



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 		a__			(		_UpperCamelCase			: int							):
   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 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: Optional[Any]							):
   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 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ViTMAEConfig()
   if "large" in checkpoint_url:
      __lowerCamelCase													=    10_24
      __lowerCamelCase													=    40_96
      __lowerCamelCase													=    24
      __lowerCamelCase													=    16
   elif "huge" in checkpoint_url:
      __lowerCamelCase													=    14
      __lowerCamelCase													=    12_80
      __lowerCamelCase													=    51_20
      __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(
          [[-0.7_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:
      __lowerCamelCase													=    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:
      __lowerCamelCase													=    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] ,_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_			=					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_			=					parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a_			=					"""true"""





def 		a__			(		_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: List[str]=82 ,_UpperCamelCase			: Optional[Any]=16							):
   set_seed(42							)
   __lowerCamelCase													=    RegressionModel()
   __lowerCamelCase													=    deepcopy(_UpperCamelCase							)
   __lowerCamelCase													=    RegressionDataset(length=_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase							)
   model.to(accelerator.device							)
   __lowerCamelCase							,__lowerCamelCase													=    accelerator.prepare(_UpperCamelCase ,_UpperCamelCase							)
   return model, ddp_model, dataloader





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: str=False							):
   __lowerCamelCase													=    AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''							)
   __lowerCamelCase													=    load_dataset('''glue''' ,'''mrpc''' ,split='''validation'''							)

   def tokenize_function(_UpperCamelCase			: int							):
      __lowerCamelCase													=    tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase							)
      return outputs

   with accelerator.main_process_first():
      __lowerCamelCase													=    dataset.map(
          _UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)

   __lowerCamelCase													=    tokenized_datasets.rename_column('''label''' ,'''labels'''							)

   def collate_fn(_UpperCamelCase			: Any							):
      if use_longest:
         return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt'''							)
      return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt'''							)

   return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16							)





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: List[str]							):
   __lowerCamelCase													=    Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase							)
   __lowerCamelCase													=    get_dataloader(_UpperCamelCase ,not dispatch_batches							)
   __lowerCamelCase													=    AutoModelForSequenceClassification.from_pretrained(
       '''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    accelerator.prepare(_UpperCamelCase ,_UpperCamelCase							)
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    []
   for batch in dataloader:
      __lowerCamelCase							,__lowerCamelCase													=    batch.values()
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase							)
         __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((logit, target)							)
         logits_and_targets.append((logit, target)							)
   __lowerCamelCase							,__lowerCamelCase													=    [], []
   for logit, targ in logits_and_targets:
      logits.append(_UpperCamelCase							)
      targs.append(_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    torch.cat(_UpperCamelCase							), torch.cat(_UpperCamelCase							)
   return logits, targs





def 		a__			(		_UpperCamelCase			: Accelerator ,_UpperCamelCase			: List[Any]=82 ,_UpperCamelCase			: str=False ,_UpperCamelCase			: List[str]=False ,_UpperCamelCase			: Optional[int]=16							):
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase							)
   assert (
       len(_UpperCamelCase							) == num_samples
   ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase							)}"""





def 		a__			(		_UpperCamelCase			: bool = False ,_UpperCamelCase			: bool = False							):
   __lowerCamelCase													=    evaluate.load('''glue''' ,'''mrpc'''							)
   __lowerCamelCase							,__lowerCamelCase													=    get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase							)
   # First do baseline
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    setup['''no''']
   model.to(_UpperCamelCase							)
   model.eval()
   for batch in dataloader:
      batch.to(_UpperCamelCase							)
      with torch.inference_mode():
         __lowerCamelCase													=    model(**_UpperCamelCase							)
      __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
      metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels''']							)
   __lowerCamelCase													=    metric.compute()

   # Then do distributed
   __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    setup['''ddp''']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         __lowerCamelCase													=    model(**_UpperCamelCase							)
      __lowerCamelCase													=    outputs.logits.argmax(dim=-1							)
      __lowerCamelCase													=    batch['''labels''']
      __lowerCamelCase							,__lowerCamelCase													=    accelerator.gather_for_metrics((preds, references)							)
      metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase							)
   __lowerCamelCase													=    metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key] ,distributed[key]							), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""





def 		a__			(		):
   __lowerCamelCase													=    Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print('''**Testing gather_for_metrics**'''							)
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""							)
            test_mrpc(_UpperCamelCase ,_UpperCamelCase							)
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test torch metrics**'''							)
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         __lowerCamelCase													=    Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase							)
         if accelerator.is_local_main_process:
            print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""							)
         test_torch_metrics(_UpperCamelCase ,99							)
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test last batch is not dropped when perfectly divisible**'''							)
   __lowerCamelCase													=    Accelerator()
   test_torch_metrics(_UpperCamelCase ,5_12							)
   accelerator.state._reset_state()





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






