
'''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   UpperCAmelCase_					:






						'''simple docstring'''






						_lowercase		:				List[Any]									=       4_2
						_lowercase		:				str									=       field(default_factory=lowerCamelCase__			)
						_lowercase		:				int									=       field(default_factory=lowerCamelCase__			)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



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




						def __call__( self   ,       _lowercase				):

												"""simple docstring"""



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




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



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






@dataclass
class   UpperCAmelCase_					:






						'''simple docstring'''






						_lowercase		:				Optional[int]									=       4_2
						_lowercase		:				Optional[int]									=       4_2
						_lowercase		:				Optional[Any]									=       0
						_lowercase		:				Union[str, Any]									=       field(default_factory=lowerCamelCase__			)
						_lowercase		:				List[str]									=       field(default_factory=lowerCamelCase__			)




						def __call__( self   ,       _lowercase				):

												"""simple docstring"""



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

												_lowerCAmelCase  						=			list(filter(lambda _lowercase				: type(__snake_case				) not in self.src_skip   ,       __snake_case				)				)
												_lowerCAmelCase  						=			list(filter(lambda _lowercase				: type(__snake_case				) not in self.dest_skip   ,       __snake_case				)				)

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

												for dest_m, src_m in zip(__snake_case   ,       __snake_case				):
																		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				:Dict			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:List[str] = True		):
						print(f'Converting {name}...'		)
						with torch.no_grad():
												_lowerCAmelCase  						=			timm.create_model(_A			,     pretrained=_A		).eval()
												_lowerCAmelCase  						=			ResNetForImageClassification(_A		).eval()
												_lowerCAmelCase  						=			ModuleTransfer(src=_A			,     dest=_A		)
												_lowerCAmelCase  						=			torch.randn((1, 3, 224, 224)		)
												module_transfer(_A		)

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

						_lowerCAmelCase  						=			f'resnet{"-".join(name.split("resnet"		)		)}'
						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
												_lowerCAmelCase  						=			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 A     (__lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Tuple = None			,     __lowerCamelCase				:Tuple = True		):
						_lowerCAmelCase  						=			'imagenet-1k-id2label.json'
						_lowerCAmelCase  						=			1000
						_lowerCAmelCase  						=			(1, num_labels)

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

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

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

						_lowerCAmelCase  						=			{
						    '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(_A			,     names_to_config[model_name]			,     _A			,     _A		)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(_A			,     _A			,     _A			,     _A		)
						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    =		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TableTransformerForObjectDetection""",
						    """TableTransformerModel""",
						    """TableTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_table_transformer import (
						    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TableTransformerConfig,
						    TableTransformerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_table_transformer import (
												    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TableTransformerForObjectDetection,
												    TableTransformerModel,
												    TableTransformerPreTrainedModel,
												)

else:
						import sys

						_lowercase    =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
