
import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				str=False  ):
 __a :  Any						=  []
 for i in range(config.num_hidden_layers  ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")  )
  rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")  )
  rename_keys.append(
      (f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")  )
  rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")  )
  rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")  )
  rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")  )
  rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")  )
  rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")  )
  rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")  )
  rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")  )

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ('''module.cls_token''', '''vit.embeddings.cls_token'''),
         ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
         ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
         ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
     ]  )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('''module.norm.weight''', '''layernorm.weight'''),
          ('''module.norm.bias''', '''layernorm.bias'''),
      ]  )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  __a :  Optional[int]						=  [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''  ) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''norm.weight''', '''vit.layernorm.weight'''),
          ('''norm.bias''', '''vit.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]  )

 return rename_keys




def    __UpperCamelCase     (				lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				List[Any]=False  ):
 for i in range(config.num_hidden_layers  ):
  if base_model:
   __a :  Dict						=  ''''''
  else:
   __a :  str						=  '''vit.'''
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __a :  List[Any]						=  state_dict.pop(f"module.blocks.{i}.attn.qkv.weight"  )
  __a :  Tuple						=  state_dict.pop(f"module.blocks.{i}.attn.qkv.bias"  )
  # next, add query, keys and values (in that order) to the state dict
  __a :  Dict						=  in_proj_weight[
      : config.hidden_size, :
  ]
  __a :  str						=  in_proj_bias[: config.hidden_size]
  __a :  int						=  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __a :  Dict						=  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __a :  str						=  in_proj_weight[
      -config.hidden_size :, :
  ]
  __a :  Optional[Any]						=  in_proj_bias[-config.hidden_size :]




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Dict						=  ['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(lowerCAmelCase__  ,   lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ):
 # projection head is used in the self-supervised pre-training in MSN,
 # for downstream task it's not needed.
 __a :  str						=  [
     '''module.fc.fc1.weight''',
     '''module.fc.fc1.bias''',
     '''module.fc.bn1.weight''',
     '''module.fc.bn1.bias''',
     '''module.fc.bn1.running_mean''',
     '''module.fc.bn1.running_var''',
     '''module.fc.bn1.num_batches_tracked''',
     '''module.fc.fc2.weight''',
     '''module.fc.fc2.bias''',
     '''module.fc.bn2.weight''',
     '''module.fc.bn2.bias''',
     '''module.fc.bn2.running_mean''',
     '''module.fc.bn2.running_var''',
     '''module.fc.bn2.num_batches_tracked''',
     '''module.fc.fc3.weight''',
     '''module.fc.fc3.bias''',
 ]
 for k in ignore_keys:
  state_dict.pop(lowerCAmelCase__  ,   lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				List[Any]  ):
 __a :  Optional[Any]						=  dct.pop(lowerCAmelCase__  )
 __a :  Optional[Any]						=  val




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				str  ):
 __a :  str						=  ViTMSNConfig()
 __a :  List[Any]						=  1_0_0_0

 __a :  Union[str, Any]						=  '''datasets/huggingface/label-files'''
 __a :  Optional[int]						=  '''imagenet-1k-id2label.json'''
 __a :  Optional[int]						=  json.load(open(hf_hub_download(lowerCAmelCase__  ,   lowerCAmelCase__  )  ,   '''r'''  )  )
 __a :  Dict						=  {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
 __a :  Tuple						=  idalabel
 __a :  int						=  {v: k for k, v in idalabel.items()}

 if "s16" in checkpoint_url:
  __a :  Union[str, Any]						=  3_8_4
  __a :  Union[str, Any]						=  1_5_3_6
  __a :  Union[str, Any]						=  6
 elif "l16" in checkpoint_url:
  __a :  str						=  1_0_2_4
  __a :  Union[str, Any]						=  4_0_9_6
  __a :  Optional[int]						=  2_4
  __a :  int						=  1_6
  __a :  List[Any]						=  0.1
 elif "b4" in checkpoint_url:
  __a :  int						=  4
 elif "l7" in checkpoint_url:
  __a :  int						=  7
  __a :  List[Any]						=  1_0_2_4
  __a :  Union[str, Any]						=  4_0_9_6
  __a :  List[str]						=  2_4
  __a :  int						=  1_6
  __a :  Tuple						=  0.1

 __a :  Dict						=  ViTMSNModel(lowerCAmelCase__  )

 __a :  Union[str, Any]						=  torch.hub.load_state_dict_from_url(lowerCAmelCase__  ,   map_location='''cpu'''  )['''target_encoder''']

