




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



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Tuple		       =     ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(UpperCamelCase	,      UpperCamelCase      )



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[Any]:
  # projection head is used in the self-supervised pre-training in MSN,
  # for downstream task it's not needed.
  lowerCAmelCase__				:  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(UpperCamelCase	,      UpperCamelCase      )



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Optional[Any]		       =     dct.pop(UpperCamelCase      )
  lowerCAmelCase__				:  str		       =     val



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Any:
  lowerCAmelCase__				:  List[str]		       =     ViTMSNConfig()
  lowerCAmelCase__				:  Dict		       =     1000

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

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

  lowerCAmelCase__				:  Union[str, Any]		       =     ViTMSNModel(UpperCamelCase      )

  lowerCAmelCase__				:  Union[str, Any]		       =     torch.hub.load_state_dict_from_url(UpperCamelCase	,      map_location='''cpu'''      )['''target_encoder''']

  lowerCAmelCase__				:  List[str]		       =     ViTImageProcessor(size=config.image_size      )

  remove_projection_head(UpperCamelCase      )
  lowerCAmelCase__				:  Union[str, Any]		       =     create_rename_keys(UpperCamelCase	,      base_model=UpperCamelCase      )

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

  model.load_state_dict(UpperCamelCase      )
  model.eval()

  lowerCAmelCase__				:  Any		       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''

  lowerCAmelCase__				:  Union[str, Any]		       =     Image.open(requests.get(UpperCamelCase	,      stream=UpperCamelCase      ).raw      )
  lowerCAmelCase__				:  int		       =     ViTImageProcessor(
      size=config.image_size	,      image_mean=UpperCamelCase	,      image_std=UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     image_processor(images=UpperCamelCase	,      return_tensors='''pt'''      )

  # forward pass
  torch.manual_seed(2      )
  lowerCAmelCase__				:  str		       =     model(**UpperCamelCase      )
  lowerCAmelCase__				:  int		       =     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:
    lowerCAmelCase__				:  Optional[int]		       =     torch.tensor([[-1.0915, -1.4876, -1.1809]]      )
  elif "b16" in checkpoint_url:
    lowerCAmelCase__				:  Union[str, Any]		       =     torch.tensor([[14.2889, -18.9045, 11.7281]]      )
  elif "l16" in checkpoint_url:
    lowerCAmelCase__				:  Any		       =     torch.tensor([[41.5028, -22.8681, 45.6475]]      )
  elif "b4" in checkpoint_url:
    lowerCAmelCase__				:  str		       =     torch.tensor([[-4.3868, 5.2932, -0.4137]]      )
  else:
    lowerCAmelCase__				:  Any		       =     torch.tensor([[-0.1792, -0.6465, 2.4263]]      )

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

  print(F"""Saving model to {pytorch_dump_folder_path}"""      )
  model.save_pretrained(UpperCamelCase      )

  print(F"""Saving image processor to {pytorch_dump_folder_path}"""      )
  image_processor.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
     lowerCAmelCase_					    = 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."""
     )

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_					    = {
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = ["""LongformerTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """LongformerForMaskedLM""",
         """LongformerForMultipleChoice""",
         """LongformerForQuestionAnswering""",
         """LongformerForSequenceClassification""",
         """LongformerForTokenClassification""",
         """LongformerModel""",
         """LongformerPreTrainedModel""",
         """LongformerSelfAttention""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFLongformerForMaskedLM""",
         """TFLongformerForMultipleChoice""",
         """TFLongformerForQuestionAnswering""",
         """TFLongformerForSequenceClassification""",
         """TFLongformerForTokenClassification""",
         """TFLongformerModel""",
         """TFLongformerPreTrainedModel""",
         """TFLongformerSelfAttention""",
     ]


if TYPE_CHECKING:
     from .configuration_longformer import (
         LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LongformerConfig,
         LongformerOnnxConfig,
     )
     from .tokenization_longformer import LongformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_longformer_fast import LongformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_longformer import (
              LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              LongformerForMaskedLM,
              LongformerForMultipleChoice,
              LongformerForQuestionAnswering,
              LongformerForSequenceClassification,
              LongformerForTokenClassification,
              LongformerModel,
              LongformerPreTrainedModel,
              LongformerSelfAttention,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_longformer import (
              TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLongformerForMaskedLM,
              TFLongformerForMultipleChoice,
              TFLongformerForQuestionAnswering,
              TFLongformerForSequenceClassification,
              TFLongformerForTokenClassification,
              TFLongformerModel,
              TFLongformerPreTrainedModel,
              TFLongformerSelfAttention,
          )

else:
     import sys

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

