




"""simple docstring"""

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def 						_snake_case     (					_snake_case							:					Union[str, Any]							)    ->					str:






      '''simple docstring'''


      if "cls_token" in name:
            _A					   =   name.replace('cls_token'				,  'vit.embeddings.cls_token'							)
      if "mask_token" in name:
            _A					   =   name.replace('mask_token'				,  'decoder.mask_token'							)
      if "decoder_pos_embed" in name:
            _A					   =   name.replace('decoder_pos_embed'				,  'decoder.decoder_pos_embed'							)
      if "pos_embed" in name and "decoder" not in name:
            _A					   =   name.replace('pos_embed'				,  'vit.embeddings.position_embeddings'							)
      if "patch_embed.proj" in name:
            _A					   =   name.replace('patch_embed.proj'				,  'vit.embeddings.patch_embeddings.projection'							)
      if "patch_embed.norm" in name:
            _A					   =   name.replace('patch_embed.norm'				,  'vit.embeddings.norm'							)
      if "decoder_blocks" in name:
            _A					   =   name.replace('decoder_blocks'				,  'decoder.decoder_layers'							)
      if "blocks" in name:
            _A					   =   name.replace('blocks'				,  'vit.encoder.layer'							)
      if "attn.proj" in name:
            _A					   =   name.replace('attn.proj'				,  'attention.output.dense'							)
      if "attn" in name:
            _A					   =   name.replace('attn'				,  'attention.self'							)
      if "norm1" in name:
            _A					   =   name.replace('norm1'				,  'layernorm_before'							)
      if "norm2" in name:
            _A					   =   name.replace('norm2'				,  'layernorm_after'							)
      if "mlp.fc1" in name:
            _A					   =   name.replace('mlp.fc1'				,  'intermediate.dense'							)
      if "mlp.fc2" in name:
            _A					   =   name.replace('mlp.fc2'				,  'output.dense'							)
      if "decoder_embed" in name:
            _A					   =   name.replace('decoder_embed'				,  'decoder.decoder_embed'							)
      if "decoder_norm" in name:
            _A					   =   name.replace('decoder_norm'				,  'decoder.decoder_norm'							)
      if "decoder_pred" in name:
            _A					   =   name.replace('decoder_pred'				,  'decoder.decoder_pred'							)
      if "norm.weight" in name and "decoder" not in name:
            _A					   =   name.replace('norm.weight'				,  'vit.layernorm.weight'							)
      if "norm.bias" in name and "decoder" not in name:
            _A					   =   name.replace('norm.bias'				,  'vit.layernorm.bias'							)

      return name






def 						_snake_case     (					_snake_case							:					Dict				,  _snake_case							:					Tuple							)    ->					Union[str, Any]:






      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            _A					   =   orig_state_dict.pop(_UpperCAmelCase							)

            if "qkv" in key:
                  _A					   =   key.split('.'							)
                  _A					   =   int(key_split[1]							)
                  if "decoder_blocks" in key:
                        _A					   =   config.decoder_hidden_size
                        _A					   =   'decoder.decoder_layers.'
                        if "weight" in key:
                              _A					   =   val[:dim, :]
                              _A					   =   val[dim : dim * 2, :]
                              _A					   =   val[-dim:, :]
                        elif "bias" in key:
                              _A					   =   val[:dim]
                              _A					   =   val[dim : dim * 2]
                              _A					   =   val[-dim:]
                  else:
                        _A					   =   config.hidden_size
                        _A					   =   'vit.encoder.layer.'
                        if "weight" in key:
                              _A					   =   val[:dim, :]
                              _A					   =   val[dim : dim * 2, :]
                              _A					   =   val[-dim:, :]
                        elif "bias" in key:
                              _A					   =   val[:dim]
                              _A					   =   val[dim : dim * 2]
                              _A					   =   val[-dim:]

            else:
                  _A					   =   val

      return orig_state_dict






def 						_snake_case     (					_snake_case							:					Optional[int]				,  _snake_case							:					Union[str, Any]							)    ->					int:






      '''simple docstring'''


      _A					   =   ViTMAEConfig()
      if "large" in checkpoint_url:
            _A					   =   10_24
            _A					   =   40_96
            _A					   =   24
            _A					   =   16
      elif "huge" in checkpoint_url:
            _A					   =   14
            _A					   =   12_80
            _A					   =   51_20
            _A					   =   32
            _A					   =   16

      _A					   =   ViTMAEForPreTraining(_UpperCAmelCase							)

      _A					   =   torch.hub.load_state_dict_from_url(_UpperCAmelCase				,  map_location='cpu'							)['model']

      _A					   =   ViTMAEImageProcessor(size=config.image_size							)

      _A					   =   convert_state_dict(_UpperCAmelCase				,  _UpperCAmelCase							)

      model.load_state_dict(_UpperCAmelCase							)
      model.eval()

      _A					   =   'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

      _A					   =   Image.open(requests.get(_UpperCAmelCase				,  stream=_UpperCAmelCase							).raw							)
      _A					   =   ViTMAEImageProcessor(size=config.image_size							)
      _A					   =   image_processor(images=_UpperCAmelCase				,  return_tensors='pt'							)

      # forward pass
      torch.manual_seed(2							)
      _A					   =   model(**_UpperCAmelCase							)
      _A					   =   outputs.logits

      if "large" in checkpoint_url:
            _A					   =   torch.tensor(
                [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]							)
      elif "huge" in checkpoint_url:
            _A					   =   torch.tensor(
                [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]							)
      else:
            _A					   =   torch.tensor(
                [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]							)

      # verify logits
      assert torch.allclose(logits[0, :3, :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__":
       a					=			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
           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.'''
       )

       a					=			parser.parse_args()
       convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Optional[Any]					 =		set()
    # edges = list of graph's edges
    lowerCamelCase__    :      List[str]					 =		get_edges(_UpperCAmelCase )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		edges.pop()
        chosen_vertices.add(_UpperCAmelCase )
        chosen_vertices.add(_UpperCAmelCase )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(_UpperCAmelCase )
    return chosen_vertices


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     set:
    lowerCamelCase__    :      Union[str, Any]					 =		set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node) )
    return edges


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





