



from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


__snake_case						:Dict						=					[
    '''openmmlab/upernet-convnext-tiny''',
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
__snake_case						:Any						=					'''UperNetConfig'''

class    _A	(     nn.Module   ):



       def __init__(		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int]]					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int], str] = 0					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int]] = 1					,		):


           '''simple docstring'''

           super().__init__()
           __a							=    nn.Convad(
               in_channels=__SCREAMING_SNAKE_CASE					,		out_channels=__SCREAMING_SNAKE_CASE					,		kernel_size=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		bias=__SCREAMING_SNAKE_CASE					,		dilation=__SCREAMING_SNAKE_CASE					,		)
           __a							=    nn.BatchNormad(__SCREAMING_SNAKE_CASE)
           __a							=    nn.ReLU()





       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    self.conv(__SCREAMING_SNAKE_CASE)
           __a							=    self.batch_norm(__SCREAMING_SNAKE_CASE)
           __a							=    self.activation(__SCREAMING_SNAKE_CASE)

           return output


class    _A	(     nn.Module   ):



       def __init__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           super().__init__()
           __a							=    [
               nn.AdaptiveAvgPoolad(__SCREAMING_SNAKE_CASE),
               UperNetConvModule(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		kernel_size=1),
           ]
           for i, layer in enumerate(self.layers):
               self.add_module(str(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    input
           for layer in self.layers:
               __a							=    layer(__SCREAMING_SNAKE_CASE)
           return hidden_state


class    _A	(     nn.Module   ):



       def __init__(		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Tuple[int, ...]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					bool):


           '''simple docstring'''

           super().__init__()
           __a							=    pool_scales
           __a							=    align_corners
           __a							=    in_channels
           __a							=    channels
           __a							=    []
           for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE):
               __a							=    UperNetPyramidPoolingBlock(pool_scale=__SCREAMING_SNAKE_CASE					,		in_channels=__SCREAMING_SNAKE_CASE					,		channels=__SCREAMING_SNAKE_CASE)
               self.blocks.append(__SCREAMING_SNAKE_CASE)
               self.add_module(str(__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    []
           for ppm in self.blocks:
               __a							=    ppm(__SCREAMING_SNAKE_CASE)
               __a							=    nn.functional.interpolate(
                   __SCREAMING_SNAKE_CASE					,		size=x.size()[2:]					,		mode='''bilinear'''					,		align_corners=self.align_corners)
               ppm_outs.append(__SCREAMING_SNAKE_CASE)
           return ppm_outs


class    _A	(     nn.Module   ):



       def __init__(		self							:					int					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           super().__init__()

           __a							=    config
           __a							=    config.pool_scales  # e.g. (1, 2, 3, 6)
           __a							=    in_channels
           __a							=    config.hidden_size
           __a							=    False
           __a							=    nn.Convad(self.channels					,		config.num_labels					,		kernel_size=1)

           # PSP Module
           __a							=    UperNetPyramidPoolingModule(
               self.pool_scales					,		self.in_channels[-1]					,		self.channels					,		align_corners=self.align_corners					,		)
           __a							=    UperNetConvModule(
               self.in_channels[-1] + len(self.pool_scales) * self.channels					,		self.channels					,		kernel_size=3					,		padding=1					,		)
           # FPN Module
           __a							=    nn.ModuleList()
           __a							=    nn.ModuleList()
           for in_channels in self.in_channels[:-1]:  # skip the top layer
               __a							=    UperNetConvModule(__SCREAMING_SNAKE_CASE					,		self.channels					,		kernel_size=1)
               __a							=    UperNetConvModule(self.channels					,		self.channels					,		kernel_size=3					,		padding=1)
               self.lateral_convs.append(__SCREAMING_SNAKE_CASE)
               self.fpn_convs.append(__SCREAMING_SNAKE_CASE)

           __a							=    UperNetConvModule(
               len(self.in_channels) * self.channels					,		self.channels					,		kernel_size=3					,		padding=1					,		)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           self.apply(self._init_weights)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		nn.Convad):
               module.weight.data.normal_(mean=0.0					,		std=self.config.initializer_range)
               if module.bias is not None:
                   module.bias.data.zero_()



