


'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 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 __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Dict	):
 for key in orig_state_dict.copy().keys():
  lowerCamelCase_  				=						orig_state_dict.pop(UpperCAmelCase_	)

  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 __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		List[Any]	):
 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(UpperCAmelCase_	)

 lowerCamelCase_  				=						torch.hub.load_state_dict_from_url(UpperCAmelCase_      ,       map_location="cpu"	)["model"]

 lowerCamelCase_  				=						ViTMAEImageProcessor(size=config.image_size	)

 lowerCamelCase_  				=						convert_state_dict(UpperCAmelCase_      ,       UpperCAmelCase_	)

 model.load_state_dict(UpperCAmelCase_	)
 model.eval()

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

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

 # forward pass
 torch.manual_seed(2	)
 lowerCamelCase_  				=						model(**UpperCAmelCase_	)
 lowerCamelCase_  				=						outputs.logits

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

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

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


if __name__ == "__main__":
     a_						:       List[Any]      			=				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_						:       List[Any]      			=				parser.parse_args()
     convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''simple docstring'''

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]=10	):
 lowerCamelCase_  				=						[]
 for _ in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
 return lrs





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[Any]=10	):
 lowerCamelCase_  				=						[]
 for step in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "schedule.bin"	)
    torch.save(scheduler.state_dict()      ,       UpperCAmelCase_	)

    lowerCamelCase_  				=						torch.load(UpperCAmelCase_	)
    scheduler.load_state_dict(UpperCAmelCase_	)
 return lrs





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						AdamW(params=[w]    ,						lr=2e-1    ,						weight_decay=0.0  )
   for _ in range(100  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						Adafactor(
       params=[w]    ,						lr=1e-2    ,						eps=(1e-30, 1e-3)    ,						clip_threshold=1.0    ,						decay_rate=-0.8    ,						betaa=UpperCamelCase    ,						weight_decay=0.0    ,						relative_step=UpperCamelCase    ,						scale_parameter=UpperCamelCase    ,						warmup_init=UpperCamelCase    ,						)
   for _ in range(1000  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						nn.Linear(50    ,		50    ) if is_torch_available() else None
  _lowerCamelCase						=						AdamW(m.parameters()    ,		lr=10.0    ) if is_torch_available() else None
  _lowerCamelCase						=						10






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase    ,						msg=UpperCamelCase  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{"num_warmup_steps": 2, "num_training_steps": 10}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   lowerCamelCase_  				=						{
       get_constant_schedule: ({}, [10.0] * self.num_steps),
       get_constant_schedule_with_warmup: (
           {"num_warmup_steps": 4},
           [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
       ),
       get_linear_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
       ),
       get_cosine_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
       ),
       get_cosine_with_hard_restarts_schedule_with_warmup: (
           {**common_kwargs, "num_cycles": 2},
           [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
       ),
       get_polynomial_decay_schedule_with_warmup: (
           {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
           [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
       ),
       get_inverse_sqrt_schedule: (
           {"num_warmup_steps": 2},
           [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
       ),
   }

   for scheduler_func, data in scheds.items():
    lowerCamelCase_				,lowerCamelCase_  				=						data

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    self.assertEqual(len([scheduler.get_lr()[0]]  )    ,						1  )
    lowerCamelCase_  				=						unwrap_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListAlmostEqual(
        UpperCamelCase    ,						UpperCamelCase    ,						tol=1e-2    ,						msg=f'''failed for {scheduler_func} in normal scheduler'''    ,						)

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    if scheduler_func.__name__ != "get_constant_schedule":
     LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase  )  # wrap to test picklability of the schedule
    lowerCamelCase_  				=						unwrap_and_save_reload_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListEqual(UpperCamelCase    ,						UpperCamelCase    ,						msg=f'''failed for {scheduler_func} in save and reload'''  )





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						fn






  def __call__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.fn(*UpperCamelCase    ,						**UpperCamelCase  )







  @classmethod
  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						list(map(self    ,						scheduler.lr_lambdas  )  )



