import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





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

					return name





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any					):
					for key in orig_state_dict.copy().keys():
										SCREAMING_SNAKE_CASE           =    orig_state_dict.pop(UpperCAmelCase__					)

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

										else:
															SCREAMING_SNAKE_CASE           =    val

					return orig_state_dict





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Union[str, Any]					):
					SCREAMING_SNAKE_CASE           =    ViTMAEConfig()
					if "large" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    1_0_2_4
										SCREAMING_SNAKE_CASE           =    4_0_9_6
										SCREAMING_SNAKE_CASE           =    2_4
										SCREAMING_SNAKE_CASE           =    1_6
					elif "huge" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    1_4
										SCREAMING_SNAKE_CASE           =    1_2_8_0
										SCREAMING_SNAKE_CASE           =    5_1_2_0
										SCREAMING_SNAKE_CASE           =    3_2
										SCREAMING_SNAKE_CASE           =    1_6

					SCREAMING_SNAKE_CASE           =    ViTMAEForPreTraining(UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    torch.hub.load_state_dict_from_url(UpperCAmelCase__ ,	map_location="cpu"					)["model"]

					SCREAMING_SNAKE_CASE           =    ViTMAEImageProcessor(size=config.image_size					)

					SCREAMING_SNAKE_CASE           =    convert_state_dict(UpperCAmelCase__ ,	UpperCAmelCase__					)

					model.load_state_dict(UpperCAmelCase__					)
					model.eval()

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

					SCREAMING_SNAKE_CASE           =    Image.open(requests.get(UpperCAmelCase__ ,	stream=UpperCAmelCase__					).raw					)
					SCREAMING_SNAKE_CASE           =    ViTMAEImageProcessor(size=config.image_size					)
					SCREAMING_SNAKE_CASE           =    image_processor(images=UpperCAmelCase__ ,	return_tensors="pt"					)

					# forward pass
					torch.manual_seed(2					)
					SCREAMING_SNAKE_CASE           =    model(**UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    outputs.logits

					if "large" in checkpoint_url:
										SCREAMING_SNAKE_CASE           =    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:
										SCREAMING_SNAKE_CASE           =    torch.tensor(
										    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]					)
					else:
										SCREAMING_SNAKE_CASE           =    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__":
							_lowerCamelCase   :				Tuple     			=						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   :				Tuple     			=						parser.parse_args()
							convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

_lowerCamelCase   :				Union[str, Any]     			=						TypeVar('''T''')


class   lowercase      (       Generic[T]       ):




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {}  # dictionary of lists
												SCREAMING_SNAKE_CASE           =    directed




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   T		,							_UpperCamelCase				:   T )   ->			GraphAdjacencyList[T]:



												'''simple docstring'''

												if not self.directed:  # For undirected graphs
																	# if both source vertex and destination vertex are both present in the
																	# adjacency list, add destination vertex to source vertex list of adjacent
																	# vertices and add source vertex to destination vertex list of adjacent
																	# vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination vertex
																	# to source vertex list of adjacent vertices, then create a new vertex with
																	# destination vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
																	# if only destination vertex is present in adjacency list, add source vertex
																	# to destination vertex list of adjacent vertices, then create a new vertex
																	# with source vertex as key and assign a list containing the source vertex
																	# as it's first adjacent vertex.
																	elif destination_vertex in self.adj_list:
																						self.adj_list[destination_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and assign a list
																	# containing the destination vertex as it's first adjacent vertex also
																	# create a new vertex with destination vertex as key and assign a list
																	# containing the source vertex as it's first adjacent vertex.
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    [source_vertex]
												else:  # For directed graphs
																	# if both source vertex and destination vertex are present in adjacency
																	# list, add destination vertex to source vertex list of adjacent vertices.
																	if source_vertex in self.adj_list and destination_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																	# if only source vertex is present in adjacency list, add destination
																	# vertex to source vertex list of adjacent vertices and create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	elif source_vertex in self.adj_list:
																						self.adj_list[source_vertex].append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    []
																	# if only destination vertex is present in adjacency list, create a new
																	# vertex with source vertex as key and assign a list containing destination
																	# vertex as first adjacent vertex
																	elif destination_vertex in self.adj_list:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																	# if both source vertex and destination vertex are not present in adjacency
																	# list, create a new vertex with source vertex as key and a list containing
																	# destination vertex as it's first adjacent vertex. Then create a new vertex
																	# with destination vertex as key, which has no adjacent vertex
																	else:
																						SCREAMING_SNAKE_CASE           =    [destination_vertex]
																						SCREAMING_SNAKE_CASE           =    []

												return self





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



												'''simple docstring'''

												return pformat(self.adj_list )




