'''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 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Optional[Any]=False    )    ->    Any:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[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"
              _UpperCAmelCase     :  str							      =    [(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 	UpperCamelCase_							(					_UpperCAmelCase  :      int			,	_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Dict=False    )    ->    Optional[Any]:


       """simple docstring"""







       for i in range(config.num_hidden_layers    ):
              if base_model:
                     _UpperCAmelCase     :  int							      =    ""
              else:
                     _UpperCAmelCase     :  Any							      =    "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     :  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
              _UpperCAmelCase     :  List[Any]							      =    in_proj_weight[
                  : config.hidden_size, :
              ]
              _UpperCAmelCase     :  List[str]							      =    in_proj_bias[: config.hidden_size]
              _UpperCAmelCase     :  Any							      =    in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _UpperCAmelCase     :  str							      =    in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              _UpperCAmelCase     :  Optional[Any]							      =    in_proj_weight[
                  -config.hidden_size :, :
              ]
              _UpperCAmelCase     :  List[str]							      =    in_proj_bias[-config.hidden_size :]

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]    )    ->    Optional[int]:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  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(_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Tuple    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    dct.pop(_UpperCAmelCase    )
       _UpperCAmelCase     :  int							      =    val

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Dict    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    ViTMSNConfig()
       _UpperCAmelCase     :  Optional[int]							      =    1_000

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

       if "s16" in checkpoint_url:
              _UpperCAmelCase     :  Dict							      =    384
              _UpperCAmelCase     :  Any							      =    1_536
              _UpperCAmelCase     :  str							      =    6
       elif "l16" in checkpoint_url:
              _UpperCAmelCase     :  Union[str, Any]							      =    1_024
              _UpperCAmelCase     :  Any							      =    4_096
              _UpperCAmelCase     :  List[Any]							      =    24
              _UpperCAmelCase     :  Tuple							      =    16
              _UpperCAmelCase     :  Optional[Any]							      =    0.1
       elif "b4" in checkpoint_url:
              _UpperCAmelCase     :  Optional[Any]							      =    4
       elif "l7" in checkpoint_url:
              _UpperCAmelCase     :  List[str]							      =    7
              _UpperCAmelCase     :  Optional[Any]							      =    1_024
              _UpperCAmelCase     :  List[str]							      =    4_096
              _UpperCAmelCase     :  Optional[int]							      =    24
              _UpperCAmelCase     :  Union[str, Any]							      =    16
              _UpperCAmelCase     :  Optional[int]							      =    0.1

       _UpperCAmelCase     :  Tuple							      =    ViTMSNModel(_UpperCAmelCase    )

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

       _UpperCAmelCase     :  List[Any]							      =    ViTImageProcessor(size=config.image_size    )

       remove_projection_head(_UpperCAmelCase    )
       _UpperCAmelCase     :  str							      =    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     :  Any							      =    "http://images.cocodataset.org/val2017/000000039769.jpg"

       _UpperCAmelCase     :  List[Any]							      =    Image.open(requests.get(_UpperCAmelCase			,	stream=_UpperCAmelCase    ).raw    )
       _UpperCAmelCase     :  Any							      =    ViTImageProcessor(
           size=config.image_size			,	image_mean=_UpperCAmelCase			,	image_std=_UpperCAmelCase    )
       _UpperCAmelCase     :  Dict							      =    image_processor(images=_UpperCAmelCase			,	return_tensors="pt"    )

       # forward pass
       torch.manual_seed(2    )
       _UpperCAmelCase     :  Tuple							      =    model(**_UpperCAmelCase    )
       _UpperCAmelCase     :  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:
              _UpperCAmelCase     :  Optional[int]							      =    torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]    )
       elif "b16" in checkpoint_url:
              _UpperCAmelCase     :  List[str]							      =    torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]    )
       elif "l16" in checkpoint_url:
              _UpperCAmelCase     :  Optional[int]							      =    torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]    )
       elif "b4" in checkpoint_url:
              _UpperCAmelCase     :  List[str]							      =    torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]    )
       else:
              _UpperCAmelCase     :  str							      =    torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]    )

       # 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__":
  __SCREAMING_SNAKE_CASE       :     Any     =  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."""
  )

  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


'''simple docstring'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







       _validation(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       # Creates data structures and fill initial step
       _UpperCAmelCase     :  dict							      =    {}
       _UpperCAmelCase     :  dict							      =    {}
       for state in states_space:
              _UpperCAmelCase     :  Union[str, Any]							      =    observations_space[0]
              _UpperCAmelCase     :  Tuple							      =    (
                  initial_probabilities[state] * emission_probabilities[state][observation]
              )
              _UpperCAmelCase     :  List[str]							      =    None

       # Fills the data structure with the probabilities of
       # different transitions and pointers to previous states
       for o in range(1			,	len(_UpperCAmelCase    )    ):
              _UpperCAmelCase     :  Optional[Any]							      =    observations_space[o]
              _UpperCAmelCase     :  int							      =    observations_space[o - 1]
              for state in states_space:
                     # Calculates the argmax for probability function
                     _UpperCAmelCase     :  str							      =    ""
                     _UpperCAmelCase     :  Tuple							      =    -1
                     for k_state in states_space:
                            _UpperCAmelCase     :  Any							      =    (
                                probabilities[(k_state, prior_observation)]
                                * transition_probabilities[k_state][state]
                                * emission_probabilities[state][observation]
                            )
                            if probability > max_probability:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    probability
                                   _UpperCAmelCase     :  str							      =    k_state

            # Update probabilities and pointers dicts
                     _UpperCAmelCase     :  Optional[int]							      =    (
                         probabilities[(arg_max, prior_observation)]
                         * transition_probabilities[arg_max][state]
                         * emission_probabilities[state][observation]
                     )

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

       # argmax for given final observation
       _UpperCAmelCase     :  List[str]							      =    ""
       _UpperCAmelCase     :  Any							      =    -1
       for k_state in states_space:
              _UpperCAmelCase     :  Optional[int]							      =    probabilities[(k_state, final_observation)]
              if probability > max_probability:
                     _UpperCAmelCase     :  int							      =    probability
                     _UpperCAmelCase     :  Dict							      =    k_state
       _UpperCAmelCase     :  Dict							      =    arg_max

       # Process pointers backwards
       _UpperCAmelCase     :  List[Any]							      =    last_state
       _UpperCAmelCase     :  str							      =    []
       for o in range(len(_UpperCAmelCase    ) - 1			,	-1			,	-1    ):
              result.append(_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    pointers[previous, observations_space[o]]
       result.reverse()

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       if not all(
           [
               observations_space,
               states_space,
               initial_probabilities,
               transition_probabilities,
               emission_probabilities,
           ]    ):
              raise ValueError("There's an empty parameter"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _validate_dict(_object			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       for x in _object.values():
              _validate_dict(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()

