





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()
_UpperCAmelCase				       = logging.get_logger(__name__)



@dataclass
class 							_UpperCamelCase    :
       _UpperCamelCase :    nn.Module
       _UpperCamelCase :    List[nn.Module]   			=      field(default_factory=lowerCAmelCase_   )
       _UpperCamelCase :    list   			=      field(default_factory=lowerCAmelCase_   )

       def lowercase       (						self:       Optional[int]    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       Tensor    ,      _SCREAMING_SNAKE_CASE:       Tensor						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_      				=     len(list(m.modules()						)						) == 1 or isinstance(_SCREAMING_SNAKE_CASE    ,      nn.Convad						) or isinstance(_SCREAMING_SNAKE_CASE    ,      nn.BatchNormad						)
              if has_not_submodules:
                     if not isinstance(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						):
                            self.traced.append(_SCREAMING_SNAKE_CASE						)

       def __call__(						self:       str    ,      _SCREAMING_SNAKE_CASE:       Tensor						)				->       int:




              """simple docstring"""


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



       @property
       def lowercase       (						self:       Dict						)				->       List[Any]:




              """simple docstring"""


              return list(filter(lambda _SCREAMING_SNAKE_CASE						: len(list(x.state_dict().keys()						)						) > 0    ,      self.traced						)						)



@dataclass
class 							_UpperCamelCase    :
       _UpperCamelCase :    nn.Module
       _UpperCamelCase :    nn.Module
       _UpperCamelCase :    int   			=      0
       _UpperCamelCase :    List   			=      field(default_factory=lowerCAmelCase_   )
       _UpperCamelCase :    List   			=      field(default_factory=lowerCAmelCase_   )

       def __call__(						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Tensor						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_      				=     Tracker(self.dest						)(_SCREAMING_SNAKE_CASE						).parametrized
              UpperCamelCase_      				=     Tracker(self.src						)(_SCREAMING_SNAKE_CASE						).parametrized

              UpperCamelCase_      				=     list(filter(lambda _SCREAMING_SNAKE_CASE						: type(_SCREAMING_SNAKE_CASE						) not in self.src_skip    ,      _SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase_      				=     list(filter(lambda _SCREAMING_SNAKE_CASE						: type(_SCREAMING_SNAKE_CASE						) not in self.dest_skip    ,      _SCREAMING_SNAKE_CASE						)						)

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

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




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						nn.Module:
       # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
       UpperCamelCase_      				=     from_model.state_dict()
       UpperCamelCase_      				=     our_model.state_dict()
       UpperCamelCase_      				=     our_model.config
       UpperCamelCase_      				=     []
       for stage_idx in range(len(config.hidden_sizes     )     ):
              for block_id in range(config.depths[stage_idx]     ):
                     UpperCamelCase_      				=     F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
                     UpperCamelCase_      				=     F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

                     all_keys.append((from_key, to_key)     )
                     UpperCamelCase_      				=     F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                     UpperCamelCase_      				=     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:
              UpperCamelCase_      				=     from_state_dict.pop(UpperCamelCase_     )

       our_model.load_state_dict(UpperCamelCase_     )
       return our_model




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_ = True    ,						)   ->						int:
       print(F'''Downloading weights for {name}...'''     )
       UpperCamelCase_      				=     cached_download(UpperCamelCase_     )
       print(F'''Converting {name}...'''     )
       UpperCamelCase_      				=     torch.load(UpperCamelCase_     )["state_dict"]
       from_model.load_state_dict(UpperCamelCase_     )
       from_model.eval()
       with torch.no_grad():
              UpperCamelCase_      				=     VanForImageClassification(UpperCamelCase_     ).eval()
              UpperCamelCase_      				=     ModuleTransfer(src=UpperCamelCase_    ,						dest=UpperCamelCase_     )
              UpperCamelCase_      				=     torch.randn((1, 3, 224, 224)     )
              module_transfer(UpperCamelCase_     )
              UpperCamelCase_      				=     copy_parameters(UpperCamelCase_    ,						UpperCamelCase_     )

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

       UpperCamelCase_      				=     name
       print(UpperCamelCase_     )

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

              # we can use the convnext one
              UpperCamelCase_      				=     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=UpperCamelCase_    ,						)

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




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_ = None    ,						UpperCamelCase_ = True     )   ->						List[str]:
       UpperCamelCase_      				=     "imagenet-1k-id2label.json"
       UpperCamelCase_      				=     1000

       UpperCamelCase_      				=     "huggingface/label-files"
       UpperCamelCase_      				=     num_labels
       UpperCamelCase_      				=     json.load(open(hf_hub_download(UpperCamelCase_    ,						UpperCamelCase_    ,						repo_type="dataset"     )    ,						"r"     )     )
       UpperCamelCase_      				=     {int(UpperCamelCase_     ): v for k, v in idalabel.items()}

       UpperCamelCase_      				=     idalabel
       UpperCamelCase_      				=     {v: k for k, v in idalabel.items()}

       UpperCamelCase_      				=     partial(UpperCamelCase_    ,						num_labels=UpperCamelCase_    ,						idalabel=UpperCamelCase_    ,						labelaid=UpperCamelCase_     )

       UpperCamelCase_      				=     {
           "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]    ,						),
       }

       UpperCamelCase_      				=     {
           "van-tiny": van_tiny,
           "van-small": van_small,
           "van-base": van_base,
           "van-large": van_large,
       }

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


if __name__ == "__main__":
     _UpperCAmelCase				       = 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.',
     )

     _UpperCAmelCase				       = parser.parse_args()
     _UpperCAmelCase				       = args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     _UpperCAmelCase				       = 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)








def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						list:
       UpperCamelCase_      				=     sorted(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[2]    ,						reverse=UpperCamelCase_     )

       # Create a list of size equal to the maximum deadline
       # and initialize it with -1
       UpperCamelCase_      				=     max(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[1]     )[1]
       UpperCamelCase_      				=     [-1] * max_deadline

       # Finding the maximum profit and the count of jobs
       UpperCamelCase_      				=     0
       UpperCamelCase_      				=     0
       for job in jobs:
              # Find a free time slot for this job
              # (Note that we start from the last possible slot)
              for i in range(job[1] - 1    ,						-1    ,						-1     ):
                     if time_slots[i] == -1:
                            UpperCamelCase_      				=     job[0]
                            count += 1
                            max_profit += job[2]
                            break
       return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

