



'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase	 =       logging.get_logger(__name__)

lowerCamelCase	 =       {
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    """swinv2"""

				lowerCAmelCase__					    =    {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(							self  :  str  ,       _lowerCAmelCase  :  int=2_2_4  ,       _lowerCAmelCase  :  str=4  ,       _lowerCAmelCase  :  int=3  ,       _lowerCAmelCase  :  Optional[Any]=9_6  ,       _lowerCAmelCase  :  str=[2, 2, 6, 2]  ,       _lowerCAmelCase  :  Dict=[3, 6, 1_2, 2_4]  ,       _lowerCAmelCase  :  Any=7  ,       _lowerCAmelCase  :  Optional[int]=4.0  ,       _lowerCAmelCase  :  Optional[Any]=True  ,       _lowerCAmelCase  :  Any=0.0  ,       _lowerCAmelCase  :  Union[str, Any]=0.0  ,       _lowerCAmelCase  :  Any=0.1  ,       _lowerCAmelCase  :  Tuple="gelu"  ,       _lowerCAmelCase  :  Union[str, Any]=False  ,       _lowerCAmelCase  :  Optional[int]=0.02  ,       _lowerCAmelCase  :  Any=1e-5  ,       _lowerCAmelCase  :  Tuple=3_2  ,       **_lowerCAmelCase  :  Any  ,       ):







											'''simple docstring'''

											super().__init__(**_lowerCAmelCase)

											__lowercase       	=image_size
											__lowercase       	=patch_size
											__lowercase       	=num_channels
											__lowercase       	=embed_dim
											__lowercase       	=depths
											__lowercase       	=len(_lowerCAmelCase)
											__lowercase       	=num_heads
											__lowercase       	=window_size
											__lowercase       	=mlp_ratio
											__lowercase       	=qkv_bias
											__lowercase       	=hidden_dropout_prob
											__lowercase       	=attention_probs_dropout_prob
											__lowercase       	=drop_path_rate
											__lowercase       	=hidden_act
											__lowercase       	=use_absolute_embeddings
											__lowercase       	=layer_norm_eps
											__lowercase       	=initializer_range
											__lowercase       	=encoder_stride
											# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											__lowercase       	=int(embed_dim * 2 ** (len(_lowerCAmelCase) - 1))
											__lowercase       	=(0, 0, 0, 0)








'''simple docstring'''



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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	 =       logging.get_logger()





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  Tensor  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=len(list(m.modules())) == 1 or isinstance(_lowerCAmelCase  ,       nn.Convad) or isinstance(_lowerCAmelCase  ,       nn.BatchNormad)
											if has_not_submodules:
																		self.traced.append(_lowerCAmelCase)

				def __call__(							self  :  Dict  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											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  :  Any):







											'''simple docstring'''

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





@dataclass
class _UpperCamelCase		:





				'''simple docstring'''





				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    42
				lowerCAmelCase__					    =    0
				lowerCAmelCase__					    =    field(default_factory=A		)
				lowerCAmelCase__					    =    field(default_factory=A		)

				def __call__(							self  :  Any  ,       _lowerCAmelCase  :  Tensor):







											'''simple docstring'''

											__lowercase       	=Tracker(self.dest)(_lowerCAmelCase).parametrized
											__lowercase       	=Tracker(self.src)(_lowerCAmelCase).parametrized

											__lowercase       	=list(filter(lambda _lowerCAmelCase: type(_lowerCAmelCase) not in self.src_skip  ,       _lowerCAmelCase))
											__lowercase       	=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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							print(f"""Converting {name}..."""					)
							with torch.no_grad():
														__lowercase       	=timm.create_model(_lowerCAmelCase  ,   pretrained=_lowerCAmelCase					).eval()
														__lowercase       	=ResNetForImageClassification(_lowerCAmelCase					).eval()
														__lowercase       	=ModuleTransfer(src=_lowerCAmelCase  ,   dest=_lowerCAmelCase					)
														__lowercase       	=torch.randn((1, 3, 224, 224)					)
														module_transfer(_lowerCAmelCase					)

							assert torch.allclose(from_model(_lowerCAmelCase					)  ,   our_model(_lowerCAmelCase					).logits					), "The model logits don't match the original one."

							__lowercase       	=f"""resnet{'-'.join(name.split('resnet'					)					)}"""
							print(_lowerCAmelCase					)

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

														# we can use the convnext one
														__lowercase       	=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=_lowerCAmelCase  ,   )

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = True					):



							"""simple docstring"""



							__lowercase       	='imagenet-1k-id2label.json'
							__lowercase       	=1_000
							__lowercase       	=(1, num_labels)

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

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

							__lowercase       	=partial(_lowerCAmelCase  ,   num_labels=_lowerCAmelCase  ,   idalabel=_lowerCAmelCase  ,   labelaid=_lowerCAmelCase					)

							__lowercase       	={
							    'resnet18': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]  ,   hidden_sizes=[64, 128, 256, 512]  ,   layer_type='basic'					),
							    'resnet26': ImageNetPreTrainedConfig(
							        depths=[2, 2, 2, 2]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet34': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]  ,   hidden_sizes=[64, 128, 256, 512]  ,   layer_type='basic'					),
							    'resnet50': ImageNetPreTrainedConfig(
							        depths=[3, 4, 6, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet101': ImageNetPreTrainedConfig(
							        depths=[3, 4, 23, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							    'resnet152': ImageNetPreTrainedConfig(
							        depths=[3, 8, 36, 3]  ,   hidden_sizes=[256, 512, 1_024, 2_048]  ,   layer_type='bottleneck'					),
							}

							if model_name:
														convert_weight_and_push(_lowerCAmelCase  ,   names_to_config[model_name]  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)
							return config, expected_shape


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: resnet18,26,34,50,101,152. 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(
		    """--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)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