 __a :  Optional[int]						=  ViTImageProcessor(size=config.image_size  )

 remove_projection_head(lowerCAmelCase__  )
 __a :  str						=  create_rename_keys(lowerCAmelCase__  ,   base_model=lowerCAmelCase__  )

 for src, dest in rename_keys:
  rename_key(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
 read_in_q_k_v(lowerCAmelCase__  ,   lowerCAmelCase__  ,   base_model=lowerCAmelCase__  )

 model.load_state_dict(lowerCAmelCase__  )
 model.eval()

 __a :  str						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''

 __a :  str						=  Image.open(requests.get(lowerCAmelCase__  ,   stream=lowerCAmelCase__  ).raw  )
 __a :  Any						=  ViTImageProcessor(
     size=config.image_size  ,   image_mean=lowerCAmelCase__  ,   image_std=lowerCAmelCase__  )
 __a :  Tuple						=  image_processor(images=lowerCAmelCase__  ,   return_tensors='''pt'''  )

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

 # The following Colab Notebook was used to generate these outputs:
 # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
 if "s16" in checkpoint_url:
  __a :  Dict						=  torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]  )
 elif "b16" in checkpoint_url:
  __a :  List[str]						=  torch.tensor([[14.28_89, -18.90_45, 11.72_81]]  )
 elif "l16" in checkpoint_url:
  __a :  str						=  torch.tensor([[41.50_28, -22.86_81, 45.64_75]]  )
 elif "b4" in checkpoint_url:
  __a :  Union[str, Any]						=  torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]  )
 else:
  __a :  Dict						=  torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]  )

 # verify logits
 assert torch.allclose(last_hidden_state[:, 0, :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__":
      lowercase__       =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint_url',
          default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
          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.'
      )

      lowercase__       =parser.parse_args()
      convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase__       =getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

 __a :  Tuple						=  Path(lowerCAmelCase__  )
 __a :  Dict						=  save_dir.joinpath(f"rank_{local_rank}_output.json"  )
 torch.cuda.set_device(lowerCAmelCase__  )
 __a :  Dict						=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__  ).cuda()
 if fpaa:
  __a :  str						=  model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(lowerCAmelCase__  ,   lowerCAmelCase__  )  # update config with task specific params
 __a :  List[str]						=  generate_kwargs.pop('''num_beams'''  ,   model.config.num_beams  )  # AttributeError risk?
 if num_return_sequences > num_beams:
  __a :  Dict						=  num_return_sequences

 __a :  Optional[Any]						=  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  SeqaSeqDataset(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   max_target_length=1_0_2_4  ,   type_path=lowerCAmelCase__  ,   n_obs=lowerCAmelCase__  ,   prefix=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __a :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  model.generate(
      input_ids=batch['''input_ids'''].to(model.device  )  ,   attention_mask=batch['''attention_mask'''].to(model.device  )  ,   num_return_sequences=lowerCAmelCase__  ,   num_beams=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  chunks(lowerCAmelCase__  ,   lowerCAmelCase__  )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCAmelCase__  ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}  )
 save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
 return results, sampler.num_replicas




def    __UpperCamelCase     (				):
 __a :  str						=  argparse.ArgumentParser(
     epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''  )
 parser.add_argument('''--data_dir'''  ,   type=lowerCAmelCase__  ,   help='''like cnn_dm/test.source'''  )
 parser.add_argument(
     '''--model_name'''  ,   type=lowerCAmelCase__  ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''  ,   default='''sshleifer/distilbart-xsum-12-3'''  ,   )
 parser.add_argument('''--save_dir'''  ,   type=lowerCAmelCase__  ,   help='''where to save'''  ,   default='''tmp_gen'''  )
 parser.add_argument('''--max_source_length'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  )
 parser.add_argument(
     '''--type_path'''  ,   type=lowerCAmelCase__  ,   default='''test'''  ,   help='''which subset to evaluate typically train/val/test'''  )
 parser.add_argument('''--task'''  ,   type=lowerCAmelCase__  ,   default='''summarization'''  ,   help='''used for task_specific_params + metrics'''  )
 parser.add_argument('''--bs'''  ,   type=lowerCAmelCase__  ,   default=8  ,   required=lowerCAmelCase__  ,   help='''batch size'''  )
 parser.add_argument(
     '''--local_rank'''  ,   type=lowerCAmelCase__  ,   default=-1  ,   required=lowerCAmelCase__  ,   help='''should be passed by distributed.launch'''  )

