
"""simple docstring"""






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 							snake_case       :
   a_					:		nn.Module
   a_					:		List[nn.Module]     					=     field(default_factory=SCREAMING_SNAKE_CASE_		)
   a_					:		list     					=     field(default_factory=SCREAMING_SNAKE_CASE_		)







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->Optional[Any]:
      a_						   =     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)   ->int:
      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    UpperCAmelCase__	( self)   ->Dict:
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda __UpperCAmelCase: len(list(x.state_dict().keys())) > 0       ,							self.traced))






@dataclass
class 							snake_case       :
   a_					:		nn.Module
   a_					:		nn.Module
   a_					:		int     					=     0
   a_					:		List     					=     field(default_factory=SCREAMING_SNAKE_CASE_		)
   a_					:		List     					=     field(default_factory=SCREAMING_SNAKE_CASE_		)







   def __call__( self       ,							__UpperCAmelCase)   ->str:
      a_						   =     Tracker(self.dest)(__UpperCAmelCase).parametrized
      a_						   =     Tracker(self.src)(__UpperCAmelCase).parametrized

      a_						   =     list(filter(lambda __UpperCAmelCase: type(__UpperCAmelCase) not in self.src_skip       ,							__UpperCAmelCase))
      a_						   =     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    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->nn.Module:

   """simple docstring"""



   a_						   =     from_model.state_dict()
   a_						   =     our_model.state_dict()
   a_						   =     our_model.config
   a_						   =     []
   for stage_idx in range(len(config.hidden_sizes     )     ):
      for block_id in range(config.depths[stage_idx]     ):
         a_						   =     F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
         a_						   =     F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

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

   our_model.load_state_dict(UpperCAmelCase     )
   return our_model
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase = True ,   )						->List[str]:

   """simple docstring"""



   print(F'''Downloading weights for {name}...'''     )
   a_						   =     cached_download(UpperCAmelCase     )
   print(F'''Converting {name}...'''     )
   a_						   =     torch.load(UpperCAmelCase     )["state_dict"]
   from_model.load_state_dict(UpperCAmelCase     )
   from_model.eval()
   with torch.no_grad():
      a_						   =     VanForImageClassification(UpperCAmelCase     ).eval()
      a_						   =     ModuleTransfer(src=UpperCAmelCase ,   dest=UpperCAmelCase     )
      a_						   =     torch.randn((1, 3, 224, 224)     )
      module_transfer(UpperCAmelCase     )
      a_						   =     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."     )

   a_						   =     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
      a_						   =     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    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase = None ,   UpperCAmelCase = True     )						->List[Any]:

   """simple docstring"""



   a_						   =     "imagenet-1k-id2label.json"
   a_						   =     1_000

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

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

   a_						   =     partial(UpperCAmelCase ,   num_labels=UpperCAmelCase ,   idalabel=UpperCAmelCase ,   labelaid=UpperCAmelCase     )

   a_						   =     {
       "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] ,   ),
   }

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

   a_						   =     {
       "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)

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_						   =   {
    'configuration_sam': [
        'SAM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SamConfig',
        'SamMaskDecoderConfig',
        'SamPromptEncoderConfig',
        'SamVisionConfig',
    ],
    'processing_sam': ['SamProcessor'],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SamModel',
          'SamPreTrainedModel',
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSamModel',
          'TFSamPreTrainedModel',
      ]
try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   ['SamImageProcessor']


if TYPE_CHECKING:
      from .configuration_sam import (
          SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SamConfig,
          SamMaskDecoderConfig,
          SamPromptEncoderConfig,
          SamVisionConfig,
      )
      from .processing_sam import SamProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_sam import SamImageProcessor

else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)