






'''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()
_A     :      Optional[Any]   					=logging.get_logger()

@dataclass
class _lowercase     :
							a    				=	42
							a    				=	field(default_factory=_lowercase		)
							a    				=	field(default_factory=_lowercase		)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Tensor      ,							UpperCamelCase__:    Tensor					):
														lowerCamelCase__ :     int	  =	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:    Tuple      ,							UpperCamelCase__:    Tensor					):
														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:    List[Any]					):
														# 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 _lowercase     :
							a    				=	42
							a    				=	42
							a    				=	0
							a    				=	field(default_factory=_lowercase		)
							a    				=	field(default_factory=_lowercase		)




							def __call__(							self:    Dict      ,							UpperCamelCase__:    Tensor					):
														lowerCamelCase__ :     Union[str, Any]	  =	Tracker(self.dest					)(UpperCamelCase__					).parametrized
														lowerCamelCase__ :     Any	  =	Tracker(self.src					)(UpperCamelCase__					).parametrized

														lowerCamelCase__ :     List[str]	  =	list(filter(lambda UpperCamelCase__					: type(UpperCamelCase__					) not in self.src_skip      ,							UpperCamelCase__					)					)
														lowerCamelCase__ :     Optional[int]	  =	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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = True				)				->	Dict:
							print(f'''Converting {name}...'''				)
							with torch.no_grad():
														lowerCamelCase__ :     int	  =	timm.create_model(UpperCamelCase    ,      pretrained=UpperCamelCase				).eval()
														lowerCamelCase__ :     Dict	  =	ResNetForImageClassification(UpperCamelCase				).eval()
														lowerCamelCase__ :     Tuple	  =	ModuleTransfer(src=UpperCamelCase    ,      dest=UpperCamelCase				)
														lowerCamelCase__ :     Dict	  =	torch.randn((1, 3, 224, 224)				)
														module_transfer(UpperCamelCase				)

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

							lowerCamelCase__ :     Optional[Any]	  =	f'''resnet{'-'.join(name.split('resnet'				)				)}'''
							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
														lowerCamelCase__ :     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=UpperCamelCase    ,      )

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase = None    ,      UpperCamelCase = True				)				->	List[Any]:
							lowerCamelCase__ :     Tuple	  =	"""imagenet-1k-id2label.json"""
							lowerCamelCase__ :     List[Any]	  =	1000
							lowerCamelCase__ :     Dict	  =	(1, num_labels)

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

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

							lowerCamelCase__ :     Optional[Any]	  =	partial(UpperCamelCase    ,      num_labels=UpperCamelCase    ,      idalabel=UpperCamelCase    ,      labelaid=UpperCamelCase				)

							lowerCamelCase__ :     str	  =	{
							    """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(UpperCamelCase    ,      names_to_config[model_name]    ,      UpperCamelCase    ,      UpperCamelCase				)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							return config, expected_shape


if __name__ == "__main__":
			_A     :      List[str]   					=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.''',
			)

			_A     :      Optional[Any]   					=parser.parse_args()
			_A     :      Path   					=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'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A     :      Union[str, Any]   					=logging.get_logger(__name__)

_A     :      List[Any]   					={
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class _lowercase     (    _lowercase		):
							a    				=	"""deformable_detr"""
							a    				=	{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Tuple=None      ,							UpperCamelCase__:    List[str]=3      ,							UpperCamelCase__:    Any=300      ,							UpperCamelCase__:    Optional[int]=1_024      ,							UpperCamelCase__:    int=6      ,							UpperCamelCase__:    str=1_024      ,							UpperCamelCase__:    Optional[Any]=8      ,							UpperCamelCase__:    Optional[Any]=6      ,							UpperCamelCase__:    Union[str, Any]=1_024      ,							UpperCamelCase__:    Tuple=8      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    Tuple=True      ,							UpperCamelCase__:    Any="relu"      ,							UpperCamelCase__:    Any=256      ,							UpperCamelCase__:    List[Any]=0.1      ,							UpperCamelCase__:    Optional[int]=0.0      ,							UpperCamelCase__:    Any=0.0      ,							UpperCamelCase__:    List[str]=0.02      ,							UpperCamelCase__:    str=1.0      ,							UpperCamelCase__:    Any=True      ,							UpperCamelCase__:    List[Any]=False      ,							UpperCamelCase__:    str="sine"      ,							UpperCamelCase__:    Optional[Any]="resnet50"      ,							UpperCamelCase__:    Optional[int]=True      ,							UpperCamelCase__:    Optional[int]=False      ,							UpperCamelCase__:    List[Any]=4      ,							UpperCamelCase__:    Any=4      ,							UpperCamelCase__:    int=4      ,							UpperCamelCase__:    int=False      ,							UpperCamelCase__:    Optional[Any]=300      ,							UpperCamelCase__:    str=False      ,							UpperCamelCase__:    int=1      ,							UpperCamelCase__:    Tuple=5      ,							UpperCamelCase__:    List[Any]=2      ,							UpperCamelCase__:    Optional[int]=1      ,							UpperCamelCase__:    int=1      ,							UpperCamelCase__:    Tuple=5      ,							UpperCamelCase__:    str=2      ,							UpperCamelCase__:    str=0.1      ,							UpperCamelCase__:    List[str]=0.25      ,							UpperCamelCase__:    Any=False      ,							**UpperCamelCase__:    Optional[Any]      ,							):
														if backbone_config is not None and use_timm_backbone:
																					raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

