'''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       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  Union[str, Any]=False					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      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_            =  [(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  :  Tuple							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Any=False					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(_UpperCamelCase							,    _UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  [
          '''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  :  Optional[Any]							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Tuple					):


      '''simple docstring'''


      UpperCAmelCase_            =  dct.pop(_UpperCamelCase					)
      UpperCAmelCase_            =  val



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ViTMSNConfig()
      UpperCAmelCase_            =  1000

      UpperCAmelCase_            =  '''datasets/huggingface/label-files'''
      UpperCAmelCase_            =  '''imagenet-1k-id2label.json'''
      UpperCAmelCase_            =  json.load(open(hf_hub_download(_UpperCamelCase							,    _UpperCamelCase					)							,    '''r'''					)					)
      UpperCAmelCase_            =  {int(_UpperCamelCase					): v for k, v in idalabel.items()}
      UpperCAmelCase_            =  idalabel
      UpperCAmelCase_            =  {v: k for k, v in idalabel.items()}

      if "s16" in checkpoint_url:
            UpperCAmelCase_            =  384
            UpperCAmelCase_            =  1536
            UpperCAmelCase_            =  6
      elif "l16" in checkpoint_url:
            UpperCAmelCase_            =  1024
            UpperCAmelCase_            =  4096
            UpperCAmelCase_            =  24
            UpperCAmelCase_            =  16
            UpperCAmelCase_            =  0.1
      elif "b4" in checkpoint_url:
            UpperCAmelCase_            =  4
      elif "l7" in checkpoint_url:
            UpperCAmelCase_            =  7
            UpperCAmelCase_            =  1024
            UpperCAmelCase_            =  4096
            UpperCAmelCase_            =  24
            UpperCAmelCase_            =  16
            UpperCAmelCase_            =  0.1

      UpperCAmelCase_            =  ViTMSNModel(_UpperCamelCase					)

      UpperCAmelCase_            =  torch.hub.load_state_dict_from_url(_UpperCamelCase							,    map_location='''cpu'''					)['''target_encoder''']

      UpperCAmelCase_            =  ViTImageProcessor(size=config.image_size					)

      remove_projection_head(_UpperCamelCase					)
      UpperCAmelCase_            =  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_            =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

      UpperCAmelCase_            =  Image.open(requests.get(_UpperCamelCase							,    stream=_UpperCamelCase					).raw					)
      UpperCAmelCase_            =  ViTImageProcessor(
          size=config.image_size							,    image_mean=_UpperCamelCase							,    image_std=_UpperCamelCase					)
      UpperCAmelCase_            =  image_processor(images=_UpperCamelCase							,    return_tensors='''pt'''					)

      # forward pass
      torch.manual_seed(2					)
      UpperCAmelCase_            =  model(**_UpperCamelCase					)
      UpperCAmelCase_            =  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_            =  torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]					)
      elif "b16" in checkpoint_url:
            UpperCAmelCase_            =  torch.tensor([[14.2_889, -18.9_045, 11.7_281]]					)
      elif "l16" in checkpoint_url:
            UpperCAmelCase_            =  torch.tensor([[41.5_028, -22.8_681, 45.6_475]]					)
      elif "b4" in checkpoint_url:
            UpperCAmelCase_            =  torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]					)
      else:
            UpperCAmelCase_            =  torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]					)

      # 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__":
      lowercase__  :      Dict        =					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."
      )

      lowercase__  :      str        =					parser.parse_args()
      convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


lowercase__  :      Optional[int]        =					logging.get_logger(__name__)



def  __lowerCamelCase       (      _UpperCamelCase  :  np.ndarray							,    _UpperCamelCase  :  Union[int, Iterable[int]]							,    _UpperCamelCase  :  bool							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      def constraint_to_multiple_of(_UpperCamelCase  :  int							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[int]=0							,    _UpperCamelCase  :  str=None					):
            UpperCAmelCase_            =  round(val / multiple					) * multiple

            if max_val is not None and x > max_val:
                  UpperCAmelCase_            =  math.floor(val / multiple					) * multiple

            if x < min_val:
                  UpperCAmelCase_            =  math.ceil(val / multiple					) * multiple

            return x

      UpperCAmelCase_            =  (output_size, output_size) if isinstance(_UpperCamelCase							,    _UpperCamelCase					) else output_size

      UpperCAmelCase_      ,		UpperCAmelCase_            =  get_image_size(_UpperCamelCase					)
      UpperCAmelCase_      ,		UpperCAmelCase_            =  output_size

