





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



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

 return name



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 for key in orig_state_dict.copy().keys():
  UpperCamelCase	:					int       	=   orig_state_dict.pop(_lowerCAmelCase     )

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

  else:
   UpperCamelCase	:					List[str]       	=   val

 return orig_state_dict



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

 UpperCamelCase	:					Optional[Any]       	=   ViTMAEForPreTraining(_lowerCAmelCase     )

 UpperCamelCase	:					Dict       	=   torch.hub.load_state_dict_from_url(_lowerCAmelCase							,   map_location="cpu"     )["model"]

 UpperCamelCase	:					Dict       	=   ViTMAEImageProcessor(size=config.image_size     )

 UpperCamelCase	:					Any       	=   convert_state_dict(_lowerCAmelCase							,   _lowerCAmelCase     )

 model.load_state_dict(_lowerCAmelCase     )
 model.eval()

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

 UpperCamelCase	:					List[str]       	=   Image.open(requests.get(_lowerCAmelCase							,   stream=_lowerCAmelCase     ).raw     )
 UpperCamelCase	:					List[Any]       	=   ViTMAEImageProcessor(size=config.image_size     )
 UpperCamelCase	:					Union[str, Any]       	=   image_processor(images=_lowerCAmelCase							,   return_tensors="pt"     )

 # forward pass
 torch.manual_seed(2     )
 UpperCamelCase	:					str       	=   model(**_lowerCAmelCase     )
 UpperCamelCase	:					Union[str, Any]       	=   outputs.logits

 if "large" in checkpoint_url:
  UpperCamelCase	:					Optional[int]       	=   torch.tensor(
      [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]     )
 elif "huge" in checkpoint_url:
  UpperCamelCase	:					Optional[Any]       	=   torch.tensor(
      [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]     )
 else:
  UpperCamelCase	:					int       	=   torch.tensor(
      [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]     )

 # 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       :     Tuple   =     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       :     Optional[int]   =     parser.parse_args()
    convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowerCAmelCase							:			Tuple     							=							logging.get_logger(__name__)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:
			try:
						with open(snake_case__       , """rb"""				) as flax_state_f:
									lowerCamelCase			      =					from_bytes(snake_case__       , flax_state_f.read()				)
			except UnpicklingError as e:
						try:
									with open(snake_case__				) as f:
												if f.read().startswith("""version"""				):
															raise OSError(
															    """You seem to have cloned a repository without having git-lfs installed. Please"""
															    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
															    """ folder you cloned."""				)
												else:
															raise ValueError from e
						except (UnicodeDecodeError, ValueError):
									raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '				)

			return load_flax_weights_in_pytorch_model(snake_case__       , snake_case__				)



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:

			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
						    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
						    """ instructions."""				)
						raise

			# check if we have bf16 weights
			lowerCamelCase			      =					flatten_dict(jax.tree_util.tree_map(lambda snake_case__				: x.dtype == jnp.bfloataa       , snake_case__				)				).values()
			if any(snake_case__				):
						# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
						    """before loading those in PyTorch model."""				)
						lowerCamelCase			      =					jax.tree_util.tree_map(
						    lambda snake_case__				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params       , snake_case__				)

			lowerCamelCase			      =					""""""

			lowerCamelCase			      =					flatten_dict(snake_case__       , sep="""."""				)
			lowerCamelCase			      =					pt_model.state_dict()

			# keep track of unexpected & missing keys
			lowerCamelCase			      =					[]
			lowerCamelCase			      =					set(pt_model_dict.keys()				)

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						lowerCamelCase			      =					flax_key_tuple.split("""."""				)

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					jnp.transpose(snake_case__       , (3, 2, 0, 1)				)
						elif flax_key_tuple_array[-1] == "kernel":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]
									lowerCamelCase			      =					flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									lowerCamelCase			      =					flax_key_tuple_array[:-1] + ["""weight"""]

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(snake_case__				):
												lowerCamelCase			      =					(
												    flax_key_tuple_string.replace("""_0"""       , """.0"""				)
												    .replace("""_1"""       , """.1"""				)
												    .replace("""_2"""       , """.2"""				)
												    .replace("""_3"""       , """.3"""				)
												    .replace("""_4"""       , """.4"""				)
												    .replace("""_5"""       , """.5"""				)
												    .replace("""_6"""       , """.6"""				)
												    .replace("""_7"""       , """.7"""				)
												    .replace("""_8"""       , """.8"""				)
												    .replace("""_9"""       , """.9"""				)
												)

						lowerCamelCase			      =					""".""".join(snake_case__				)

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
												    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'				)
									else:
												# add weight to pytorch dict
												lowerCamelCase			      =					np.asarray(snake_case__				) if not isinstance(snake_case__       , np.ndarray				) else flax_tensor
												lowerCamelCase			      =					torch.from_numpy(snake_case__				)
												# remove from missing keys
												missing_keys.remove(snake_case__				)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(snake_case__				)

			pt_model.load_state_dict(snake_case__				)

			# re-transform missing_keys to list
			lowerCamelCase			      =					list(snake_case__				)

			if len(snake_case__				) > 0:
						logger.warning(
						    """Some weights of the Flax model were not used when initializing the PyTorch model"""
						    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
						    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
						    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
						    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
						    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
						    """ FlaxBertForSequenceClassification model)."""				)
			if len(snake_case__				) > 0:
						logger.warning(
						    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
						    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
						    """ use it for predictions and inference."""				)

			return pt_model



