
"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def 					__lowerCAmelCase       (_UpperCamelCase	):







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

 return name
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):







 for key in orig_state_dict.copy().keys():
  __lowerCAmelCase    :		Tuple		    =   orig_state_dict.pop(lowerCAmelCase__	)

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

  else:
   __lowerCAmelCase    :		Optional[int]		    =   val

 return orig_state_dict
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
 __lowerCAmelCase    :		Dict		    =   ViTMAEConfig()
 if "large" in checkpoint_url:
  __lowerCAmelCase    :		Any		    =   1024
  __lowerCAmelCase    :		Any		    =   4096
  __lowerCAmelCase    :		List[str]		    =   24
  __lowerCAmelCase    :		List[str]		    =   16
 elif "huge" in checkpoint_url:
  __lowerCAmelCase    :		str		    =   14
  __lowerCAmelCase    :		List[str]		    =   1280
  __lowerCAmelCase    :		int		    =   5120
  __lowerCAmelCase    :		Optional[int]		    =   32
  __lowerCAmelCase    :		List[str]		    =   16

 __lowerCAmelCase    :		Optional[int]		    =   ViTMAEForPreTraining(lowerCAmelCase__	)

 __lowerCAmelCase    :		List[str]		    =   torch.hub.load_state_dict_from_url(lowerCAmelCase__ ,						map_location='cpu'	)['model']

 __lowerCAmelCase    :		Union[str, Any]		    =   ViTMAEImageProcessor(size=config.image_size	)

 __lowerCAmelCase    :		int		    =   convert_state_dict(lowerCAmelCase__ ,						lowerCAmelCase__	)

 model.load_state_dict(lowerCAmelCase__	)
 model.eval()

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

 __lowerCAmelCase    :		Dict		    =   Image.open(requests.get(lowerCAmelCase__ ,						stream=lowerCAmelCase__	).raw	)
 __lowerCAmelCase    :		Union[str, Any]		    =   ViTMAEImageProcessor(size=config.image_size	)
 __lowerCAmelCase    :		Union[str, Any]		    =   image_processor(images=lowerCAmelCase__ ,						return_tensors='pt'	)

 # forward pass
 torch.manual_seed(2	)
 __lowerCAmelCase    :		Any		    =   model(**lowerCAmelCase__	)
 __lowerCAmelCase    :		Union[str, Any]		    =   outputs.logits

 if "large" in checkpoint_url:
  __lowerCAmelCase    :		Any		    =   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:
  __lowerCAmelCase    :		Dict		    =   torch.tensor(
      [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]	)
 else:
  __lowerCAmelCase    :		int		    =   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] ,						lowerCAmelCase__ ,						atol=1e-4	)

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

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


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

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

"""simple docstring"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

			for dest in self.connections[node]:
				current_probability += self.connections[node][dest]
				if current_probability > random_value:
					return dest
			return ""



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   graph.transition(_UpperCamelCase	)
		visited[node] += 1

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()