



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 logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    iter(self.dataset				)
    __lowerCamelCase													=    True
    while more_examples:
       __lowerCamelCase							,__lowerCamelCase													=    [], 0
       while True:
          if buffer_len >= self.input_characters:
             break
          try:
             buffer.append(next(__UpperCAmelCase				)['''content''']				)
             buffer_len += len(buffer[-1]				)
          except StopIteration:
             __lowerCamelCase													=    False
             break
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				truncation=__UpperCAmelCase				)['''input_ids''']
       __lowerCamelCase													=    []
       for tokenized_input in tokenized_inputs:
          all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
       for i in range(0	,				len(__UpperCAmelCase				)	,				self.seq_length				):
          __lowerCamelCase													=    all_token_ids[i : i + self.seq_length]
          if len(__UpperCAmelCase				) == self.seq_length:
             yield torch.tensor(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





def 		a__			(		_UpperCamelCase			: str							):
   model.eval()
   __lowerCamelCase													=    []
   for step, batch in enumerate(_UpperCamelCase							):
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase ,labels=_UpperCamelCase							)
      __lowerCamelCase													=    outputs.loss.repeat(args.batch_size							)
      losses.append(accelerator.gather(_UpperCamelCase							)							)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   __lowerCamelCase													=    torch.mean(torch.cat(_UpperCamelCase							)							)
   try:
      __lowerCamelCase													=    torch.exp(_UpperCamelCase							)
   except OverflowError:
      __lowerCamelCase													=    float('''inf'''							)
   return loss.item(), perplexity.item()


# Setup Accelerator
a_			=					Accelerator()

# Parse configuration
a_			=					HfArgumentParser(EvaluationArguments)
a_			=					parser.parse_args()
set_seed(args.seed)

# Logging
a_			=					logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
a_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

# Prepare everything with our `accelerator`.
a_		,						a_			=					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_		,						a_			=					evaluate(args)





logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")






