



'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    return name
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    for key in orig_state_dict.copy().keys():
        lowerCAmelCase__ :  Any							      =					orig_state_dict.pop(UpperCamelCase			)

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

        else:
            lowerCAmelCase__ :  List[str]							      =					val

    return orig_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Tuple							      =					ViTMAEForPreTraining(UpperCamelCase			)

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

    lowerCAmelCase__ :  str							      =					ViTMAEImageProcessor(size=config.image_size			)

    lowerCAmelCase__ :  str							      =					convert_state_dict(UpperCamelCase							,    UpperCamelCase			)

    model.load_state_dict(UpperCamelCase			)
    model.eval()

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

    lowerCAmelCase__ :  Tuple							      =					Image.open(requests.get(UpperCamelCase							,    stream=UpperCamelCase			).raw			)
    lowerCAmelCase__ :  int							      =					ViTMAEImageProcessor(size=config.image_size			)
    lowerCAmelCase__ :  Optional[Any]							      =					image_processor(images=UpperCamelCase							,    return_tensors="""pt"""			)

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

    if "large" in checkpoint_url:
        lowerCAmelCase__ :  List[str]							      =					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__ :  Union[str, Any]							      =					torch.tensor(
            [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]			)
    else:
        lowerCAmelCase__ :  int							      =					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__":
 _lowerCAmelCase      						=	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      						=	parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      int				=      '''xlm-roberta-xl'''




  def __init__(    self  ,__UpperCAmelCase=25_0880  ,__UpperCAmelCase=2560  ,__UpperCAmelCase=36  ,__UpperCAmelCase=32  ,__UpperCAmelCase=1_0240  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=514  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-05  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,__UpperCAmelCase=2  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		str:
      super().__init__(pad_token_id=__UpperCAmelCase  ,bos_token_id=__UpperCAmelCase  ,eos_token_id=__UpperCAmelCase  ,**__UpperCAmelCase      )
      lowerCAmelCase__ :  List[Any]							      =					vocab_size
      lowerCAmelCase__ :  int							      =					hidden_size
      lowerCAmelCase__ :  int							      =					num_hidden_layers
      lowerCAmelCase__ :  str							      =					num_attention_heads
      lowerCAmelCase__ :  int							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					intermediate_size
      lowerCAmelCase__ :  List[Any]							      =					hidden_dropout_prob
      lowerCAmelCase__ :  str							      =					attention_probs_dropout_prob
      lowerCAmelCase__ :  Optional[int]							      =					max_position_embeddings
      lowerCAmelCase__ :  List[str]							      =					type_vocab_size
      lowerCAmelCase__ :  List[Any]							      =					initializer_range
      lowerCAmelCase__ :  Tuple							      =					layer_norm_eps
      lowerCAmelCase__ :  int							      =					position_embedding_type
      lowerCAmelCase__ :  Optional[Any]							      =					use_cache
      lowerCAmelCase__ :  Optional[Any]							      =					classifier_dropout







class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          lowerCAmelCase__ :  Dict							      =					{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          lowerCAmelCase__ :  Any							      =					{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]      )


