


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







@dataclass
class        snake_case_   :
					UpperCAmelCase__					:    nn.Module
					UpperCAmelCase__					:    List[nn.Module]          =			field(default_factory=lowerCamelCase_      )
					UpperCAmelCase__					:    list          =			field(default_factory=lowerCamelCase_      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :int			,__snake_case   :Tensor			,__snake_case   :Tensor      )					->    Union[str, Any]:
												a__             =     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   :Union[str, Any]			,__snake_case   :Tensor      )					->    int:
												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  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												# 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        snake_case_   :
					UpperCAmelCase__					:    nn.Module
					UpperCAmelCase__					:    nn.Module
					UpperCAmelCase__					:    int          =			1
					UpperCAmelCase__					:    List          =			field(default_factory=lowerCamelCase_      )
					UpperCAmelCase__					:    List          =			field(default_factory=lowerCamelCase_      )
					UpperCAmelCase__					:    bool          =			True
					def __call__(      self   :List[Any]			,__snake_case   :Tensor      )					->    int:
												a__             =     Tracker(self.dest      )(__snake_case      ).parametrized
												a__             =     Tracker(self.src      )(__snake_case      ).parametrized

												a__             =     list(filter(lambda __snake_case      : type(__snake_case      ) not in self.src_skip			,__snake_case      )      )
												a__             =     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        snake_case_   (nn.Module      ):
					def __init__(      self   :int			,__snake_case   :nn.Module      )					->    Optional[Any]:
												super().__init__()

												a__             =     []
												# - 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}'
																			a__             =     len(__snake_case      ) + 1
																			feature_blocks.append((F'res{block_index}', v)      )

												a__             =     nn.ModuleDict(__snake_case      )



					def  lowerCamelCase__(      self   :str			,__snake_case   :Tensor      )					->    Tuple:
												return get_trunk_forward_outputs(
												    __snake_case			,out_feat_keys=__snake_case			,feature_blocks=self._feature_blocks			,)







class        snake_case_   (lowerCamelCase_      ):
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :str      )					->    str:
												a__             =     x.split('-'      )
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )



					def __getitem__(      self   :int			,__snake_case   :str      )					->    Callable[[], Tuple[nn.Module, Dict]]:
												# default to timm!
												if x not in self:
																			a__             =     self.convert_name_to_timm(__snake_case      )
																			a__             =     partial(lambda: (timm.create_model(__snake_case			,pretrained=__snake_case      ).eval(), None)      )

												else:
																			a__             =     super().__getitem__(__snake_case      )

												return val







class        snake_case_   (lowerCamelCase_      ):
					def __getitem__(      self   :Union[str, Any]			,__snake_case   :str      )					->    Callable[[], nn.Module]:
												if "seer" in x and "in1k" not in x:
																			a__             =     RegNetModel
												else:
																			a__             =     RegNetForImageClassification
												return val






def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				Tuple					,    __lowerCAmelCase							:				List[Tuple[str, str]]   ):
							for from_key, to_key in keys:
														a__             =     from_state_dict[from_key].clone()
														print(F'Copied key={from_key} to={to_key}'   )
							return to_state_dict






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Callable[[], nn.Module]					,    __lowerCAmelCase							:				Callable[[], nn.Module]					,    __lowerCAmelCase							:				RegNetConfig					,    __lowerCAmelCase							:				Path					,    __lowerCAmelCase							:				bool = True					,    ):
							print(F'Converting {name}...'   )
							with torch.no_grad():
														a__      ,					a__             =     from_model_func()
														a__             =     our_model_func(__lowerCAmelCase   ).eval()
														a__             =     ModuleTransfer(src=__lowerCAmelCase					,    dest=__lowerCAmelCase					,    raise_if_mismatch=__lowerCAmelCase   )
														a__             =     torch.randn((1, 3, 2_2_4, 2_2_4)   )
														module_transfer(__lowerCAmelCase   )

