


import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case   							=						logging.get_logger(__name__)






def 		_A		(		_lowercase )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			MobileNetVaConfig(layer_norm_eps=0.0_01 )

     if "_quant" in model_name:
          raise ValueError('Quantized models are not supported.' )

     __UpperCamelCase =			re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'					,  _lowercase )
     if matches:
          __UpperCamelCase =			float(matches[1] )
          __UpperCamelCase =			int(matches[2] )

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     __UpperCamelCase =			10_01
     __UpperCamelCase =			'imagenet-1k-id2label.json'
     __UpperCamelCase =			'huggingface/label-files'
     __UpperCamelCase =			json.load(open(hf_hub_download(_lowercase					,  _lowercase					,  repo_type='dataset' )					,  'r' ) )
     __UpperCamelCase =			{int(_lowercase ) + 1: v for k, v in idalabel.items()}
     __UpperCamelCase =			'background'
     __UpperCamelCase =			idalabel
     __UpperCamelCase =			{v: k for k, v in idalabel.items()}

     return config






def 		_A		(		)					-> Dict:


     """simple docstring"""


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






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase=False )					-> Optional[Any]:


     """simple docstring"""


     __UpperCamelCase =			get_mobilenet_va_config(_lowercase )

     # Load 🤗 model
     __UpperCamelCase =			MobileNetVaForImageClassification(_lowercase ).eval()

     # Load weights from TensorFlow checkpoint
     load_tf_weights_in_mobilenet_va(_lowercase					,  _lowercase					,  _lowercase )

     # Check outputs on an image, prepared by MobileNetV1ImageProcessor
     __UpperCamelCase =			MobileNetVaImageProcessor(
         crop_size={'width': config.image_size, 'height': config.image_size}					,  size={'shortest_edge': config.image_size + 32}					,  )
     __UpperCamelCase =			image_processor(images=prepare_img()					,  return_tensors='pt' )
     __UpperCamelCase =			model(**_lowercase )
     __UpperCamelCase =			outputs.logits

     assert logits.shape == (1, 10_01)

     if model_name == "mobilenet_v1_1.0_224":
          __UpperCamelCase =			torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
     elif model_name == "mobilenet_v1_0.75_192":
          __UpperCamelCase =			torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
     else:
          __UpperCamelCase =			None

     if expected_logits is not None:
          assert torch.allclose(logits[0, :3]					,  _lowercase					,  atol=1e-4 )

     Path(_lowercase ).mkdir(exist_ok=_lowercase )
     print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
     model.save_pretrained(_lowercase )
     print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
     image_processor.save_pretrained(_lowercase )

     if push_to_hub:
          print('Pushing to the hub...' )
          __UpperCamelCase =			'google/' + model_name
          image_processor.push_to_hub(_lowercase )
          model.push_to_hub(_lowercase )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''mobilenet_v1_1.0_224''',
        type=str,
        help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
    )
    parser.add_argument(
        '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )

    __snake_case   							=						parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )






#
# 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 		_A		(		*_lowercase )					-> Tuple:


     """simple docstring"""


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


__snake_case   							=						int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
__snake_case   							=						torch.device('''cuda''', local_rank)
__snake_case   							=						socket.gethostname()

__snake_case   							=						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
    __snake_case   							=						dist.get_rank()
    __snake_case   							=						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