														if not use_timm_backbone:
																					if backbone_config is None:
																												logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																												lowerCamelCase__ :     Union[str, Any]	  =	CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
																					elif isinstance(UpperCamelCase__      ,							UpperCamelCase__					):
																												lowerCamelCase__ :     Tuple	  =	backbone_config.get("""model_type"""					)
																												lowerCamelCase__ :     Dict	  =	CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__ :     Optional[Any]	  =	config_class.from_dict(UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	use_timm_backbone
														lowerCamelCase__ :     Tuple	  =	backbone_config
														lowerCamelCase__ :     Union[str, Any]	  =	num_channels
														lowerCamelCase__ :     str	  =	num_queries
														lowerCamelCase__ :     int	  =	max_position_embeddings
														lowerCamelCase__ :     str	  =	d_model
														lowerCamelCase__ :     Dict	  =	encoder_ffn_dim
														lowerCamelCase__ :     Union[str, Any]	  =	encoder_layers
														lowerCamelCase__ :     int	  =	encoder_attention_heads
														lowerCamelCase__ :     str	  =	decoder_ffn_dim
														lowerCamelCase__ :     Optional[Any]	  =	decoder_layers
														lowerCamelCase__ :     Optional[int]	  =	decoder_attention_heads
														lowerCamelCase__ :     int	  =	dropout
														lowerCamelCase__ :     Optional[Any]	  =	attention_dropout
														lowerCamelCase__ :     List[Any]	  =	activation_dropout
														lowerCamelCase__ :     List[Any]	  =	activation_function
														lowerCamelCase__ :     int	  =	init_std
														lowerCamelCase__ :     Dict	  =	init_xavier_std
														lowerCamelCase__ :     Tuple	  =	encoder_layerdrop
														lowerCamelCase__ :     str	  =	auxiliary_loss
														lowerCamelCase__ :     int	  =	position_embedding_type
														lowerCamelCase__ :     Tuple	  =	backbone
														lowerCamelCase__ :     Tuple	  =	use_pretrained_backbone
														lowerCamelCase__ :     Optional[int]	  =	dilation
														# deformable attributes
														lowerCamelCase__ :     Optional[int]	  =	num_feature_levels
														lowerCamelCase__ :     Tuple	  =	encoder_n_points
														lowerCamelCase__ :     Tuple	  =	decoder_n_points
														lowerCamelCase__ :     Dict	  =	two_stage
														lowerCamelCase__ :     int	  =	two_stage_num_proposals
														lowerCamelCase__ :     Any	  =	with_box_refine
														if two_stage is True and with_box_refine is False:
																					raise ValueError("""If two_stage is True, with_box_refine must be True."""					)
														# Hungarian matcher
														lowerCamelCase__ :     Optional[int]	  =	class_cost
														lowerCamelCase__ :     List[str]	  =	bbox_cost
														lowerCamelCase__ :     Any	  =	giou_cost
														# Loss coefficients
														lowerCamelCase__ :     Union[str, Any]	  =	mask_loss_coefficient
														lowerCamelCase__ :     Tuple	  =	dice_loss_coefficient
														lowerCamelCase__ :     int	  =	bbox_loss_coefficient
														lowerCamelCase__ :     Optional[Any]	  =	giou_loss_coefficient
														lowerCamelCase__ :     Dict	  =	eos_coefficient
														lowerCamelCase__ :     Any	  =	focal_alpha
														lowerCamelCase__ :     Optional[Any]	  =	disable_custom_kernels
														super().__init__(is_encoder_decoder=UpperCamelCase__      ,							**UpperCamelCase__					)




							@property
							def 		lowerCamelCase_    (							self:    Dict					):
														return self.encoder_attention_heads




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return self.d_model




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Optional[int]	  =	copy.deepcopy(self.__dict__					)
														if self.backbone_config is not None:
																					lowerCamelCase__ :     int	  =	self.backbone_config.to_dict()
														lowerCamelCase__ :     int	  =	self.__class__.model_type
														return output