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    inputs[-1]
           __a							=    [x]
           psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE))
           __a							=    torch.cat(__SCREAMING_SNAKE_CASE					,		dim=1)
           __a							=    self.bottleneck(__SCREAMING_SNAKE_CASE)

           return output





       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]

           laterals.append(self.psp_forward(__SCREAMING_SNAKE_CASE))

           # build top-down path
           __a							=    len(__SCREAMING_SNAKE_CASE)
           for i in range(used_backbone_levels - 1					,		0					,		-1):
               __a							=    laterals[i - 1].shape[2:]
               __a							=    laterals[i - 1] + nn.functional.interpolate(
                   laterals[i]					,		size=__SCREAMING_SNAKE_CASE					,		mode='''bilinear'''					,		align_corners=self.align_corners)

           # build outputs
           __a							=    [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
           # append psp feature
           fpn_outs.append(laterals[-1])

           for i in range(used_backbone_levels - 1					,		0					,		-1):
               __a							=    nn.functional.interpolate(
                   fpn_outs[i]					,		size=fpn_outs[0].shape[2:]					,		mode='''bilinear'''					,		align_corners=self.align_corners)
           __a							=    torch.cat(__SCREAMING_SNAKE_CASE					,		dim=1)
           __a							=    self.fpn_bottleneck(__SCREAMING_SNAKE_CASE)
           __a							=    self.classifier(__SCREAMING_SNAKE_CASE)

           return output


class    _A	(     nn.Module   ):



       def __init__(		self							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int = 2					,		__SCREAMING_SNAKE_CASE							:					int = 3					,		__SCREAMING_SNAKE_CASE							:					Union[int, Tuple[int, int]] = 1):


           '''simple docstring'''

           super().__init__()

           __a							=    config
           __a							=    config.auxiliary_in_channels
           __a							=    config.auxiliary_channels
           __a							=    config.auxiliary_num_convs
           __a							=    config.auxiliary_concat_input
           __a							=    in_index

           __a							=    (kernel_size // 2) * dilation
           __a							=    []
           convs.append(
               UperNetConvModule(
                   self.in_channels					,		self.channels					,		kernel_size=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		dilation=__SCREAMING_SNAKE_CASE))
           for i in range(self.num_convs - 1):
               convs.append(
                   UperNetConvModule(
                       self.channels					,		self.channels					,		kernel_size=__SCREAMING_SNAKE_CASE					,		padding=__SCREAMING_SNAKE_CASE					,		dilation=__SCREAMING_SNAKE_CASE))
           if self.num_convs == 0:
               __a							=    nn.Identity()
           else:
               __a							=    nn.Sequential(*__SCREAMING_SNAKE_CASE)
           if self.concat_input:
               __a							=    UperNetConvModule(
                   self.in_channels + self.channels					,		self.channels					,		kernel_size=__SCREAMING_SNAKE_CASE					,		padding=kernel_size // 2)

           __a							=    nn.Convad(self.channels					,		config.num_labels					,		kernel_size=1)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           self.apply(self._init_weights)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		nn.Convad):
               module.weight.data.normal_(mean=0.0					,		std=self.config.initializer_range)
               if module.bias is not None:
                   module.bias.data.zero_()





       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					torch.Tensor):


           '''simple docstring'''

           __a							=    encoder_hidden_states[self.in_index]
           __a							=    self.convs(__SCREAMING_SNAKE_CASE)
           if self.concat_input:
               __a							=    self.conv_cat(torch.cat([hidden_states, output]					,		dim=1))
           __a							=    self.classifier(__SCREAMING_SNAKE_CASE)
           return output


class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   Optional[Any]						 =						UperNetConfig
       UpperCamelCase__  :   int						 =						'''pixel_values'''
       UpperCamelCase__  :   Dict						 =						True



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               module.backbone.init_weights()
               module.decode_head.init_weights()
               module.auxiliary_head.init_weights()



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           self.backbone.init_weights()
           self.decode_head.init_weights()
           self.auxiliary_head.init_weights()





       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=False):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE):
               __a							=    value


__snake_case						:List[Any]						=					r'''
    Parameters:
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__snake_case						:List[str]						=					r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
            returned tensors for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''

