





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





@dataclass
class 				a			:
				SCREAMING_SNAKE_CASE__     :   nn.Module
				SCREAMING_SNAKE_CASE__     :   List[nn.Module]		      =     field(default_factory=__lowercase			)
				SCREAMING_SNAKE_CASE__     :   list		      =     field(default_factory=__lowercase			)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   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			, _lowerCAmelCase					):

										"""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 			snake_case_    (  self					):

										"""simple docstring"""




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





@dataclass
class 				a			:
				SCREAMING_SNAKE_CASE__     :   nn.Module
				SCREAMING_SNAKE_CASE__     :   nn.Module
				SCREAMING_SNAKE_CASE__     :   int		      =     0
				SCREAMING_SNAKE_CASE__     :   List		      =     field(default_factory=__lowercase			)
				SCREAMING_SNAKE_CASE__     :   List		      =     field(default_factory=__lowercase			)






				def __call__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   Tracker(self.dest					)(_lowerCAmelCase					).parametrized
										__SCREAMING_SNAKE_CASE:				Dict           =   Tracker(self.src					)(_lowerCAmelCase					).parametrized

										__SCREAMING_SNAKE_CASE:				Any           =   list(filter(lambda _lowerCAmelCase					: type(_lowerCAmelCase					) not in self.src_skip			, _lowerCAmelCase					)					)
										__SCREAMING_SNAKE_CASE:				str           =   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 		lowerCAmelCase  (	UpperCamelCase__   :   str       ,			UpperCamelCase__   :   ResNetConfig       ,			UpperCamelCase__   :   Path       ,			UpperCamelCase__   :   bool = True					)      ->    Any:

						"""simple docstring"""





						print(F"""Converting {name}..."""					)
						with torch.no_grad():
												__SCREAMING_SNAKE_CASE:				List[Any]           =   timm.create_model(UpperCamelCase__       ,			pretrained=UpperCamelCase__					).eval()
												__SCREAMING_SNAKE_CASE:				Any           =   ResNetForImageClassification(UpperCamelCase__					).eval()
												__SCREAMING_SNAKE_CASE:				str           =   ModuleTransfer(src=UpperCamelCase__       ,			dest=UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				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."

						__SCREAMING_SNAKE_CASE:				int           =   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
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   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 		lowerCAmelCase  (	UpperCamelCase__   :   Path       ,			UpperCamelCase__   :   str = None       ,			UpperCamelCase__   :   bool = True					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   '''imagenet-1k-id2label.json'''
						__SCREAMING_SNAKE_CASE:				str           =   1_000
						__SCREAMING_SNAKE_CASE:				Any           =   (1, num_labels)

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

						__SCREAMING_SNAKE_CASE:				int           =   idalabel
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   {v: k for k, v in idalabel.items()}

						__SCREAMING_SNAKE_CASE:				Optional[int]           =   partial(UpperCamelCase__       ,			num_labels=UpperCamelCase__       ,			idalabel=UpperCamelCase__       ,			labelaid=UpperCamelCase__					)

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   {
						    '''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(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__":
							lowerCAmelCase							:       Optional[int]					 =	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							:       Optional[int]					 =	parser.parse_args()
							lowerCAmelCase							:       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)










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
							from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase							:       Tuple					 =	logging.get_logger(__name__)

lowerCAmelCase							:       List[Any]					 =	{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     '''deberta-v2'''






				def __init__(  self			, _lowerCAmelCase=128100			, _lowerCAmelCase=1536			, _lowerCAmelCase=24			, _lowerCAmelCase=24			, _lowerCAmelCase=6144			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-7			, _lowerCAmelCase=False			, _lowerCAmelCase=-1			, _lowerCAmelCase=0			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, _lowerCAmelCase="gelu"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				str           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				List[Any]           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				Dict           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Dict           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   type_vocab_size
										__SCREAMING_SNAKE_CASE:				str           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Any           =   relative_attention
										__SCREAMING_SNAKE_CASE:				Tuple           =   max_relative_positions
										__SCREAMING_SNAKE_CASE:				List[str]           =   pad_token_id
										__SCREAMING_SNAKE_CASE:				List[str]           =   position_biased_input

										# Backwards compatibility
										if type(_lowerCAmelCase					) == str:
																__SCREAMING_SNAKE_CASE:				str           =   [x.strip() for x in pos_att_type.lower().split('''|'''					)]

										__SCREAMING_SNAKE_CASE:				int           =   pos_att_type
										__SCREAMING_SNAKE_CASE:				List[Any]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   kwargs.get('''pooler_hidden_size'''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   pooler_dropout
										__SCREAMING_SNAKE_CASE:				int           =   pooler_hidden_act





class 				a			(      __lowercase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''sequence'''}
										if self._config.type_vocab_size > 0:
																return OrderedDict(
																    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]					)
										else:
																return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]					)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 12






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = 3			, _lowerCAmelCase = 40			, _lowerCAmelCase = 40			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   super().generate_dummy_inputs(preprocessor=_lowerCAmelCase			, framework=_lowerCAmelCase					)
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
										return dummy_inputs



