


'''simple docstring'''


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_		:   Tuple							   =		logging.get_logger()





@dataclass
class     lowercase__  :





							'''simple docstring'''


							A_							:      nn.Module
							A_							:      List[nn.Module]      	=  field(default_factory=_snake_case					)
							A_							:      list      	=  field(default_factory=_snake_case					)






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      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    ,					__snake_case  ):
											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 UpperCAmelCase_	(    self  ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda __snake_case  : len(list(x.state_dict().keys()  )  ) > 0    ,					self.traced  )  )





@dataclass
class     lowercase__  :





							'''simple docstring'''


							A_							:      nn.Module
							A_							:      nn.Module
							A_							:      int      	=  1
							A_							:      List      	=  field(default_factory=_snake_case					)
							A_							:      List      	=  field(default_factory=_snake_case					)
							A_							:      bool      	=  True






							def __call__(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      Tracker(self.dest  )(__snake_case  ).parametrized
											_SCREAMING_SNAKE_CASE :       Dict     		=      Tracker(self.src  )(__snake_case  ).parametrized

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      list(filter(lambda __snake_case  : type(__snake_case  ) not in self.src_skip    ,					__snake_case  )  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      list(filter(lambda __snake_case  : type(__snake_case  ) not in self.dest_skip    ,					__snake_case  )  )

											if len(__snake_case  ) != len(__snake_case  ) and self.raise_if_mismatch:
															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}"""  )





class     lowercase__  (						nn.Module					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case  ):
											super().__init__()

											_SCREAMING_SNAKE_CASE :       List[Tuple[str, nn.Module]]     		=      []
											# - get the stem
											feature_blocks.append(("""conv1""", model.stem)  )
											# - get all the feature blocks
											for k, v in model.trunk_output.named_children():
															assert k.startswith("""block"""  ), f"""Unexpected layer name {k}"""
															_SCREAMING_SNAKE_CASE :       Dict     		=      len(__snake_case  ) + 1
															feature_blocks.append((f"""res{block_index}""", v)  )

											_SCREAMING_SNAKE_CASE :       List[str]     		=      nn.ModuleDict(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											return get_trunk_forward_outputs(
											    __snake_case    ,					out_feat_keys=__snake_case    ,					feature_blocks=self._feature_blocks    ,					)





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      x.split("""-"""  )
											return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]  )






							def __getitem__(    self    ,					__snake_case  ):
											# default to timm!
											if x not in self:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.convert_name_to_timm(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Tuple     		=      partial(lambda: (timm.create_model(__snake_case    ,					pretrained=__snake_case  ).eval(), None)  )

											else:
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      super().__getitem__(__snake_case  )

											return val





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							def __getitem__(    self    ,					__snake_case  ):
											if "seer" in x and "in1k" not in x:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      RegNetModel
											else:
															_SCREAMING_SNAKE_CASE :       Tuple     		=      RegNetForImageClassification
											return val







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				for from_key, to_key in keys:
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      from_state_dict[from_key].clone()
								print(f"""Copied key={from_key} to={to_key}"""     )
				return to_state_dict







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__ = True							,      ):







				"""simple docstring"""





				print(f"""Converting {name}..."""     )
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      from_model_func()
								_SCREAMING_SNAKE_CASE :       List[str]     		=      our_model_func(SCREAMING_SNAKE_CASE__     ).eval()
								_SCREAMING_SNAKE_CASE :       Dict     		=      ModuleTransfer(src=SCREAMING_SNAKE_CASE__							,      dest=SCREAMING_SNAKE_CASE__							,      raise_if_mismatch=SCREAMING_SNAKE_CASE__     )
								_SCREAMING_SNAKE_CASE :       Dict     		=      torch.randn((1, 3, 224, 224)     )
								module_transfer(SCREAMING_SNAKE_CASE__     )

				if from_state_dict is not None:
								_SCREAMING_SNAKE_CASE :       Tuple     		=      []
								# for seer - in1k finetuned we have to manually copy the head
								if "seer" in name and "in1k" in name:
												_SCREAMING_SNAKE_CASE :       int     		=      [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
								_SCREAMING_SNAKE_CASE :       int     		=      manually_copy_vissl_head(SCREAMING_SNAKE_CASE__							,      our_model.state_dict()							,      SCREAMING_SNAKE_CASE__     )
								our_model.load_state_dict(SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Dict     		=      our_model(SCREAMING_SNAKE_CASE__							,      output_hidden_states=SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      (
				    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ) else our_outputs.last_hidden_state
				)

