
'''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()
lowercase__						       =      logging.get_logger()






@dataclass
class 	snake_case__       :







		"""simple docstring"""

		lowerCamelCase     							=						42
		lowerCamelCase     							=						field(default_factory=__SCREAMING_SNAKE_CASE					)
		lowerCamelCase     							=						field(default_factory=__SCREAMING_SNAKE_CASE					)



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Tensor  ,	UpperCamelCase__				:			Tensor      )						->			Optional[Any]:






				"""simple docstring"""







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



		def __call__(      self				:			int  ,	UpperCamelCase__				:			Tensor      )						->			Tuple:






				"""simple docstring"""







				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       lowerCAmelCase				(      self				:			Any      )						->			Any:






				"""simple docstring"""







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







@dataclass
class 	snake_case__       :







		"""simple docstring"""

		lowerCamelCase     							=						42
		lowerCamelCase     							=						42
		lowerCamelCase     							=						0
		lowerCamelCase     							=						field(default_factory=__SCREAMING_SNAKE_CASE					)
		lowerCamelCase     							=						field(default_factory=__SCREAMING_SNAKE_CASE					)



		def __call__(      self				:			str  ,	UpperCamelCase__				:			Tensor      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						Tracker(self.dest      )(UpperCamelCase__      ).parametrized
				snake_case  :       Tuple     			=						Tracker(self.src      )(UpperCamelCase__      ).parametrized

				snake_case  :       Dict     			=						list(filter(lambda UpperCamelCase__      : type(UpperCamelCase__      ) not in self.src_skip  ,	UpperCamelCase__      )      )
				snake_case  :       Tuple     			=						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						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ = True			)  ->							Dict:


		'''simple docstring'''


		print(F'Converting {name}...'			)
		with torch.no_grad():
				snake_case  :       List[str]     			=						timm.create_model(SCREAMING_SNAKE_CASE__ ,       pretrained=SCREAMING_SNAKE_CASE__			).eval()
				snake_case  :       str     			=						ResNetForImageClassification(SCREAMING_SNAKE_CASE__			).eval()
				snake_case  :       Union[str, Any]     			=						ModuleTransfer(src=SCREAMING_SNAKE_CASE__ ,       dest=SCREAMING_SNAKE_CASE__			)
				snake_case  :       List[str]     			=						torch.randn((1, 3, 224, 224)			)
				module_transfer(SCREAMING_SNAKE_CASE__			)

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

		snake_case  :       Dict     			=						F'resnet{"-".join(name.split("resnet"			)			)}'
		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
				snake_case  :       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=SCREAMING_SNAKE_CASE__ ,       )

				print(F'Pushed {checkpoint_name}'			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ = None ,       SCREAMING_SNAKE_CASE__ = True			)  ->							Any:


		'''simple docstring'''


		snake_case  :       Any     			=						'''imagenet-1k-id2label.json'''
		snake_case  :       Optional[Any]     			=						1000
		snake_case  :       Optional[Any]     			=						(1, num_labels)

		snake_case  :       Dict     			=						'''huggingface/label-files'''
		snake_case  :       str     			=						num_labels
		snake_case  :       Optional[Any]     			=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       repo_type='''dataset'''			) ,       '''r'''			)			)
		snake_case  :       Dict     			=						{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}

		snake_case  :       int     			=						idalabel
		snake_case  :       Union[str, Any]     			=						{v: k for k, v in idalabel.items()}

		snake_case  :       int     			=						partial(SCREAMING_SNAKE_CASE__ ,       num_labels=SCREAMING_SNAKE_CASE__ ,       idalabel=SCREAMING_SNAKE_CASE__ ,       labelaid=SCREAMING_SNAKE_CASE__			)

		snake_case  :       Optional[int]     			=						{
		    '''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, 1024, 2048] ,       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, 1024, 2048] ,       layer_type='''bottleneck'''			),
		    '''resnet101''': ImageNetPreTrainedConfig(
		        depths=[3, 4, 23, 3] ,       hidden_sizes=[256, 512, 1024, 2048] ,       layer_type='''bottleneck'''			),
		    '''resnet152''': ImageNetPreTrainedConfig(
		        depths=[3, 8, 36, 3] ,       hidden_sizes=[256, 512, 1024, 2048] ,       layer_type='''bottleneck'''			),
		}

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


if __name__ == "__main__":
						lowercase__						       =      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.",
						)

						lowercase__						       =      parser.parse_args()
						lowercase__						       =      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)




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

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



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


