
"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



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

					return name



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					for key in orig_state_dict.copy().keys():
										lowercase						     =						orig_state_dict.pop(lowercase_		)

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

										else:
															lowercase						     =						val

					return orig_state_dict



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						ViTMAEConfig()
					if "large" in checkpoint_url:
										lowercase						     =						1024
										lowercase						     =						4096
										lowercase						     =						24
										lowercase						     =						16
					elif "huge" in checkpoint_url:
										lowercase						     =						14
										lowercase						     =						1280
										lowercase						     =						5120
										lowercase						     =						32
										lowercase						     =						16

					lowercase						     =						ViTMAEForPreTraining(lowercase_		)

					lowercase						     =						torch.hub.load_state_dict_from_url(lowercase_			,      map_location='''cpu'''		)['''model''']

					lowercase						     =						ViTMAEImageProcessor(size=config.image_size		)

					lowercase						     =						convert_state_dict(lowercase_			,      lowercase_		)

					model.load_state_dict(lowercase_		)
					model.eval()

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

					lowercase						     =						Image.open(requests.get(lowercase_			,      stream=lowercase_		).raw		)
					lowercase						     =						ViTMAEImageProcessor(size=config.image_size		)
					lowercase						     =						image_processor(images=lowercase_			,      return_tensors='''pt'''		)

					# forward pass
					torch.manual_seed(2		)
					lowercase						     =						model(**lowercase_		)
					lowercase						     =						outputs.logits

					if "large" in checkpoint_url:
										lowercase						     =						torch.tensor(
										    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]		)
					elif "huge" in checkpoint_url:
										lowercase						     =						torch.tensor(
										    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]		)
					else:
										lowercase						     =						torch.tensor(
										    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]		)

					# verify logits
					assert torch.allclose(logits[0, :3, :3]			,      lowercase_			,      atol=1E-4		)

					print(f'Saving model to {pytorch_dump_folder_path}'		)
					model.save_pretrained(lowercase_		)

					print(f'Saving image processor to {pytorch_dump_folder_path}'		)
					image_processor.save_pretrained(lowercase_		)


if __name__ == "__main__":
							lowercase__		:Optional[int]			  =   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__		:Optional[int]			  =   parser.parse_args()
							convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__		:str			  =   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:str			  =   ["ViTFeatureExtractor"]
							lowercase__		:int			  =   ["ViTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:int			  =   [
							    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTForImageClassification",
							    "ViTForMaskedImageModeling",
							    "ViTModel",
							    "ViTPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Any			  =   [
							    "TFViTForImageClassification",
							    "TFViTModel",
							    "TFViTPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "FlaxViTForImageClassification",
							    "FlaxViTModel",
							    "FlaxViTPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_vit import ViTFeatureExtractor
														from .image_processing_vit import ViTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit import (
														    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTForImageClassification,
														    ViTForMaskedImageModeling,
														    ViTModel,
														    ViTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
							import sys

							lowercase__		:Dict			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