				_SCREAMING_SNAKE_CASE :       int     		=      from_model(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      from_output[-1] if type(SCREAMING_SNAKE_CASE__     ) is list else from_output

				# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
				if "seer" in name and "in1k" in name:
								_SCREAMING_SNAKE_CASE :       List[str]     		=      our_outputs.hidden_states[-1]

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

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / name							,      commit_message="""Add model"""							,      use_temp_dir=SCREAMING_SNAKE_CASE__							,      )

								_SCREAMING_SNAKE_CASE :       int     		=      224 if """seer""" not in name else 384
								# we can use the convnext one
								_SCREAMING_SNAKE_CASE :       Tuple     		=      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""							,      size=SCREAMING_SNAKE_CASE__     )
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / name							,      commit_message="""Add image processor"""							,      use_temp_dir=SCREAMING_SNAKE_CASE__							,      )

								print(f"""Pushed {name}"""     )







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__ = None							,      SCREAMING_SNAKE_CASE__ = True     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """imagenet-1k-id2label.json"""
				_SCREAMING_SNAKE_CASE :       Tuple     		=      1000
				_SCREAMING_SNAKE_CASE :       str     		=      (1, num_labels)

				_SCREAMING_SNAKE_CASE :       Any     		=      """huggingface/label-files"""
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      num_labels
				_SCREAMING_SNAKE_CASE :       List[str]     		=      json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      repo_type="""dataset"""     )     )							,      """r"""     )     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {int(SCREAMING_SNAKE_CASE__     ): v for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE :       Tuple     		=      idalabel
				_SCREAMING_SNAKE_CASE :       Dict     		=      {v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE :       List[str]     		=      partial(SCREAMING_SNAKE_CASE__							,      num_labels=SCREAMING_SNAKE_CASE__							,      idalabel=SCREAMING_SNAKE_CASE__							,      labelaid=SCREAMING_SNAKE_CASE__     )

