"""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()
UpperCAmelCase       =  logging.get_logger()







@dataclass
class      UpperCAmelCase_       :
							snake_case__        =      42
							snake_case__        =      field(default_factory=__SCREAMING_SNAKE_CASE)
							snake_case__        =      field(default_factory=__SCREAMING_SNAKE_CASE)






							def       _UpperCamelCase			(		self :      Union[str, Any]     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      List[Any]  )       ->       int:
												_UpperCamelCase      						=	len(list(m.modules()  )  ) == 1 or isinstance(_a     ,						nn.Convad  ) or isinstance(_a     ,						nn.BatchNormad  )
												if has_not_submodules:
																	self.traced.append(_a  )






							def __call__(		self :      int     ,						__UpperCamelCase :      str  )       ->       int:
												for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook  )  )
												self.module(_a  )
												[x.remove() for x in self.handles]
												return self






							@property
							def       _UpperCamelCase			(		self :      Tuple  )       ->       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      UpperCAmelCase_       :
							snake_case__        =      42
							snake_case__        =      42
							snake_case__        =      0
							snake_case__        =      field(default_factory=__SCREAMING_SNAKE_CASE)
							snake_case__        =      field(default_factory=__SCREAMING_SNAKE_CASE)






							def __call__(		self :      List[Any]     ,						__UpperCamelCase :      Union[str, Any]  )       ->       List[str]:
												_UpperCamelCase      						=	Tracker(self.dest  )(_a  ).parametrized
												_UpperCamelCase      						=	Tracker(self.src  )(_a  ).parametrized

												_UpperCamelCase      						=	list(filter(lambda __UpperCamelCase  : type(_a  ) not in self.src_skip     ,						_a  )  )
												_UpperCamelCase      						=	list(filter(lambda __UpperCamelCase  : type(_a  ) not in self.dest_skip     ,						_a  )  )

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

												for dest_m, src_m in zip(_a     ,						_a  ):
																	dest_m.load_state_dict(src_m.state_dict()  )
																	if self.verbose == 1:
																						print(F'''Transfered from={src_m} to={dest_m}'''  )






def      lowercase		(						a__	:		str ,  a__	:		ResNetConfig ,  a__	:		Path ,  a__	:		bool = True     )						->   Tuple:
					print(F'''Converting {name}...'''     )
					with torch.no_grad():
										_UpperCamelCase      						=	timm.create_model(lowerCAmelCase__ ,  pretrained=lowerCAmelCase__     ).eval()
										_UpperCamelCase      						=	ResNetForImageClassification(lowerCAmelCase__     ).eval()
										_UpperCamelCase      						=	ModuleTransfer(src=lowerCAmelCase__ ,  dest=lowerCAmelCase__     )
										_UpperCamelCase      						=	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."

					_UpperCamelCase      						=	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
										_UpperCamelCase      						=	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      lowercase		(						a__	:		Path ,  a__	:		str = None ,  a__	:		bool = True     )						->   Optional[Any]:
					_UpperCamelCase      						=	'''imagenet-1k-id2label.json'''
					_UpperCamelCase      						=	1000
					_UpperCamelCase      						=	(1, num_labels)

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

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

					_UpperCamelCase      						=	partial(lowerCAmelCase__ ,  num_labels=lowerCAmelCase__ ,  idalabel=lowerCAmelCase__ ,  labelaid=lowerCAmelCase__     )

					_UpperCamelCase      						=	{
					    '''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(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__":
		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: 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.""",
		)

		UpperCAmelCase       =  parser.parse_args()
		UpperCAmelCase       =  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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			initializer_range
				__a           =			layer_norm_eps

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

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





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






