

'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_    :						Any    		=      logging.get_logger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Any		,							_UpperCamelCase		:  List[str]=False	)		->							Any:
 '''simple docstring'''


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

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

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

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  SCREAMING_SNAKE_CASE								=			[(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        __lowerCAmelCase    (       _UpperCamelCase		:  Any		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  str=False	)		->							Optional[int]:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(_UpperCamelCase		,							_UpperCamelCase	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			dct.pop(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			val


def        __lowerCAmelCase    (       )		->							str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE								=			Image.open(requests.get(_UpperCamelCase		,							stream=_UpperCamelCase	).raw	)
 return im


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Union[str, Any]	)		->							List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			ViTConfig()
 SCREAMING_SNAKE_CASE								=			False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  SCREAMING_SNAKE_CASE								=			True
  SCREAMING_SNAKE_CASE								=			int(vit_name[-12:-10]	)
  SCREAMING_SNAKE_CASE								=			int(vit_name[-9:-6]	)
 else:
  SCREAMING_SNAKE_CASE								=			10_00
  SCREAMING_SNAKE_CASE								=			'huggingface/label-files'
  SCREAMING_SNAKE_CASE								=			'imagenet-1k-id2label.json'
  SCREAMING_SNAKE_CASE								=			json.load(open(hf_hub_download(_UpperCamelCase		,							_UpperCamelCase		,							repo_type='dataset'	)		,							'r'	)	)
  SCREAMING_SNAKE_CASE								=			{int(_UpperCamelCase	): v for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE								=			idalabel
  SCREAMING_SNAKE_CASE								=			{v: k for k, v in idalabel.items()}
  SCREAMING_SNAKE_CASE								=			int(vit_name[-6:-4]	)
  SCREAMING_SNAKE_CASE								=			int(vit_name[-3:]	)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith('tiny'	):
   SCREAMING_SNAKE_CASE								=			1_92
   SCREAMING_SNAKE_CASE								=			7_68
   SCREAMING_SNAKE_CASE								=			12
   SCREAMING_SNAKE_CASE								=			3
  elif vit_name[9:].startswith('small'	):
   SCREAMING_SNAKE_CASE								=			3_84
   SCREAMING_SNAKE_CASE								=			15_36
   SCREAMING_SNAKE_CASE								=			12
   SCREAMING_SNAKE_CASE								=			6
  else:
   pass
 else:
  if vit_name[4:].startswith('small'	):
   SCREAMING_SNAKE_CASE								=			7_68
   SCREAMING_SNAKE_CASE								=			23_04
   SCREAMING_SNAKE_CASE								=			8
   SCREAMING_SNAKE_CASE								=			8
  elif vit_name[4:].startswith('base'	):
   pass
  elif vit_name[4:].startswith('large'	):
   SCREAMING_SNAKE_CASE								=			10_24
   SCREAMING_SNAKE_CASE								=			40_96
   SCREAMING_SNAKE_CASE								=			24
   SCREAMING_SNAKE_CASE								=			16
  elif vit_name[4:].startswith('huge'	):
   SCREAMING_SNAKE_CASE								=			12_80
   SCREAMING_SNAKE_CASE								=			51_20
   SCREAMING_SNAKE_CASE								=			32
   SCREAMING_SNAKE_CASE								=			16

    # load original model from timm
 SCREAMING_SNAKE_CASE								=			timm.create_model(_UpperCamelCase		,							pretrained=_UpperCamelCase	)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 SCREAMING_SNAKE_CASE								=			timm_model.state_dict()
 if base_model:
  remove_classification_head_(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			create_rename_keys(_UpperCamelCase		,							_UpperCamelCase	)
 for src, dest in rename_keys:
  rename_key(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
 read_in_q_k_v(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  SCREAMING_SNAKE_CASE								=			ViTModel(_UpperCamelCase	).eval()
 else:
  SCREAMING_SNAKE_CASE								=			ViTForImageClassification(_UpperCamelCase	).eval()
 model.load_state_dict(_UpperCamelCase	)

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  SCREAMING_SNAKE_CASE								=			DeiTImageProcessor(size=config.image_size	)
 else:
  SCREAMING_SNAKE_CASE								=			ViTImageProcessor(size=config.image_size	)
 SCREAMING_SNAKE_CASE								=			image_processor(images=prepare_img()		,							return_tensors='pt'	)
 SCREAMING_SNAKE_CASE								=			encoding['pixel_values']
 SCREAMING_SNAKE_CASE								=			model(_UpperCamelCase	)

 if base_model:
  SCREAMING_SNAKE_CASE								=			timm_model.forward_features(_UpperCamelCase	)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(_UpperCamelCase		,							outputs.pooler_output		,							atol=1e-3	)
 else:
  SCREAMING_SNAKE_CASE								=			timm_model(_UpperCamelCase	)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(_UpperCamelCase		,							outputs.logits		,							atol=1e-3	)

 Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
 print(f"""Saving model {vit_name} 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_    :						Dict    		=      argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--vit_name",
          default="vit_base_patch16_224",
          type=str,
          help="Name of the ViT timm model 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_    :						Union[str, Any]    		=      parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist


def        __lowerCAmelCase    (       *_UpperCamelCase		:  Optional[Any]	)		->							Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,							'r'	) as fh:
  fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_EX	)
  try:
   print(*_UpperCamelCase	)
  finally:
   fcntl.flock(_UpperCamelCase		,							fcntl.LOCK_UN	)


a_    :						int    		=      int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
a_    :						str    		=      torch.device("cuda", local_rank)
a_    :						Optional[int]    		=      socket.gethostname()

a_    :						Union[str, Any]    		=      F"""[{hostname}-{local_rank}]"""

try:
      # test distributed
      dist.init_process_group("nccl")
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      a_    :						Dict    		=      dist.get_rank()
      a_    :						Any    		=      dist.get_world_size()

      printflock(F"""{gpu} is OK (global rank: {rank}/{world_size})""")

      dist.barrier()
      if rank == 0:
            printflock(F"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
      printflock(F"""{gpu} is broken""")
      raise

