
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 		lowercase_    (     A__	)							->							str:
				"""simple docstring"""

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

				return name



def 		lowercase_    (     A__				,	A__	)							->							Dict:
				"""simple docstring"""

				for key in orig_state_dict.copy().keys():
								snake_case				  =      orig_state_dict.pop(A__	)

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

								else:
												snake_case				  =      val

				return orig_state_dict



def 		lowercase_    (     A__				,	A__	)							->							Dict:
				"""simple docstring"""

				snake_case				  =      ViTMAEConfig()
				if "large" in checkpoint_url:
								snake_case				  =      1024
								snake_case				  =      4096
								snake_case				  =      24
								snake_case				  =      16
				elif "huge" in checkpoint_url:
								snake_case				  =      14
								snake_case				  =      1280
								snake_case				  =      5120
								snake_case				  =      32
								snake_case				  =      16

				snake_case				  =      ViTMAEForPreTraining(A__	)

				snake_case				  =      torch.hub.load_state_dict_from_url(A__				,	map_location="cpu"	)["model"]

				snake_case				  =      ViTMAEImageProcessor(size=config.image_size	)

				snake_case				  =      convert_state_dict(A__				,	A__	)

				model.load_state_dict(A__	)
				model.eval()

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

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

				# forward pass
				torch.manual_seed(2	)
				snake_case				  =      model(**A__	)
				snake_case				  =      outputs.logits

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

				print(F'Saving model to {pytorch_dump_folder_path}'	)
				model.save_pretrained(A__	)

				print(F'Saving image processor to {pytorch_dump_folder_path}'	)
				image_processor.save_pretrained(A__	)


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 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      [0 for i in range(r + 1	)]
    # nc0 = 1
    snake_case				  =      1
    for i in range(1				,	n + 1	):
        # to compute current row from previous row.
        snake_case				  =      min(A__				,	A__	)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]





print(binomial_coefficient(n=10, r=5))

