


'''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()
lowerCAmelCase__  =     logging.get_logger(__name__)







@dataclass
class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42
   _lowercase    						= field(default_factory=UpperCamelCase							)
   _lowercase    						= field(default_factory=UpperCamelCase							)




   def   __lowerCAmelCase						(	self	:					Optional[Any]					, lowerCamelCase	:					Union[str, Any]					, lowerCamelCase	:					Tensor					, lowerCamelCase	:					Tensor						)->	List[Any]:
          snake_case__					:	Tuple          =					len(list(m.modules()						)						) == 1 or isinstance(lowerCamelCase					, nn.Convad						) or isinstance(lowerCamelCase					, nn.BatchNormad						)
          if has_not_submodules:
                 if not isinstance(lowerCamelCase					, lowerCamelCase						):
                        self.traced.append(lowerCamelCase						)




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







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




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







@dataclass
class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42
   _lowercase    						= 42
   _lowercase    						= 0
   _lowercase    						= field(default_factory=UpperCamelCase							)
   _lowercase    						= field(default_factory=UpperCamelCase							)




   def __call__(	self	:					Optional[Any]					, lowerCamelCase	:					Tensor						)->	str:
          snake_case__					:	Optional[int]          =					Tracker(self.dest						)(lowerCamelCase						).parametrized
          snake_case__					:	Any          =					Tracker(self.src						)(lowerCamelCase						).parametrized

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

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

          for dest_m, src_m in zip(lowerCamelCase					, lowerCamelCase						):
                 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     ):






       """simple docstring"""





       snake_case__					:	Any          =					from_model.state_dict()
       snake_case__					:	Tuple          =					our_model.state_dict()
       snake_case__					:	int          =					our_model.config
       snake_case__					:	Tuple          =					[]
       for stage_idx in range(len(config.hidden_sizes     )     ):
              for block_id in range(config.depths[stage_idx]     ):
                     snake_case__					:	Union[str, Any]          =					f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
                     snake_case__					:	Optional[Any]          =					f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

                     all_keys.append((from_key, to_key)     )
                     snake_case__					:	Tuple          =					f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
                     snake_case__					:	int          =					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:
              snake_case__					:	str          =					from_state_dict.pop(_A     )

       our_model.load_state_dict(_A     )
       return our_model



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase = True    ,       ):






       """simple docstring"""





       print(f"""Downloading weights for {name}..."""     )
       snake_case__					:	Optional[Any]          =					cached_download(_A     )
       print(f"""Converting {name}..."""     )
       snake_case__					:	List[Any]          =					torch.load(_A     )["""state_dict"""]
       from_model.load_state_dict(_A     )
       from_model.eval()
       with torch.no_grad():
              snake_case__					:	Any          =					VanForImageClassification(_A     ).eval()
              snake_case__					:	Tuple          =					ModuleTransfer(src=_A    ,       dest=_A     )
              snake_case__					:	Tuple          =					torch.randn((1, 3, 224, 224)     )
              module_transfer(_A     )
              snake_case__					:	List[Any]          =					copy_parameters(_A    ,       _A     )

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

       snake_case__					:	Union[str, Any]          =					name
       print(_A     )

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

              # we can use the convnext one
              snake_case__					:	List[str]          =					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=_A    ,       )

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



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = None    ,       UpperCAmelCase = True     ):






       """simple docstring"""





       snake_case__					:	Dict          =					"""imagenet-1k-id2label.json"""
       snake_case__					:	Dict          =					1000

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

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

       snake_case__					:	Any          =					partial(_A    ,       num_labels=_A    ,       idalabel=_A    ,       labelaid=_A     )

       snake_case__					:	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]    ,       ),
       }

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

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


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

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


lowerCamelCase_					:			Any     						=				{
    '''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:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SamModel''',
					    '''SamPreTrainedModel''',
					]
try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFSamModel''',
					    '''TFSamPreTrainedModel''',
					]
try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				['''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

					lowerCamelCase_					:			Optional[Any]     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
