





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

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

   return name

def   __snake_case    (       _lowerCAmelCase			:			Tuple					,					_lowerCAmelCase			:			Any						)			->    Union[str, Any]:
   for key in orig_state_dict.copy().keys():
      A_       :					List[Any]        =      orig_state_dict.pop(lowerCamelCase_						)

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

      else:
         A_       :					str        =      val

   return orig_state_dict

def   __snake_case    (       _lowerCAmelCase			:			str					,					_lowerCAmelCase			:			Tuple						)			->    str:
   A_       :					str        =      ViTMAEConfig()
   if "large" in checkpoint_url:
      A_       :					Dict        =      1024
      A_       :					List[str]        =      4096
      A_       :					str        =      24
      A_       :					Optional[Any]        =      16
   elif "huge" in checkpoint_url:
      A_       :					int        =      14
      A_       :					Union[str, Any]        =      1280
      A_       :					Union[str, Any]        =      5120
      A_       :					Tuple        =      32
      A_       :					Optional[Any]        =      16

   A_       :					Union[str, Any]        =      ViTMAEForPreTraining(lowerCamelCase_						)

   A_       :					Dict        =      torch.hub.load_state_dict_from_url(lowerCamelCase_					,					map_location="cpu"						)['model']

   A_       :					Union[str, Any]        =      ViTMAEImageProcessor(size=config.image_size						)

   A_       :					Optional[Any]        =      convert_state_dict(lowerCamelCase_					,					lowerCamelCase_						)

   model.load_state_dict(lowerCamelCase_						)
   model.eval()

   A_       :					Optional[Any]        =      'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

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

   # forward pass
   torch.manual_seed(2						)
   A_       :					str        =      model(**lowerCamelCase_						)
   A_       :					Optional[int]        =      outputs.logits

   if "large" in checkpoint_url:
      A_       :					Union[str, Any]        =      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:
      A_       :					Any        =      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:
      A_       :					Optional[int]        =      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]					,					lowerCamelCase_					,					atol=1e-4						)

   print(f"Saving model to {pytorch_dump_folder_path}"						)
   model.save_pretrained(lowerCamelCase_						)

   print(f"Saving image processor to {pytorch_dump_folder_path}"						)
   image_processor.save_pretrained(lowerCamelCase_						)


if __name__ == "__main__":
 _lowerCAmelCase				:   Union[str, 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.'''
 )

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




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


