





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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]   )   ->			int:
				UpperCamelCase__							:       Optional[int]      =   ['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(__UpperCAmelCase	,     __UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			str:
				# projection head is used in the self-supervised pre-training in MSN,
				# for downstream task it's not needed.
				UpperCamelCase__							:       Optional[int]      =   [
				    '''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:    List[str]	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Union[str, Any]   )   ->			Dict:
				UpperCamelCase__							:       Optional[Any]      =   dct.pop(__UpperCAmelCase   )
				UpperCamelCase__							:       int      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    str   )   ->			str:
				UpperCamelCase__							:       List[Any]      =   ViTMSNConfig()
				UpperCamelCase__							:       Union[str, Any]      =   1000

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

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

				UpperCamelCase__							:       Union[str, Any]      =   ViTMSNModel(__UpperCAmelCase   )

				UpperCamelCase__							:       Dict      =   torch.hub.load_state_dict_from_url(__UpperCAmelCase	,     map_location='''cpu'''   )['''target_encoder''']

				UpperCamelCase__							:       Tuple      =   ViTImageProcessor(size=config.image_size   )

				remove_projection_head(__UpperCAmelCase   )
				UpperCamelCase__							:       Tuple      =   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()

				UpperCamelCase__							:       Optional[Any]      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''

				UpperCamelCase__							:       List[str]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
				UpperCamelCase__							:       List[str]      =   ViTImageProcessor(
				    size=config.image_size	,     image_mean=__UpperCAmelCase	,     image_std=__UpperCAmelCase   )
				UpperCamelCase__							:       List[Any]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				UpperCamelCase__							:       Tuple      =   model(**__UpperCAmelCase   )
				UpperCamelCase__							:       Optional[Any]      =   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:
								UpperCamelCase__							:       Union[str, Any]      =   torch.tensor([[-1.0915, -1.4876, -1.1809]]   )
				elif "b16" in checkpoint_url:
								UpperCamelCase__							:       Optional[Any]      =   torch.tensor([[14.2889, -18.9045, 11.7281]]   )
				elif "l16" in checkpoint_url:
								UpperCamelCase__							:       Optional[int]      =   torch.tensor([[41.5028, -22.8681, 45.6475]]   )
				elif "b4" in checkpoint_url:
								UpperCamelCase__							:       Dict      =   torch.tensor([[-4.3868, 5.2932, -0.4137]]   )
				else:
								UpperCamelCase__							:       Dict      =   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__":
		UpperCAmelCase_  							=				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.'
		)

		UpperCAmelCase_  							=				parser.parse_args()
		convert_vit_msn_checkpoint(args.checkpoint_url, 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


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      '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

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