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

							a__             =     our_model(__lowerCAmelCase					,    output_hidden_states=__lowerCAmelCase   )
							a__             =     (
							    our_outputs.logits if isinstance(__lowerCAmelCase					,    __lowerCAmelCase   ) else our_outputs.last_hidden_state
							)

							a__             =     from_model(__lowerCAmelCase   )
							a__             =     from_output[-1] if type(__lowerCAmelCase   ) 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:
														a__             =     our_outputs.hidden_states[-1]

							assert torch.allclose(__lowerCAmelCase					,    __lowerCAmelCase   ), "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=__lowerCAmelCase					,    )

														a__             =     2_2_4 if 'seer' not in name else 3_8_4
														# we can use the convnext one
														a__             =     AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					,    size=__lowerCAmelCase   )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name					,    commit_message='Add image processor'					,    use_temp_dir=__lowerCAmelCase					,    )

														print(F'Pushed {name}'   )






def 							__lowercase   ( __lowerCAmelCase							:				Path					,    __lowerCAmelCase							:				str = None					,    __lowerCAmelCase							:				bool = True   ):
							a__             =     'imagenet-1k-id2label.json'
							a__             =     1_0_0_0
							a__             =     (1, num_labels)

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

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

							a__             =     partial(__lowerCAmelCase					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid=__lowerCAmelCase   )

							a__             =     {
							    'regnet-x-002': ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]					,    hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]					,    groups_width=8					,    layer_type='x'   ),
							    'regnet-x-004': ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 1_2]					,    hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]					,    groups_width=1_6					,    layer_type='x'   ),
							    'regnet-x-006': ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]					,    hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]					,    groups_width=2_4					,    layer_type='x'   ),
							    'regnet-x-008': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]					,    hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]					,    groups_width=1_6					,    layer_type='x'   ),
							    'regnet-x-016': ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 2]					,    hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]					,    groups_width=2_4					,    layer_type='x'   ),
							    'regnet-x-032': ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_5, 2]					,    hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]					,    groups_width=4_8					,    layer_type='x'   ),
							    'regnet-x-040': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_4, 2]					,    hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]					,    groups_width=4_0					,    layer_type='x'   ),
							    'regnet-x-064': ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 1]					,    hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]					,    groups_width=5_6					,    layer_type='x'   ),
							    'regnet-x-080': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_5, 1]					,    hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]					,    groups_width=1_2_0					,    layer_type='x'   ),
							    'regnet-x-120': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_1, 1]					,    hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]					,    groups_width=1_1_2					,    layer_type='x'   ),
							    'regnet-x-160': ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_3, 1]					,    hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]					,    groups_width=1_2_8					,    layer_type='x'   ),
							    'regnet-x-320': ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_3, 1]					,    hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]					,    groups_width=1_6_8					,    layer_type='x'   ),
							    # y variant
							    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]					,    hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]					,    groups_width=8   ),
							    'regnet-y-004': ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]					,    hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]					,    groups_width=8   ),
							    'regnet-y-006': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]					,    hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]					,    groups_width=1_6   ),
							    'regnet-y-008': ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]					,    hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]					,    groups_width=1_6   ),
							    'regnet-y-016': ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_7, 2]					,    hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]					,    groups_width=2_4   ),
							    'regnet-y-032': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_3, 1]					,    hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]					,    groups_width=2_4   ),
							    'regnet-y-040': ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_2, 2]					,    hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]					,    groups_width=6_4   ),
							    'regnet-y-064': ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_4, 2]					,    hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]					,    groups_width=7_2   ),
							    'regnet-y-080': ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 1]					,    hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]					,    groups_width=5_6   ),
							    'regnet-y-120': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_1, 1]					,    hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]					,    groups_width=1_1_2   ),
							    'regnet-y-160': ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_1, 1]					,    hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]					,    groups_width=1_1_2   ),
							    'regnet-y-320': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]					,    hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]					,    groups_width=2_3_2   ),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 1_2, 1]					,    hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]					,    groups_width=2_3_2   ),
							    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 1_2, 1]					,    hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]					,    groups_width=3_2_8   ),
							    'regnet-y-1280-seer': RegNetConfig(
							        depths=[2, 7, 1_7, 1]					,    hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]					,    groups_width=2_6_4   ),
							    'regnet-y-2560-seer': RegNetConfig(
							        depths=[3, 7, 1_6, 1]					,    hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]					,    groups_width=6_4_0   ),
							    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]					,    hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]					,    groups_width=1_0_1_0   ),
							    # finetuned on imagenet
							    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]					,    hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]					,    groups_width=2_3_2   ),
							    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]					,    hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]					,    groups_width=3_2_8   ),
							    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]					,    hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]					,    groups_width=2_6_4   ),
							    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[3, 7, 1_6, 1]					,    hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]					,    groups_width=6_4_0   ),
							    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]					,    hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]					,    groups_width=1_0_1_0   ),
							}