				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {
				    """regnet-x-002""": ImageNetPreTrainedConfig(
				        depths=[1, 1, 4, 7]							,      hidden_sizes=[24, 56, 152, 368]							,      groups_width=8							,      layer_type="""x"""     ),
				    """regnet-x-004""": ImageNetPreTrainedConfig(
				        depths=[1, 2, 7, 12]							,      hidden_sizes=[32, 64, 160, 384]							,      groups_width=16							,      layer_type="""x"""     ),
				    """regnet-x-006""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 5, 7]							,      hidden_sizes=[48, 96, 240, 528]							,      groups_width=24							,      layer_type="""x"""     ),
				    """regnet-x-008""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 5]							,      hidden_sizes=[64, 128, 288, 672]							,      groups_width=16							,      layer_type="""x"""     ),
				    """regnet-x-016""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 2]							,      hidden_sizes=[72, 168, 408, 912]							,      groups_width=24							,      layer_type="""x"""     ),
				    """regnet-x-032""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 15, 2]							,      hidden_sizes=[96, 192, 432, 1008]							,      groups_width=48							,      layer_type="""x"""     ),
				    """regnet-x-040""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 14, 2]							,      hidden_sizes=[80, 240, 560, 1360]							,      groups_width=40							,      layer_type="""x"""     ),
				    """regnet-x-064""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]							,      hidden_sizes=[168, 392, 784, 1624]							,      groups_width=56							,      layer_type="""x"""     ),
				    """regnet-x-080""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 15, 1]							,      hidden_sizes=[80, 240, 720, 1920]							,      groups_width=120							,      layer_type="""x"""     ),
				    """regnet-x-120""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]							,      hidden_sizes=[224, 448, 896, 2240]							,      groups_width=112							,      layer_type="""x"""     ),
				    """regnet-x-160""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 13, 1]							,      hidden_sizes=[256, 512, 896, 2048]							,      groups_width=128							,      layer_type="""x"""     ),
				    """regnet-x-320""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 13, 1]							,      hidden_sizes=[336, 672, 1344, 2520]							,      groups_width=168							,      layer_type="""x"""     ),
				    # y variant
				    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]							,      hidden_sizes=[24, 56, 152, 368]							,      groups_width=8     ),
				    """regnet-y-004""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 6, 6]							,      hidden_sizes=[48, 104, 208, 440]							,      groups_width=8     ),
				    """regnet-y-006""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 4]							,      hidden_sizes=[48, 112, 256, 608]							,      groups_width=16     ),
				    """regnet-y-008""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 8, 2]							,      hidden_sizes=[64, 128, 320, 768]							,      groups_width=16     ),
				    """regnet-y-016""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 17, 2]							,      hidden_sizes=[48, 120, 336, 888]							,      groups_width=24     ),
				    """regnet-y-032""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 13, 1]							,      hidden_sizes=[72, 216, 576, 1512]							,      groups_width=24     ),
				    """regnet-y-040""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 12, 2]							,      hidden_sizes=[128, 192, 512, 1088]							,      groups_width=64     ),
				    """regnet-y-064""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 14, 2]							,      hidden_sizes=[144, 288, 576, 1296]							,      groups_width=72     ),
				    """regnet-y-080""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]							,      hidden_sizes=[168, 448, 896, 2016]							,      groups_width=56     ),
				    """regnet-y-120""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]							,      hidden_sizes=[224, 448, 896, 2240]							,      groups_width=112     ),
				    """regnet-y-160""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 11, 1]							,      hidden_sizes=[224, 448, 1232, 3024]							,      groups_width=112     ),
				    """regnet-y-320""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]							,      hidden_sizes=[232, 696, 1392, 3712]							,      groups_width=232     ),
				    # models created by SEER -> https://arxiv.org/abs/2202.08360
				    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]							,      hidden_sizes=[232, 696, 1392, 3712]							,      groups_width=232     ),
				    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]							,      hidden_sizes=[328, 984, 1968, 4920]							,      groups_width=328     ),
				    """regnet-y-1280-seer""": RegNetConfig(
				        depths=[2, 7, 17, 1]							,      hidden_sizes=[528, 1056, 2904, 7392]							,      groups_width=264     ),
				    """regnet-y-2560-seer""": RegNetConfig(
				        depths=[3, 7, 16, 1]							,      hidden_sizes=[640, 1696, 2544, 5088]							,      groups_width=640     ),
				    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]							,      hidden_sizes=[2020, 4040, 1_1110, 2_8280]							,      groups_width=1010     ),
				    # finetuned on imagenet
				    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]							,      hidden_sizes=[232, 696, 1392, 3712]							,      groups_width=232     ),
				    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]							,      hidden_sizes=[328, 984, 1968, 4920]							,      groups_width=328     ),
				    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]							,      hidden_sizes=[528, 1056, 2904, 7392]							,      groups_width=264     ),
				    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[3, 7, 16, 1]							,      hidden_sizes=[640, 1696, 2544, 5088]							,      groups_width=640     ),
				    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]							,      hidden_sizes=[2020, 4040, 1_1110, 2_8280]							,      groups_width=1010     ),
				}

				_SCREAMING_SNAKE_CASE :       Dict     		=      NameToOurModelFuncMap()
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      NameToFromModelFuncMap()
				# add seer weights logic

				def load_using_classy_vision(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ) -> Tuple[nn.Module, Dict]:
								_SCREAMING_SNAKE_CASE :       int     		=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__							,      model_dir=str(SCREAMING_SNAKE_CASE__     )							,      map_location="""cpu"""     )
								_SCREAMING_SNAKE_CASE :       List[str]     		=      model_func()
								# check if we have a head, if yes add it
								_SCREAMING_SNAKE_CASE :       Any     		=      files["""classy_state_dict"""]["""base_model"""]["""model"""]
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      model_state_dict["""trunk"""]
								model.load_state_dict(SCREAMING_SNAKE_CASE__     )
								return model.eval(), model_state_dict["heads"]

