





'''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


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

snake_case_  :							Optional[Any]            =	{
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class        __a   (lowerCamelCase  ):
							__a       : Optional[Any]          =							"mobilevit"

							def __init__(				self				:  Union[str, Any]							,  __magic_name__				:  Any=3							,  __magic_name__				:  List[Any]=2_56							,  __magic_name__				:  Optional[int]=2							,  __magic_name__				:  Union[str, Any]=[1_44, 1_92, 2_40]							,  __magic_name__				:  Dict=[16, 32, 64, 96, 1_28, 1_60, 6_40]							,  __magic_name__				:  Any=4							,  __magic_name__				:  Any=2.0							,  __magic_name__				:  Dict=4.0							,  __magic_name__				:  List[str]="silu"							,  __magic_name__				:  Any=3							,  __magic_name__				:  Union[str, Any]=32							,  __magic_name__				:  int=0.1							,  __magic_name__				:  Optional[int]=0.0							,  __magic_name__				:  str=0.1							,  __magic_name__				:  int=0.0_2							,  __magic_name__				:  List[str]=1E-5							,  __magic_name__				:  str=True							,  __magic_name__				:  Tuple=2_56							,  __magic_name__				:  Union[str, Any]=[6, 12, 18]							,  __magic_name__				:  Optional[Any]=0.1							,  __magic_name__				:  Dict=2_55							,  **__magic_name__				:  Any							,  )       ->							Optional[int]:







													"""simple docstring"""
													super().__init__(**__magic_name__ )

													UpperCAmelCase_      :   Optional[int]      =	num_channels
													UpperCAmelCase_      :   Optional[Any]      =	image_size
													UpperCAmelCase_      :   Tuple      =	patch_size
													UpperCAmelCase_      :   Optional[int]      =	hidden_sizes
													UpperCAmelCase_      :   List[Any]      =	neck_hidden_sizes
													UpperCAmelCase_      :   Optional[Any]      =	num_attention_heads
													UpperCAmelCase_      :   Union[str, Any]      =	mlp_ratio
													UpperCAmelCase_      :   List[str]      =	expand_ratio
													UpperCAmelCase_      :   Union[str, Any]      =	hidden_act
													UpperCAmelCase_      :   Any      =	conv_kernel_size
													UpperCAmelCase_      :   List[str]      =	output_stride
													UpperCAmelCase_      :   Union[str, Any]      =	hidden_dropout_prob
													UpperCAmelCase_      :   Union[str, Any]      =	attention_probs_dropout_prob
													UpperCAmelCase_      :   Any      =	classifier_dropout_prob
													UpperCAmelCase_      :   Optional[int]      =	initializer_range
													UpperCAmelCase_      :   int      =	layer_norm_eps
													UpperCAmelCase_      :   List[Any]      =	qkv_bias

													# decode head attributes for semantic segmentation
													UpperCAmelCase_      :   List[str]      =	aspp_out_channels
													UpperCAmelCase_      :   Optional[Any]      =	atrous_rates
													UpperCAmelCase_      :   Tuple      =	aspp_dropout_prob
													UpperCAmelCase_      :   Optional[Any]      =	semantic_loss_ignore_index





class        __a   (lowerCamelCase  ):
							__a       : Union[str, Any]          =							version.parse("1.11"  )

							@property
							def      UpperCAmelCase__						(				self				:  Any )       ->							Mapping[str, Mapping[int, str]]:







													"""simple docstring"""
													return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})] )

							@property
							def      UpperCAmelCase__						(				self				:  int )       ->							Mapping[str, Mapping[int, str]]:







													"""simple docstring"""
													if self.task == "image-classification":
																			return OrderedDict([('''logits''', {0: '''batch'''})] )
													else:
																			return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )



							@property
							def      UpperCAmelCase__						(				self				:  str )       ->							float:







													"""simple docstring"""
													return 1E-4










'''simple docstring'''




import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
a  :     Any       =       logging.get_logger(__name__)

@dataclass
class      UpperCamelCase_					:
	lowercase		=					42
	lowercase		=					field(default_factory=__magic_name__     )
	lowercase		=					field(default_factory=__magic_name__     )





	def 					_lowercase(    self						,						A						,						A						,						A       )     ->     Optional[Any]:
								UpperCAmelCase	:						Tuple					=						len(list(m.modules()       )       ) == 1 or isinstance(A						,						nn.Convad       ) or isinstance(A						,						nn.BatchNormad       )
								if has_not_submodules:
															if not isinstance(A						,						A       ):
																						self.traced.append(A       )





	def __call__(    self						,						A       )     ->     Dict:
								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 					_lowercase(    self       )     ->     Dict:
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda A       : len(list(x.state_dict().keys()       )       ) > 0						,						self.traced       )       )




@dataclass
class      UpperCamelCase_					:
	lowercase		=					42
	lowercase		=					42
	lowercase		=					0
	lowercase		=					field(default_factory=__magic_name__     )
	lowercase		=					field(default_factory=__magic_name__     )





	def __call__(    self						,						A       )     ->     str:
								UpperCAmelCase	:						Dict					=						Tracker(self.dest       )(A       ).parametrized
								UpperCAmelCase	:						Union[str, Any]					=						Tracker(self.src       )(A       ).parametrized