@add_start_docstrings(
    '''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.'''				,__UpperCAmelCase				,)
class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           super().__init__(__SCREAMING_SNAKE_CASE)

           __a							=    AutoBackbone.from_config(config.backbone_config)

           # Semantic segmentation head(s)
           __a							=    UperNetHead(__SCREAMING_SNAKE_CASE					,		in_channels=self.backbone.channels)
           __a							=    UperNetFCNHead(__SCREAMING_SNAKE_CASE) if config.use_auxiliary_head else None

           # Initialize weights and apply final processing
           self.post_init()





       @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length'''))
       @replace_return_docstrings(output_type=__SCREAMING_SNAKE_CASE					,		config_class=_CONFIG_FOR_DOC)
       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[torch.Tensor] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[bool] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[bool] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[torch.Tensor] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[bool] = None					,		):


           '''simple docstring'''

           __a							=    return_dict if return_dict is not None else self.config.use_return_dict
           __a							=    (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           __a							=    output_attentions if output_attentions is not None else self.config.output_attentions

           __a							=    self.backbone.forward_with_filtered_kwargs(
               __SCREAMING_SNAKE_CASE					,		output_hidden_states=__SCREAMING_SNAKE_CASE					,		output_attentions=__SCREAMING_SNAKE_CASE)
           __a							=    outputs.feature_maps

           __a							=    self.decode_head(__SCREAMING_SNAKE_CASE)
           __a							=    nn.functional.interpolate(__SCREAMING_SNAKE_CASE					,		size=pixel_values.shape[2:]					,		mode='''bilinear'''					,		align_corners=__SCREAMING_SNAKE_CASE)

           __a							=    None
           if self.auxiliary_head is not None:
               __a							=    self.auxiliary_head(__SCREAMING_SNAKE_CASE)
               __a							=    nn.functional.interpolate(
                   __SCREAMING_SNAKE_CASE					,		size=pixel_values.shape[2:]					,		mode='''bilinear'''					,		align_corners=__SCREAMING_SNAKE_CASE)

           __a							=    None
           if labels is not None:
               if self.config.num_labels == 1:
                   raise ValueError('''The number of labels should be greater than one''')
               else:
                   # compute weighted loss
                   __a							=    CrossEntropyLoss(ignore_index=self.config.loss_ignore_index)
                   __a							=    loss_fct(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    loss_fct(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
                   __a							=    main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

           if not return_dict:
               if output_hidden_states:
                   __a							=    (logits,) + outputs[1:]
               else:
                   __a							=    (logits,) + outputs[2:]
               return ((loss,) + output) if loss is not None else output

           return SemanticSegmenterOutput(
               loss=__SCREAMING_SNAKE_CASE					,		logits=__SCREAMING_SNAKE_CASE					,		hidden_states=outputs.hidden_states					,		attentions=outputs.attentions					,		)










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						:Dict						=					logging.get_logger()

@dataclass
class    _A	:
       UpperCamelCase__  :   nn.Module
       UpperCamelCase__  :   List[nn.Module]						 =						field(default_factory=__UpperCAmelCase   )
       UpperCamelCase__  :   list						 =						field(default_factory=__UpperCAmelCase   )



       def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Tensor					,		__SCREAMING_SNAKE_CASE							:					Tensor):


           '''simple docstring'''

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



       def __call__(		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Tensor):


           '''simple docstring'''

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





       @property
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

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


@dataclass
class    _A	:
       UpperCamelCase__  :   nn.Module
       UpperCamelCase__  :   nn.Module
       UpperCamelCase__  :   int						 =						1
       UpperCamelCase__  :   List						 =						field(default_factory=__UpperCAmelCase   )
       UpperCamelCase__  :   List						 =						field(default_factory=__UpperCAmelCase   )
       UpperCamelCase__  :   bool						 =						True



       def __call__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tensor):


           '''simple docstring'''

           __a							=    Tracker(self.dest)(__SCREAMING_SNAKE_CASE).parametrized
           __a							=    Tracker(self.src)(__SCREAMING_SNAKE_CASE).parametrized

