






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

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

		return name

def      lowerCAmelCase__	(    _a       :			Dict  ,      _a       :			List[Any]    ):
		for key in orig_state_dict.copy().keys():
				snake_case_				:							int						 =       orig_state_dict.pop(_a    )

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

				else:
						snake_case_				:							Optional[int]						 =       val

		return orig_state_dict

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			int    ):
		snake_case_				:							Any						 =       ViTMAEConfig()
		if "large" in checkpoint_url:
				snake_case_				:							Dict						 =       10_24
				snake_case_				:							List[str]						 =       40_96
				snake_case_				:							Dict						 =       24
				snake_case_				:							Any						 =       16
		elif "huge" in checkpoint_url:
				snake_case_				:							Any						 =       14
				snake_case_				:							Union[str, Any]						 =       12_80
				snake_case_				:							List[str]						 =       51_20
				snake_case_				:							List[str]						 =       32
				snake_case_				:							int						 =       16

		snake_case_				:							Optional[int]						 =       ViTMAEForPreTraining(_a    )

		snake_case_				:							Optional[int]						 =       torch.hub.load_state_dict_from_url(_a  ,      map_location="cpu"    )["model"]

		snake_case_				:							Any						 =       ViTMAEImageProcessor(size=config.image_size    )

		snake_case_				:							Union[str, Any]						 =       convert_state_dict(_a  ,      _a    )

		model.load_state_dict(_a    )
		model.eval()

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

		snake_case_				:							List[str]						 =       Image.open(requests.get(_a  ,      stream=_a    ).raw    )
		snake_case_				:							List[str]						 =       ViTMAEImageProcessor(size=config.image_size    )
		snake_case_				:							int						 =       image_processor(images=_a  ,      return_tensors="pt"    )

		# forward pass
		torch.manual_seed(2    )
		snake_case_				:							Optional[int]						 =       model(**_a    )
		snake_case_				:							Optional[Any]						 =       outputs.logits

		if "large" in checkpoint_url:
				snake_case_				:							Optional[Any]						 =       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:
				snake_case_				:							Optional[Any]						 =       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:
				snake_case_				:							Any						 =       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]  ,      _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__":
							lowercase  :							Dict			  =    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.'''
							)

							lowercase  :							Dict			  =    parser.parse_args()
							convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					['image_processor', 'tokenizer']
						A     :   List[Any]						=					'ViltImageProcessor'
						A     :   Optional[Any]						=					('BertTokenizer', 'BertTokenizerFast')






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							str						 =       None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."		,				_SCREAMING_SNAKE_CASE		,				)
										snake_case_				:							str						 =       kwargs.pop("feature_extractor"				)

								snake_case_				:							Tuple						 =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."				)
								if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."				)

								super().__init__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       self.image_processor






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchEncoding:
								snake_case_				:							List[Any]						 =       self.tokenizer(
								    text=_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				return_overflowing_tokens=_SCREAMING_SNAKE_CASE		,				return_special_tokens_mask=_SCREAMING_SNAKE_CASE		,				return_offsets_mapping=_SCREAMING_SNAKE_CASE		,				return_length=_SCREAMING_SNAKE_CASE		,				verbose=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								# add pixel_values + pixel_mask
								snake_case_				:							str						 =       self.image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE				)
								encoding.update(_SCREAMING_SNAKE_CASE				)

								return encoding






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[Any]						 =       self.tokenizer.model_input_names
								snake_case_				:							Tuple						 =       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor_class






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor
