





'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  return name



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  for key in orig_state_dict.copy().keys():
    _snake_case     		=				orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

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

    else:
      _snake_case     		=				val

  return orig_state_dict



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				ViTMAEConfig()
  if "large" in checkpoint_url:
    _snake_case     		=				10_24
    _snake_case     		=				40_96
    _snake_case     		=				24
    _snake_case     		=				16
  elif "huge" in checkpoint_url:
    _snake_case     		=				14
    _snake_case     		=				12_80
    _snake_case     		=				51_20
    _snake_case     		=				32
    _snake_case     		=				16

  _snake_case     		=				ViTMAEForPreTraining(SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__					,	map_location="cpu"       )["model"]

  _snake_case     		=				ViTMAEImageProcessor(size=config.image_size       )

  _snake_case     		=				convert_state_dict(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  model.load_state_dict(SCREAMING_SNAKE_CASE__       )
  model.eval()

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

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

  # forward pass
  torch.manual_seed(2       )
  _snake_case     		=				model(**SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				outputs.logits

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

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

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


if __name__ == "__main__":
 __magic_name__   :						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."""
 )

 __magic_name__   :						List[str]    =			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_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
  return lrs




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for step in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	"schedule.bin"       )
        torch.save(scheduler.state_dict()					,	SCREAMING_SNAKE_CASE__       )

        _snake_case     		=				torch.load(SCREAMING_SNAKE_CASE__       )
        scheduler.load_state_dict(SCREAMING_SNAKE_CASE__       )
  return lrs


@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				AdamW(params=[w]						,   lr=2e-1						,   weight_decay=0.0	)
       for _ in range(100	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				Adafactor(
           params=[w]						,   lr=1e-2						,   eps=(1e-30, 1e-3)						,   clip_threshold=1.0						,   decay_rate=-0.8						,   betaa=lowerCamelCase						,   weight_decay=0.0						,   relative_step=lowerCamelCase						,   scale_parameter=lowerCamelCase						,   warmup_init=lowerCamelCase						,   )
       for _ in range(1_000	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   nn.Linear(50					,    50			) if is_torch_available() else None
     UpperCAmelCase__       :						str      =   AdamW(m.parameters()					,    lr=10.0			) if is_torch_available() else None
     UpperCAmelCase__       :						Optional[Any]      =   10

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase						,   msg=lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				{"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _snake_case     		=				{
           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():
         _snake_case		,		_snake_case     		=				data

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

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







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				fn

     def __call__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       return self.fn(*lowerCamelCase						,   **lowerCamelCase	)






     @classmethod
     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(map(self						,   scheduler.lr_lambdas	)	)