							a__             =     NameToOurModelFuncMap()
							a__             =     NameToFromModelFuncMap()
							# add seer weights logic

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

							# pretrained
							a__             =     partial(
							    __lowerCAmelCase					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,    )

							a__             =     partial(
							    __lowerCAmelCase					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,    )

							a__             =     partial(
							    __lowerCAmelCase					,    '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()   )					,    )

							a__             =     partial(
							    __lowerCAmelCase					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'					,    lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=2_7					,    group_width=1_0_1_0					,    w_a=1_7_4_4					,    w_a=620.83					,    w_m=2.52   )   )   )					,    )

							# IN1K finetuned
							a__             =     partial(
							    __lowerCAmelCase					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,    )

							a__             =     partial(
							    __lowerCAmelCase					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )					,    )

							a__             =     partial(
							    __lowerCAmelCase					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'					,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )					,    )

							a__             =     partial(
							    __lowerCAmelCase					,    'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'					,    lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=2_7					,    group_width=1_0_1_0					,    w_a=1_7_4_4					,    w_a=620.83					,    w_m=2.52   )   )   )					,    )

							if model_name:
														convert_weight_and_push(
														    __lowerCAmelCase					,    names_to_from_model_map[model_name]					,    names_to_ours_model_map[model_name]					,    names_to_config[model_name]					,    __lowerCAmelCase					,    __lowerCAmelCase					,    )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    __lowerCAmelCase					,    names_to_from_model_map[model_name]					,    names_to_ours_model_map[model_name]					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    )
							return config, expected_shape


if __name__ == "__main__":
						snake_case							:       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.''',
						)

						snake_case							:       Tuple     			=      parser.parse_args()

						snake_case							:       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 Mapping

from packaging import version

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


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

snake_case							:       str     			=      {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			'''mobilenet_v2'''
					def __init__(      self   :Optional[Any]			,__snake_case   :List[Any]=3			,__snake_case   :Any=2_24			,__snake_case   :Dict=1.0			,__snake_case   :str=8			,__snake_case   :Union[str, Any]=8			,__snake_case   :Optional[Any]=6			,__snake_case   :str=32			,__snake_case   :Tuple=True			,__snake_case   :Union[str, Any]=True			,__snake_case   :Any="relu6"			,__snake_case   :List[str]=True			,__snake_case   :Dict=0.8			,__snake_case   :Optional[int]=0.02			,__snake_case   :Tuple=0.0_01			,__snake_case   :Dict=2_55			,**__snake_case   :Dict			,)					->    Any:
												super().__init__(**__snake_case      )

												if depth_multiplier <= 0:
																			raise ValueError('depth_multiplier must be greater than zero.'      )

												a__             =     num_channels
												a__             =     image_size
												a__             =     depth_multiplier
												a__             =     depth_divisible_by
												a__             =     min_depth
												a__             =     expand_ratio
												a__             =     output_stride
												a__             =     first_layer_is_expansion
												a__             =     finegrained_output
												a__             =     hidden_act
												a__             =     tf_padding
												a__             =     classifier_dropout_prob
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     semantic_loss_ignore_index







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Tuple          =			version.parse('''1.11'''      )
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Mapping[str, Mapping[int, str]]:
												return OrderedDict([('pixel_values', {0: 'batch'})]      )
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																			return OrderedDict([('logits', {0: 'batch'})]      )
												else:
																			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )



					@property
					def  lowerCamelCase__(      self   :Any      )					->    float:
												return 1E-4