								UpperCAmelCase	:						Optional[int]					=						list(filter(lambda A       : type(A       ) not in self.src_skip						,						A       )       )
								UpperCAmelCase	:						Optional[int]					=						list(filter(lambda A       : 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  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     nn.Module:
							# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
							UpperCAmelCase	:						Optional[Any]					=						from_model.state_dict()
							UpperCAmelCase	:						int					=						our_model.state_dict()
							UpperCAmelCase	:						Optional[int]					=						our_model.config
							UpperCAmelCase	:						str					=						[]
							for stage_idx in range(len(config.hidden_sizes						)						):
														for block_id in range(config.depths[stage_idx]						):
																					UpperCAmelCase	:						List[Any]					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																					UpperCAmelCase	:						Any					=						F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

																					all_keys.append((from_key, to_key)						)
																					UpperCAmelCase	:						Optional[int]					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																					UpperCAmelCase	:						str					=						F'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

																					all_keys.append((from_key, to_key)						)

							for from_key, to_key in all_keys:
														UpperCAmelCase	:						List[Any]					=						from_state_dict.pop(_lowercase						)

							our_model.load_state_dict(_lowercase						)
							return our_model



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase = True      ,							) ->     List[Any]:
							print(F'''Downloading weights for {name}...'''						)
							UpperCAmelCase	:						List[str]					=						cached_download(_lowercase						)
							print(F'''Converting {name}...'''						)
							UpperCAmelCase	:						int					=						torch.load(_lowercase						)["""state_dict"""]
							from_model.load_state_dict(_lowercase						)
							from_model.eval()
							with torch.no_grad():
														UpperCAmelCase	:						Tuple					=						VanForImageClassification(_lowercase						).eval()
														UpperCAmelCase	:						int					=						ModuleTransfer(src=_lowercase      ,							dest=_lowercase						)
														UpperCAmelCase	:						Any					=						torch.randn((1, 3, 2_2_4, 2_2_4)						)
														module_transfer(_lowercase						)
														UpperCAmelCase	:						Tuple					=						copy_parameters(_lowercase      ,							_lowercase						)

							if not torch.allclose(from_model(_lowercase						)      ,							our_model(_lowercase						).logits						):
														raise ValueError("""The model logits don't match the original one."""						)

							UpperCAmelCase	:						List[str]					=						name
							print(_lowercase						)

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

														# we can use the convnext one
														UpperCAmelCase	:						Dict					=						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=_lowercase      ,							)

														print(F'''Pushed {checkpoint_name}'''						)



def  __lowerCamelCase    (   _lowercase      ,							_lowercase = None      ,							_lowercase = True						) ->     List[Any]:
							UpperCAmelCase	:						int					=						"""imagenet-1k-id2label.json"""
							UpperCAmelCase	:						List[Any]					=						1_0_0_0

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

							UpperCAmelCase	:						Any					=						idalabel
							UpperCAmelCase	:						List[str]					=						{v: k for k, v in idalabel.items()}

							UpperCAmelCase	:						Any					=						partial(_lowercase      ,							num_labels=_lowercase      ,							idalabel=_lowercase      ,							labelaid=_lowercase						)

							UpperCAmelCase	:						Optional[Any]					=						{
							    """van-tiny""": ImageNetPreTrainedConfig(
							        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]      ,							depths=[3, 3, 5, 2]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-small""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[2, 2, 4, 2]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-base""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[3, 3, 1_2, 3]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-large""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[3, 5, 2_7, 3]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							}

							UpperCAmelCase	:						Optional[Any]					=						{
							    """van-tiny""": van_tiny,
							    """van-small""": van_small,
							    """van-base""": van_base,
							    """van-large""": van_large,
							}

							UpperCAmelCase	:						Optional[int]					=						{
							    """van-tiny""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
							    ),
							    """van-small""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
							    ),
							    """van-base""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
							    ),
							    """van-large""": (
							        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
							    ),
							}

							if model_name:
														convert_weight_and_push(
														    _lowercase      ,							names_to_config[model_name]      ,							checkpoint=names_to_original_checkpoints[model_name]      ,							from_model=names_to_original_models[model_name]()      ,							save_directory=_lowercase      ,							push_to_hub=_lowercase      ,							)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    _lowercase      ,							_lowercase      ,							checkpoint=names_to_original_checkpoints[model_name]      ,							from_model=names_to_original_models[model_name]()      ,							save_directory=_lowercase      ,							push_to_hub=_lowercase      ,							)


if __name__ == "__main__":
							a  :     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: van-tiny/small/base/large. 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(
							    """--van_dir""",
							    required=True,
							    type=Path,
							    help=(
							        """A path to VAN's original implementation directory. You can download from here:"""
							        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
							    ),
							)
							parser.add_argument(
							    """--push_to_hub""",
							    default=True,
							    type=bool,
							    required=False,
							    help="""If True, push model and image processor to the hub.""",
							)

							a  :     List[str]       =       parser.parse_args()
							a  :     Path       =       args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							a  :     Union[str, Any]       =       args.van_dir
							# append the path to the parents to maskformer dir
							sys.path.append(str(van_dir.parent))
							from van.models.van import van_base, van_large, van_small, van_tiny

							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

