake_case   :				Union[str, Any]				 =   384
        snake_case   :				List[Any]				 =   1536
        snake_case   :				Optional[Any]				 =   6
    elif "l16" in checkpoint_url:
        snake_case   :				List[Any]				 =   1024
        snake_case   :				Optional[int]				 =   4096
        snake_case   :				Union[str, Any]				 =   24
        snake_case   :				Dict				 =   16
        snake_case   :				Optional[int]				 =   0.1
    elif "b4" in checkpoint_url:
        snake_case   :				Dict				 =   4
    elif "l7" in checkpoint_url:
        snake_case   :				Union[str, Any]				 =   7
        snake_case   :				str				 =   1024
        snake_case   :				Dict				 =   4096
        snake_case   :				Optional[int]				 =   24
        snake_case   :				Optional[int]				 =   16
        snake_case   :				Any				 =   0.1

    snake_case   :				List[Any]				 =   ViTMSNModel(lowercase			)

    snake_case   :				int				 =   torch.hub.load_state_dict_from_url(lowercase						,							map_location="cpu"			)["target_encoder"]

    snake_case   :				Dict				 =   ViTImageProcessor(size=config.image_size			)

    remove_projection_head(lowercase			)
    snake_case   :				List[Any]				 =   create_rename_keys(lowercase						,							base_model=lowercase			)

    for src, dest in rename_keys:
        rename_key(lowercase						,							lowercase						,							lowercase			)
    read_in_q_k_v(lowercase						,							lowercase						,							base_model=lowercase			)

    model.load_state_dict(lowercase			)
    model.eval()

    snake_case   :				Optional[Any]				 =   "http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case   :				Dict				 =   Image.open(requests.get(lowercase						,							stream=lowercase			).raw			)
    snake_case   :				int				 =   ViTImageProcessor(
        size=config.image_size						,							image_mean=lowercase						,							image_std=lowercase			)
    snake_case   :				Optional[int]				 =   image_processor(images=lowercase						,							return_tensors="pt"			)

    # forward pass
    torch.manual_seed(2			)
    snake_case   :				Optional[Any]				 =   model(**lowercase			)
    snake_case   :				str				 =   outputs.last_hidden_state

    # The following Colab Notebook was used to generate these outputs:
    # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
    if "s16" in checkpoint_url:
        snake_case   :				str				 =   torch.tensor([[-1.0915, -1.4876, -1.1809]]			)
    elif "b16" in checkpoint_url:
        snake_case   :				Dict				 =   torch.tensor([[14.2889, -18.9045, 11.7281]]			)
    elif "l16" in checkpoint_url:
        snake_case   :				Union[str, Any]				 =   torch.tensor([[41.5028, -22.8681, 45.6475]]			)
    elif "b4" in checkpoint_url:
        snake_case   :				int				 =   torch.tensor([[-4.3868, 5.2932, -0.4137]]			)
    else:
        snake_case   :				Optional[int]				 =   torch.tensor([[-0.1792, -0.6465, 2.4263]]			)

    # verify logits
    assert torch.allclose(last_hidden_state[:, 0, :3]						,							lowercase						,							atol=1e-4			)

    print(F'Saving model to {pytorch_dump_folder_path}'			)
    model.save_pretrained(lowercase			)

    print(F'Saving image processor to {pytorch_dump_folder_path}'			)
    image_processor.save_pretrained(lowercase			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  __snake_case             =   parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case             =   {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["""ConditionalDetrFeatureExtractor"""]
  __snake_case             =   ["""ConditionalDetrImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConditionalDetrForObjectDetection""",
      """ConditionalDetrForSegmentation""",
      """ConditionalDetrModel""",
      """ConditionalDetrPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

  __snake_case             =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