				# pretrained
				_SCREAMING_SNAKE_CASE :       Tuple     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""							,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,      )

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""							,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,      )

				_SCREAMING_SNAKE_CASE :       List[str]     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""							,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )							,      )

				_SCREAMING_SNAKE_CASE :       str     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""							,      lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27							,      group_width=1010							,      w_a=1744							,      w_a=6_2_0.8_3							,      w_m=2.5_2     )     )     )							,      )

				# IN1K finetuned
				_SCREAMING_SNAKE_CASE :       Dict     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""							,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,      )

				_SCREAMING_SNAKE_CASE :       int     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""							,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,      )

				_SCREAMING_SNAKE_CASE :       str     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""							,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )							,      )

				_SCREAMING_SNAKE_CASE :       List[str]     		=      partial(
				    SCREAMING_SNAKE_CASE__							,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""							,      lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27							,      group_width=1010							,      w_a=1744							,      w_a=6_2_0.8_3							,      w_m=2.5_2     )     )     )							,      )

				if model_name:
								convert_weight_and_push(
								    SCREAMING_SNAKE_CASE__							,      names_to_from_model_map[model_name]							,      names_to_ours_model_map[model_name]							,      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__							,      names_to_from_model_map[model_name]							,      names_to_ours_model_map[model_name]							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      )
				return config, expected_shape


if __name__ == "__main__":
				UpperCAmelCase_		:   List[Any]							   =		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 regnet* architecture,'
				        ' currently: regnetx-*, regnety-*. 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_		:   List[Any]							   =		parser.parse_args()

				UpperCAmelCase_		:   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
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
				import torch

				from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=None    ,					__snake_case=None    ,					__snake_case=None    ,					__snake_case="resnet50"    ,					__snake_case=3    ,					__snake_case=32    ,					__snake_case=3    ,					__snake_case=True    ,					__snake_case=True    ,					):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      parent
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      out_indices if out_indices is not None else [4]
											_SCREAMING_SNAKE_CASE :       str     		=      stage_names
											_SCREAMING_SNAKE_CASE :       List[str]     		=      out_features
											_SCREAMING_SNAKE_CASE :       int     		=      backbone
											_SCREAMING_SNAKE_CASE :       Any     		=      batch_size
											_SCREAMING_SNAKE_CASE :       List[str]     		=      image_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_channels
											_SCREAMING_SNAKE_CASE :       int     		=      use_pretrained_backbone
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      is_training






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.get_config()

											return config, pixel_values






							def UpperCAmelCase_	(    self  ):
											return TimmBackboneConfig(
											    image_size=self.image_size    ,					num_channels=self.num_channels    ,					out_features=self.out_features    ,					out_indices=self.out_indices    ,					stage_names=self.stage_names    ,					use_pretrained_backbone=self.use_pretrained_backbone    ,					backbone=self.backbone    ,					)






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      TimmBackbone(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(__snake_case  )
											self.parent.assertEqual(
											    result.feature_map[-1].shape    ,					(self.batch_size, model.channels[-1], 14, 14)    ,					)






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Any     		=      config_and_inputs
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {"""pixel_values""": pixel_values}
											return config, inputs_dict





@require_torch
@require_timm
class     lowercase__  (						_snake_case     ,     _snake_case     ,     _snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Optional[Any]      	=  (TimmBackbone,) if is_torch_available() else ()
							A_							:      Tuple      	=  {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							A_							:      Optional[Any]      	=  False
							A_							:      List[Any]      	=  False
							A_							:      Dict      	=  False
							A_							:      int      	=  False






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      TimmBackboneModelTester(self  )
											_SCREAMING_SNAKE_CASE :       int     		=      ConfigTester(self    ,					config_class=__snake_case    ,					has_text_modality=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """resnet18"""
											_SCREAMING_SNAKE_CASE :       Tuple     		=      """microsoft/resnet-18"""