 parser.add_argument(
     '''--n_obs'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   help='''How many observations. Defaults to all.'''  )
 parser.add_argument(
     '''--num_return_sequences'''  ,   type=lowerCAmelCase__  ,   default=1  ,   required=lowerCAmelCase__  ,   help='''How many sequences to return'''  )
 parser.add_argument(
     '''--sync_timeout'''  ,   type=lowerCAmelCase__  ,   default=6_0_0  ,   required=lowerCAmelCase__  ,   help='''How long should master process wait for other processes to finish.'''  ,   )
 parser.add_argument('''--src_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument('''--tgt_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument(
     '''--prefix'''  ,   type=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   help='''will be added to the begininng of src examples'''  )
 parser.add_argument('''--fp16'''  ,   action='''store_true'''  )
 parser.add_argument('''--debug'''  ,   action='''store_true'''  )
 __a :  int						=  time.time()
 __a				,       __a :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  list(json_save_dir.glob('''rank_*.json'''  )  )
 if intermediate_files:
  raise ValueError(f"Found files at {json_save_dir} please move or remove them."  )
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __a :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  eval_data_dir(
     args.data_dir  ,   lowerCAmelCase__  ,   args.model_name  ,   type_path=args.type_path  ,   bs=args.bs  ,   fpaa=args.fpaa  ,   task=args.task  ,   local_rank=args.local_rank  ,   n_obs=args.n_obs  ,   max_source_length=args.max_source_length  ,   num_return_sequences=args.num_return_sequences  ,   prefix=args.prefix  ,   dataset_kwargs=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )

 if args.local_rank <= 0:
  __a :  int						=  Path(args.save_dir  )
  save_dir.mkdir(exist_ok=lowerCAmelCase__  )
  __a :  List[str]						=  gather_results_from_each_node(lowerCAmelCase__  ,   lowerCAmelCase__  ,   args.sync_timeout  )
  __a :  int						=  combine_partial_results(lowerCAmelCase__  )
  if args.num_return_sequences > 1:
   __a :  List[Any]						=  save_dir.joinpath('''pseudolabel_results.json'''  )
   print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"  )
   save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return
  __a :  Any						=  Path(args.data_dir  ).joinpath(args.type_path + '''.target'''  )
  with open(lowerCAmelCase__  ) as f:
   __a :  Optional[int]						=  [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase__  )]

  # Calculate metrics, save metrics,  and save _generations.txt
  __a :  str						=  '''translation''' in args.task
  __a :  List[str]						=  calculate_bleu if calc_bleu else calculate_rouge
  __a :  Any						=  '''bleu''' if calc_bleu else '''rouge'''
  __a :  Dict						=  score_fn(lowerCAmelCase__  ,   lowerCAmelCase__  )
  __a :  Dict						=  len(lowerCAmelCase__  )
  __a :  str						=  time.time() - start_time
  __a :  List[str]						=  round(runtime / metrics['''n_obs''']  ,   4  )
  __a :  Tuple						=  num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __a :  Optional[int]						=  save_dir.joinpath(f"{args.type_path}_{metric_name}.json"  )
  save_json(lowerCAmelCase__  ,   lowerCAmelCase__  ,   indent=lowerCAmelCase__  )
  print(lowerCAmelCase__  )
  write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}_generations.txt"  )  )
  if args.debug:
   write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}.target"  )  )
  else:
   shutil.rmtree(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Optional[int]						=  []
 for partial_result in partial_results:
  records.extend(lowerCAmelCase__  )
 __a :  Tuple						=  sorted(lowerCAmelCase__  ,   key=lambda lowerCAmelCase__  : x["id"]  )
 __a :  Tuple						=  [x['''pred'''] for x in records]
 return preds




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Dict  ):
 # WAIT FOR lots of .json files
 __a :  Tuple						=  time.time()
 logger.info('''waiting for all nodes to finish'''  )
 __a :  Optional[int]						=  None
 while (time.time() - start_wait) < timeout:
  __a :  Optional[int]						=  list(save_dir.glob('''rank_*.json'''  )  )
  if len(lowerCAmelCase__  ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __a :  Tuple						=  lmap(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('''Rank 0 gave up on waiting for other processes'''  )
 # Unreachable


if __name__ == "__main__":
      # Usage for MT:
      run_generate()
