




import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       logging.get_logger(__name__)




@dataclass
class 		UpperCamelCase__     :

    '''simple docstring'''



    lowerCamelCase_				:     List[Any]     					=				4_2
    lowerCamelCase_				:     List[Any]     					=				field(default_factory=lowerCAmelCase__					)
    lowerCamelCase_				:     List[Any]     					=				field(default_factory=lowerCAmelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				List[Any]:
           lowerCamelCase       :				str      			= len(list(m.modules()					)					) == 1 or isinstance(UpperCamelCase__					,  nn.Convad					) or isinstance(UpperCamelCase__					,  nn.BatchNormad					)
           if has_not_submodules:
                  if not isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                         self.traced.append(UpperCamelCase__					)







    def __call__(   self					,  UpperCamelCase__					)					->				List[Any]:






           for m in self.module.modules():
                  self.handles.append(m.register_forward_hook(self._forward_hook					)					)
           self.module(UpperCamelCase__					)
           [x.remove() for x in self.handles]
           return self







    @property
    def    _lowercase					(   self					)					->				Union[str, Any]:
           return list(filter(lambda UpperCamelCase__					: len(list(x.state_dict().keys()					)					) > 0					,  self.traced					)					)




@dataclass
class 		UpperCamelCase__     :

    '''simple docstring'''



    lowerCamelCase_				:     Any     					=				4_2
    lowerCamelCase_				:     Optional[Any]     					=				4_2
    lowerCamelCase_				:     Optional[int]     					=				0
    lowerCamelCase_				:     Any     					=				field(default_factory=lowerCAmelCase__					)
    lowerCamelCase_				:     List[Any]     					=				field(default_factory=lowerCAmelCase__					)







    def __call__(   self					,  UpperCamelCase__					)					->				List[Any]:
           lowerCamelCase       :				Tuple      			= Tracker(self.dest					)(UpperCamelCase__					).parametrized
           lowerCamelCase       :				Dict      			= Tracker(self.src					)(UpperCamelCase__					).parametrized

           lowerCamelCase       :				Optional[int]      			= list(filter(lambda UpperCamelCase__					: type(UpperCamelCase__					) not in self.src_skip					,  UpperCamelCase__					)					)
           lowerCamelCase       :				Optional[Any]      			= list(filter(lambda UpperCamelCase__					: type(UpperCamelCase__					) not in self.dest_skip					,  UpperCamelCase__					)					)

           if len(UpperCamelCase__					) != len(UpperCamelCase__					):
                  raise Exception(
                      F'''Numbers of operations are different. Source module has {len(UpperCamelCase__					)} operations while'''
                      F''' destination module has {len(UpperCamelCase__					)}.'''					)

           for dest_m, src_m in zip(UpperCamelCase__					,  UpperCamelCase__					):
                  dest_m.load_state_dict(src_m.state_dict()					)
                  if self.verbose == 1:
                         print(F'''Transfered from={src_m} to={dest_m}'''					)




def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			nn.Module:
       lowerCamelCase       :				Any      			= from_model.state_dict()
       lowerCamelCase       :				Union[str, Any]      			= our_model.state_dict()
       lowerCamelCase       :				int      			= our_model.config
       lowerCamelCase       :				Dict      			= []
       for stage_idx in range(len(config.hidden_sizes   )   ):
              for block_id in range(config.depths[stage_idx]   ):
                     lowerCamelCase       :				int      			= f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
                     lowerCamelCase       :				Dict      			= f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

                     all_keys.append((from_key, to_key)   )
                     lowerCamelCase       :				Union[str, Any]      			= f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                     lowerCamelCase       :				Dict      			= f'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

                     all_keys.append((from_key, to_key)   )

       for from_key, to_key in all_keys:
              lowerCamelCase       :				Union[str, Any]      			= from_state_dict.pop(SCREAMING_SNAKE_CASE_   )

       our_model.load_state_dict(SCREAMING_SNAKE_CASE_   )
       return our_model

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = True			,)     ->			List[Any]:
       print(f'''Downloading weights for {name}...'''   )
       lowerCamelCase       :				Tuple      			= cached_download(SCREAMING_SNAKE_CASE_   )
       print(f'''Converting {name}...'''   )
       lowerCamelCase       :				Any      			= torch.load(SCREAMING_SNAKE_CASE_   )["state_dict"]
       from_model.load_state_dict(SCREAMING_SNAKE_CASE_   )
       from_model.eval()
       with torch.no_grad():
              lowerCamelCase       :				Any      			= VanForImageClassification(SCREAMING_SNAKE_CASE_   ).eval()
              lowerCamelCase       :				str      			= ModuleTransfer(src=SCREAMING_SNAKE_CASE_			,dest=SCREAMING_SNAKE_CASE_   )
              lowerCamelCase       :				str      			= torch.randn((1, 3, 224, 224)   )
              module_transfer(SCREAMING_SNAKE_CASE_   )
              lowerCamelCase       :				Dict      			= copy_parameters(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_   )

       if not torch.allclose(from_model(SCREAMING_SNAKE_CASE_   )			,our_model(SCREAMING_SNAKE_CASE_   ).logits   ):
              raise ValueError("The model logits don't match the original one."   )

       lowerCamelCase       :				str      			= name
       print(SCREAMING_SNAKE_CASE_   )