											_SCREAMING_SNAKE_CASE :       List[str]     		=      AutoBackbone.from_pretrained(__snake_case    ,					use_timm_backbone=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      AutoBackbone.from_pretrained(__snake_case  )

											self.assertEqual(len(timm_model.out_features  )    ,					len(transformers_model.out_features  )  )
											self.assertEqual(len(timm_model.stage_names  )    ,					len(transformers_model.stage_names  )  )
											self.assertEqual(timm_model.channels    ,					transformers_model.channels  )
											# Out indices are set to the last layer by default. For timm models, we don't know
											# the number of layers in advance, so we set it to (-1,), whereas for transformers
											# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
											self.assertEqual(timm_model.out_indices    ,					(-1,)  )
											self.assertEqual(transformers_model.out_indices    ,					[len(timm_model.stage_names  ) - 1]  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      AutoBackbone.from_pretrained(__snake_case    ,					use_timm_backbone=__snake_case    ,					out_indices=[1, 2, 3]  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      AutoBackbone.from_pretrained(__snake_case    ,					out_indices=[1, 2, 3]  )

											self.assertEqual(timm_model.out_indices    ,					transformers_model.out_indices  )
											self.assertEqual(len(timm_model.out_features  )    ,					len(transformers_model.out_features  )  )
											self.assertEqual(timm_model.channels    ,					transformers_model.channels  )






							@unittest.skip("""TimmBackbone doesn't support feed forward chunking"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""TimmBackbone initialization is managed on the timm side"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""model weights aren't tied in TimmBackbone."""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""model weights aren't tied in TimmBackbone."""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""TimmBackbone doesn't support output_attentions."""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""Safetensors is not supported by timm."""  )
							def UpperCAmelCase_	(    self  ):
											pass






							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def UpperCAmelCase_	(    self  ):
											pass






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      model_class(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Tuple     		=      inspect.signature(model.forward  )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE :       int     		=      [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE :       List[Any]     		=      ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE :       Tuple     		=      True
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.has_attentions

											# no need to test all models as different heads yield the same functionality
											_SCREAMING_SNAKE_CASE :       str     		=      self.all_model_classes[0]
											_SCREAMING_SNAKE_CASE :       str     		=      model_class(__snake_case  )
											model.to(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Tuple     		=      self._prepare_for_class(__snake_case    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(**__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      outputs[0][-1]

											# Encoder-/Decoder-only models
											_SCREAMING_SNAKE_CASE :       str     		=      outputs.hidden_states[0]
											hidden_states.retain_grad()

											if self.has_attentions:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      outputs.attentions[0]
															attentions.retain_grad()

											output.flatten()[0].backward(retain_graph=__snake_case  )

											self.assertIsNotNone(hidden_states.grad  )

											if self.has_attentions:
															self.assertIsNotNone(attentions.grad  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE :       str     		=      model_class(__snake_case  )
															model.to(__snake_case  )
															model.eval()
															_SCREAMING_SNAKE_CASE :       List[str]     		=      model(**__snake_case  )

															self.assertEqual(len(result.feature_maps  )    ,					len(config.out_indices  )  )
															self.assertEqual(len(model.channels  )    ,					len(config.out_indices  )  )

															# Check output of last stage is taken if out_features=None, out_indices=None
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      copy.deepcopy(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      None
															_SCREAMING_SNAKE_CASE :       Tuple     		=      model_class(__snake_case  )
															model.to(__snake_case  )
															model.eval()
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model(**__snake_case  )

															self.assertEqual(len(result.feature_maps  )    ,					1  )
															self.assertEqual(len(model.channels  )    ,					1  )

															# Check backbone can be initialized with fresh weights
															_SCREAMING_SNAKE_CASE :       str     		=      copy.deepcopy(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Tuple     		=      False
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model_class(__snake_case  )
															model.to(__snake_case  )
															model.eval()
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(**__snake_case  )