      # determine new height and width
      UpperCAmelCase_            =  output_height / input_height
      UpperCAmelCase_            =  output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width					) < abs(1 - scale_height					):
                  # fit width
                  UpperCAmelCase_            =  scale_width
            else:
                  # fit height
                  UpperCAmelCase_            =  scale_height

      UpperCAmelCase_            =  constraint_to_multiple_of(scale_height * input_height							,    multiple=_UpperCamelCase					)
      UpperCAmelCase_            =  constraint_to_multiple_of(scale_width * input_width							,    multiple=_UpperCamelCase					)

      return (new_height, new_width)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	['''pixel_values''']






  def __init__(      self			:       Any  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BILINEAR  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							**UpperCAmelCase__			:       str  ,							)				->None:
        super().__init__(**UpperCAmelCase__  )
        UpperCAmelCase_            =  size if size is not None else {'''height''': 384, '''width''': 384}
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size
        UpperCAmelCase_            =  keep_aspect_ratio
        UpperCAmelCase_            =  ensure_multiple_of
        UpperCAmelCase_            =  resample
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase_            =  image_std if image_std is not None else IMAGENET_STANDARD_STD






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Dict[str, int]  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BICUBIC  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       List[str]  ,							)				->np.ndarray:
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        if "height" not in size or "width" not in size:
              raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
        UpperCAmelCase_            =  get_resize_output_image_size(
            UpperCAmelCase__  ,							output_size=(size['''height'''], size['''width'''])  ,							keep_aspect_ratio=UpperCAmelCase__  ,							multiple=UpperCAmelCase__  ,							)
        return resize(UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[int, float]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       Optional[Any]  ,							)				->Any:
        return rescale(UpperCAmelCase__  ,							scale=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Union[float, List[float]]  ,							UpperCAmelCase__			:       Union[float, List[float]]  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       Optional[int]  ,							)				->np.ndarray:
        return normalize(UpperCAmelCase__  ,							mean=UpperCAmelCase__  ,							std=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       ImageInput  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       int = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       int = None  ,							UpperCAmelCase__			:       PILImageResampling = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       float = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[str, TensorType]] = None  ,							UpperCAmelCase__			:       ChannelDimension = ChannelDimension.FIRST  ,							**UpperCAmelCase__			:       Any  ,							)				->PIL.Image.Image:
        UpperCAmelCase_            =  do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase_            =  size if size is not None else self.size
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        UpperCAmelCase_            =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
        UpperCAmelCase_            =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
        UpperCAmelCase_            =  resample if resample is not None else self.resample
        UpperCAmelCase_            =  do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase_            =  rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase_            =  do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase_            =  image_std if image_std is not None else self.image_std

        UpperCAmelCase_            =  make_list_of_images(UpperCAmelCase__  )

        if not valid_images(UpperCAmelCase__  ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

        if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

        if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

        # All transformations expect numpy arrays.
        UpperCAmelCase_            =  [to_numpy_array(UpperCAmelCase__  ) for image in images]

        if do_resize:
              UpperCAmelCase_            =  [self.resize(image=UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  ) for image in images]

        if do_rescale:
              UpperCAmelCase_            =  [self.rescale(image=UpperCAmelCase__  ,							scale=UpperCAmelCase__  ) for image in images]

        if do_normalize:
              UpperCAmelCase_            =  [self.normalize(image=UpperCAmelCase__  ,							mean=UpperCAmelCase__  ,							std=UpperCAmelCase__  ) for image in images]

        UpperCAmelCase_            =  [to_channel_dimension_format(UpperCAmelCase__  ,							UpperCAmelCase__  ) for image in images]

        UpperCAmelCase_            =  {'''pixel_values''': images}
        return BatchFeature(data=UpperCAmelCase__  ,							tensor_type=UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       List[Tuple] = None  )				->Optional[Any]:
        UpperCAmelCase_            =  outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
              if len(UpperCAmelCase__  ) != len(UpperCAmelCase__  ):
                    raise ValueError(
                        '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''  )

              if is_torch_tensor(UpperCAmelCase__  ):
                    UpperCAmelCase_            =  target_sizes.numpy()

              UpperCAmelCase_            =  []

              for idx in range(len(UpperCAmelCase__  )  ):
                    UpperCAmelCase_            =  torch.nn.functional.interpolate(
                        logits[idx].unsqueeze(dim=0  )  ,							size=target_sizes[idx]  ,							mode='''bilinear'''  ,							align_corners=UpperCAmelCase__  )
                    UpperCAmelCase_            =  resized_logits[0].argmax(dim=0  )
                    semantic_segmentation.append(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  logits.argmax(dim=1  )
              UpperCAmelCase_            =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

        return semantic_segmentation





