
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def   lowerCamelCase__							(							__lowerCAmelCase    :    str							):




			"""simple docstring"""


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

			return name






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    List[Any]							):




			"""simple docstring"""


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

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

						else:
									lowerCAmelCase_         =      val

			return orig_state_dict






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    List[Any]							):




			"""simple docstring"""


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

			lowerCAmelCase_         =      ViTMAEForPreTraining(__lowerCAmelCase							)

			lowerCAmelCase_         =      torch.hub.load_state_dict_from_url(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			lowerCAmelCase_         =      ViTMAEImageProcessor(size=config.image_size							)

			lowerCAmelCase_         =      convert_state_dict(__lowerCAmelCase       ,				__lowerCAmelCase							)

			model.load_state_dict(__lowerCAmelCase							)
			model.eval()

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

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

			# forward pass
			torch.manual_seed(2							)
			lowerCAmelCase_         =      model(**__lowerCAmelCase							)
			lowerCAmelCase_         =      outputs.logits

			if "large" in checkpoint_url:
						lowerCAmelCase_         =      torch.tensor(
						    [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]							)
			elif "huge" in checkpoint_url:
						lowerCAmelCase_         =      torch.tensor(
						    [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]							)
			else:
						lowerCAmelCase_         =      torch.tensor(
						    [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]							)

			# 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__":
			_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)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A  =	logging.get_logger(__name__)

_A  =	{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''ibert'''




				def __init__(   self							,							_UpperCamelCase=30_522							,							_UpperCamelCase=768							,							_UpperCamelCase=12							,							_UpperCamelCase=12							,							_UpperCamelCase=3_072							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=1e-1_2							,							_UpperCamelCase=1							,							_UpperCamelCase=0							,							_UpperCamelCase=2							,							_UpperCamelCase="absolute"							,							_UpperCamelCase=False							,							_UpperCamelCase="none"							,							**_UpperCamelCase							,							)							-> List[str]:
							super().__init__(pad_token_id=_UpperCamelCase							,							bos_token_id=_UpperCamelCase							,							eos_token_id=_UpperCamelCase							,							**_UpperCamelCase	)

							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      layer_norm_eps
							lowerCAmelCase_         =      position_embedding_type
							lowerCAmelCase_         =      quant_mode
							lowerCAmelCase_         =      force_dequant





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_         =      {0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]	)