           __a							=    list(filter(lambda __SCREAMING_SNAKE_CASE: type(__SCREAMING_SNAKE_CASE) not in self.src_skip					,		__SCREAMING_SNAKE_CASE))
           __a							=    list(filter(lambda __SCREAMING_SNAKE_CASE: type(__SCREAMING_SNAKE_CASE) not in self.dest_skip					,		__SCREAMING_SNAKE_CASE))

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

           for dest_m, src_m in zip(__SCREAMING_SNAKE_CASE					,		__SCREAMING_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    _A	(     nn.Module   ):



       def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					nn.Module):


           '''simple docstring'''

           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(__SCREAMING_SNAKE_CASE) + 1
               feature_blocks.append((F'res{block_index}', v))

           __a							=    nn.ModuleDict(__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Tensor):


           '''simple docstring'''

           return get_trunk_forward_outputs(
               __SCREAMING_SNAKE_CASE					,		out_feat_keys=__SCREAMING_SNAKE_CASE					,		feature_blocks=self._feature_blocks					,		)


class    _A	(     __UpperCAmelCase   ):



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    x.split('''-''')
           return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])





       def __getitem__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           if x not in self:
               __a							=    self.convert_name_to_timm(__SCREAMING_SNAKE_CASE)
               __a							=    partial(lambda: (timm.create_model(__SCREAMING_SNAKE_CASE					,		pretrained=__SCREAMING_SNAKE_CASE).eval(), None))

           else:
               __a							=    super().__getitem__(__SCREAMING_SNAKE_CASE)

           return val


class    _A	(     __UpperCAmelCase   ):



       def __getitem__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           if "seer" in x and "in1k" not in x:
               __a							=    RegNetModel
           else:
               __a							=    RegNetForImageClassification
           return val





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase = True       ,			):
    print(f'Converting {name}...'    )
    with torch.no_grad():
        __a	,       __a							=    from_model_func()
        __a							=    our_model_func(_UpperCAmelCase    ).eval()
        __a							=    ModuleTransfer(src=_UpperCAmelCase       ,			dest=_UpperCAmelCase       ,			raise_if_mismatch=_UpperCAmelCase    )
        __a							=    torch.randn((1, 3, 224, 224)    )
        module_transfer(_UpperCAmelCase    )

    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(_UpperCAmelCase       ,			our_model.state_dict()       ,			_UpperCAmelCase    )
        our_model.load_state_dict(_UpperCAmelCase    )

    __a							=    our_model(_UpperCAmelCase       ,			output_hidden_states=_UpperCAmelCase    )
    __a							=    (
        our_outputs.logits if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ) else our_outputs.last_hidden_state
    )

    __a							=    from_model(_UpperCAmelCase    )
    __a							=    from_output[-1] if type(_UpperCAmelCase    ) 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(_UpperCAmelCase       ,			_UpperCAmelCase    ), "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=_UpperCAmelCase       ,			)

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

        print(f'Pushed {name}'    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = True    ):
    __a							=    '''imagenet-1k-id2label.json'''
    __a							=    1000
    __a							=    (1, num_labels)

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

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

    __a							=    partial(_UpperCAmelCase       ,			num_labels=_UpperCAmelCase       ,			idalabel=_UpperCAmelCase       ,			labelaid=_UpperCAmelCase    )

    __a							=    {
        '''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, 11110, 28280]       ,			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, 11110, 28280]       ,			groups_width=1010    ),
    }

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

    def load_using_classy_vision(_UpperCAmelCase       ,			_UpperCAmelCase    ) -> Tuple[nn.Module, Dict]:
        __a							=    torch.hub.load_state_dict_from_url(_UpperCAmelCase       ,			model_dir=str(_UpperCAmelCase    )       ,			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(_UpperCAmelCase    )
        return model.eval(), model_state_dict["heads"]

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

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

    __a							=    partial(
        _UpperCAmelCase       ,			'''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(
        _UpperCAmelCase       ,			'''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_20.83       ,			w_m=2.52    )    )    )       ,			)

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

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

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

    __a							=    partial(
        _UpperCAmelCase       ,			'''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_20.83       ,			w_m=2.52    )    )    )       ,			)

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


if __name__ == "__main__":
 __snake_case						:Tuple						=					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)





