

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 					_lowercase     (				a_				:					Optional[int]				)				->	List[str]:




					'''simple docstring'''







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

					return name




def 					_lowercase     (				a_				:					Optional[int]					,a_				:					Dict				)				->	Dict:




					'''simple docstring'''







					for key in orig_state_dict.copy().keys():
										__magic_name__							=    orig_state_dict.pop(a_				)

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

										else:
															__magic_name__							=    val

					return orig_state_dict




def 					_lowercase     (				a_				:					Optional[int]					,a_				:					Optional[int]				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ViTMAEConfig()
					if "large" in checkpoint_url:
										__magic_name__							=    1_0_2_4
										__magic_name__							=    4_0_9_6
										__magic_name__							=    2_4
										__magic_name__							=    1_6
					elif "huge" in checkpoint_url:
										__magic_name__							=    1_4
										__magic_name__							=    1_2_8_0
										__magic_name__							=    5_1_2_0
										__magic_name__							=    3_2
										__magic_name__							=    1_6

					__magic_name__							=    ViTMAEForPreTraining(a_				)

					__magic_name__							=    torch.hub.load_state_dict_from_url(a_					,map_location='cpu'				)['model']

					__magic_name__							=    ViTMAEImageProcessor(size=config.image_size				)

					__magic_name__							=    convert_state_dict(a_					,a_				)

					model.load_state_dict(a_				)
					model.eval()

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

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

					# forward pass
					torch.manual_seed(2				)
					__magic_name__							=    model(**a_				)
					__magic_name__							=    outputs.logits

					if "large" in checkpoint_url:
										__magic_name__							=    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:
										__magic_name__							=    torch.tensor(
										    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]				)
					else:
										__magic_name__							=    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)







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   int       				=  0
	_lowercase  :   bool       				=  False
	_lowercase  :   float       				=  3.0




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						self.assertDictEqual(MockClass().to_kwargs()  , {}      )
						self.assertDictEqual(MockClass(a=2      ).to_kwargs()  , {'a': 2}      )
						self.assertDictEqual(MockClass(a=2  , b=__UpperCamelCase      ).to_kwargs()  , {'a': 2, 'b': True}      )
						self.assertDictEqual(MockClass(a=2  , c=2.25      ).to_kwargs()  , {'a': 2, 'c': 2.25}      )







	@require_cuda
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    GradScalerKwargs(init_scale=10_24  , growth_factor=2      )
						AcceleratorState._reset_state()
						__magic_name__							=    Accelerator(mixed_precision='fp16'  , kwargs_handlers=[scaler_handler]      )
						print(accelerator.use_fpaa      )
						__magic_name__							=    accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale  , 1024.0      )
						self.assertEqual(scaler._growth_factor  , 2.0      )

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor  , 0.5      )
						self.assertEqual(scaler._growth_interval  , 20_00      )
						self.assertEqual(scaler._enabled  , __UpperCamelCase      )







	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
						execute_subprocess_async(__UpperCamelCase  , env=os.environ.copy()      )


if __name__ == "__main__":
						A__						=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						A__						=							Accelerator(kwargs_handlers=[ddp_scaler])
						A__						=							torch.nn.Linear(100, 200)
						A__						=							accelerator.prepare(model)

						# Check the values changed in kwargs
						A__						=							""
						A__						=							model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