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name			,commit_message="Add model"			,use_temp_dir=SCREAMING_SNAKE_CASE_			,)

              # we can use the convnext one
              lowerCamelCase       :				List[Any]      			= AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name			,commit_message="Add image processor"			,use_temp_dir=SCREAMING_SNAKE_CASE_			,)

              print(f'''Pushed {checkpoint_name}'''   )

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = None			,_SCREAMING_SNAKE_CASE = True   )     ->			Any:
       lowerCamelCase       :				int      			= "imagenet-1k-id2label.json"
       lowerCamelCase       :				int      			= 1000

       lowerCamelCase       :				List[str]      			= "huggingface/label-files"
       lowerCamelCase       :				Dict      			= num_labels
       lowerCamelCase       :				int      			= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,repo_type="dataset"   )			,"r"   )   )
       lowerCamelCase       :				List[str]      			= {int(SCREAMING_SNAKE_CASE_   ): v for k, v in idalabel.items()}

       lowerCamelCase       :				Tuple      			= idalabel
       lowerCamelCase       :				List[Any]      			= {v: k for k, v in idalabel.items()}

       lowerCamelCase       :				Optional[Any]      			= partial(SCREAMING_SNAKE_CASE_			,num_labels=SCREAMING_SNAKE_CASE_			,idalabel=SCREAMING_SNAKE_CASE_			,labelaid=SCREAMING_SNAKE_CASE_   )

       lowerCamelCase       :				List[Any]      			= {
           "van-tiny": ImageNetPreTrainedConfig(
               hidden_sizes=[32, 64, 160, 256]			,depths=[3, 3, 5, 2]			,mlp_ratios=[8, 8, 4, 4]			,),
           "van-small": ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]			,depths=[2, 2, 4, 2]			,mlp_ratios=[8, 8, 4, 4]			,),
           "van-base": ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]			,depths=[3, 3, 12, 3]			,mlp_ratios=[8, 8, 4, 4]			,),
           "van-large": ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]			,depths=[3, 5, 27, 3]			,mlp_ratios=[8, 8, 4, 4]			,),
       }

       lowerCamelCase       :				Union[str, Any]      			= {
           "van-tiny": van_tiny,
           "van-small": van_small,
           "van-base": van_base,
           "van-large": van_large,
       }

       lowerCamelCase       :				Any      			= {
           "van-tiny": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
           ),
           "van-small": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
           ),
           "van-base": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
           ),
           "van-large": (
               "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
           ),
       }

       if model_name:
              convert_weight_and_push(
                  SCREAMING_SNAKE_CASE_			,names_to_config[model_name]			,checkpoint=names_to_original_checkpoints[model_name]			,from_model=names_to_original_models[model_name]()			,save_directory=SCREAMING_SNAKE_CASE_			,push_to_hub=SCREAMING_SNAKE_CASE_			,)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,checkpoint=names_to_original_checkpoints[model_name]			,from_model=names_to_original_models[model_name]()			,save_directory=SCREAMING_SNAKE_CASE_			,push_to_hub=SCREAMING_SNAKE_CASE_			,)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model-name',
           default=None,
           type=str,
           help=(
               'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
               ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_folder_path',
           default=None,
           type=Path,
           required=True,
           help='Path to the output PyTorch model directory.',
       )
       parser.add_argument(
           '--van_dir',
           required=True,
           type=Path,
           help=(
               'A path to VAN\'s original implementation directory. You can download from here:'
               ' https://github.com/Visual-Attention-Network/VAN-Classification'
           ),
       )
       parser.add_argument(
           '--push_to_hub',
           default=True,
           type=bool,
           required=False,
           help='If True, push model and image processor to the hub.',
       )

       SCREAMING_SNAKE_CASE__	:				List[Any]  =       parser.parse_args()
       SCREAMING_SNAKE_CASE__	:				List[Any]  =       args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       args.van_dir
       # append the path to the parents to maskformer dir
       sys.path.append(str(van_dir.parent))
       from van.models.van import van_base, van_large, van_small, van_tiny

       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,start_method="spawn"   )


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
       # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

       # on-demand GPU
       # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
       SCREAMING_SNAKE_CASE__	:				Tuple  =       rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
       gpu.up_if_not()

       # on-prem GPU
       # gpu = rh.cluster(
       #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
       #       )

       # Set up remote function
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       launch_train_gpu(config, train_args, stream_logs=True)

       # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
       # gpu.install_packages(reqs)
       # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






