



import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


_lowerCAmelCase	:   Optional[int]    =   logging.get_logger(__name__)

# General docstring
_lowerCAmelCase	:   Union[str, Any]    =   "PoolFormerConfig"

# Base docstring
_lowerCAmelCase	:   Dict    =   "sail/poolformer_s12"
_lowerCAmelCase	:   Any    =   [1, 5_1_2, 7, 7]

# Image classification docstring
_lowerCAmelCase	:   int    =   "sail/poolformer_s12"
_lowerCAmelCase	:   str    =   "tabby, tabby cat"

_lowerCAmelCase	:   Any    =   [
    "sail/poolformer_s12",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__ = 0.0    ,			snake_case__ = False					)	->     Optional[Any]:



				"""simple docstring"""





				if drop_prob == 0.0 or not training:
								return input
				lowerCAmelCase__		  =  1 - drop_prob
				lowerCAmelCase__		  =  (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
				lowerCAmelCase__		  =  keep_prob + torch.rand(snake_case__    ,			dtype=input.dtype    ,			device=input.device					)
				random_tensor.floor_()  # binarize
				lowerCAmelCase__		  =  input.div(snake_case__					) * random_tensor
				return output


class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_ = None						):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  drop_prob
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						return drop_path(a_    ,self.drop_prob    ,self.training						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return "p={}".format(self.drop_prob						)



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_=None						):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  patch_size if isinstance(a_    ,collections.abc.Iterable						) else (patch_size, patch_size)
						lowerCAmelCase__		  =  stride if isinstance(a_    ,collections.abc.Iterable						) else (stride, stride)
						lowerCAmelCase__		  =  padding if isinstance(a_    ,collections.abc.Iterable						) else (padding, padding)

						lowerCAmelCase__		  =  nn.Convad(a_    ,a_    ,kernel_size=a_    ,stride=a_    ,padding=a_						)
						lowerCAmelCase__		  =  norm_layer(a_						) if norm_layer else nn.Identity()



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.projection(a_						)
						lowerCAmelCase__		  =  self.norm(a_						)
						return embeddings



class 	__snake_case   ( nn.GroupNorm ):
		def __init__(							self    ,a_    ,**a_						):







						"""simple docstring"""
						super().__init__(1    ,a_    ,**a_						)



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  nn.AvgPoolad(a_    ,stride=1    ,padding=pool_size // 2    ,count_include_pad=a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						return self.pool(a_						) - hidden_states



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  nn.Convad(a_    ,a_    ,1						)
						lowerCAmelCase__		  =  nn.Convad(a_    ,a_    ,1						)
						lowerCAmelCase__		  =  PoolFormerDropPath(a_						)
						if isinstance(config.hidden_act    ,a_						):
										lowerCAmelCase__		  =  ACTaFN[config.hidden_act]
						else:
										lowerCAmelCase__		  =  config.hidden_act



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.conva(a_						)
						lowerCAmelCase__		  =  self.act_fn(a_						)
						lowerCAmelCase__		  =  self.drop(a_						)
						lowerCAmelCase__		  =  self.conva(a_						)
						lowerCAmelCase__		  =  self.drop(a_						)

						return hidden_states



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  PoolFormerPooling(a_						)
						lowerCAmelCase__		  =  PoolFormerOutput(a_    ,a_    ,a_    ,a_						)
						lowerCAmelCase__		  =  PoolFormerGroupNorm(a_						)
						lowerCAmelCase__		  =  PoolFormerGroupNorm(a_						)

						# Useful for training neural nets
						lowerCAmelCase__		  =  PoolFormerDropPath(a_						) if drop_path > 0.0 else nn.Identity()
						lowerCAmelCase__		  =  config.use_layer_scale
						if config.use_layer_scale:
										lowerCAmelCase__		  =  nn.Parameter(
										    config.layer_scale_init_value * torch.ones((a_)						)    ,requires_grad=a_						)
										lowerCAmelCase__		  =  nn.Parameter(
										    config.layer_scale_init_value * torch.ones((a_)						)    ,requires_grad=a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						if self.use_layer_scale:
										lowerCAmelCase__		  =  self.pooling(self.before_norm(a_						)						)
										lowerCAmelCase__		  =  self.layer_scale_a.unsqueeze(-1						).unsqueeze(-1						) * pooling_output
										# First residual connection
										lowerCAmelCase__		  =  hidden_states + self.drop_path(a_						)
										lowerCAmelCase__		  =  ()

										lowerCAmelCase__		  =  self.output(self.after_norm(a_						)						)
										lowerCAmelCase__		  =  self.layer_scale_a.unsqueeze(-1						).unsqueeze(-1						) * layer_output
										# Second residual connection
										lowerCAmelCase__		  =  hidden_states + self.drop_path(a_						)

										lowerCAmelCase__		  =  (output,) + outputs
										return outputs

						else:
										lowerCAmelCase__		  =  self.drop_path(self.pooling(self.before_norm(a_						)						)						)
										# First residual connection
										lowerCAmelCase__		  =  pooling_output + hidden_states
										lowerCAmelCase__		  =  ()

										# Second residual connection inside the PoolFormerOutput block
										lowerCAmelCase__		  =  self.drop_path(self.output(self.after_norm(a_						)						)						)
										lowerCAmelCase__		  =  hidden_states + layer_output

										lowerCAmelCase__		  =  (output,) + outputs
										return outputs



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  config
						# stochastic depth decay rule
						lowerCAmelCase__		  =  [x.item() for x in torch.linspace(0    ,config.drop_path_rate    ,sum(config.depths						)						)]

						# patch embeddings
						lowerCAmelCase__		  =  []
						for i in range(config.num_encoder_blocks						):
										embeddings.append(
										    PoolFormerEmbeddings(
										        patch_size=config.patch_sizes[i]    ,stride=config.strides[i]    ,padding=config.padding[i]    ,num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]    ,hidden_size=config.hidden_sizes[i]    ,)						)
						lowerCAmelCase__		  =  nn.ModuleList(a_						)

						# Transformer blocks
						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  0
						for i in range(config.num_encoder_blocks						):
										# each block consists of layers
										lowerCAmelCase__		  =  []
										if i != 0:
														cur += config.depths[i - 1]
										for j in range(config.depths[i]						):
														layers.append(
														    PoolFormerLayer(
														        a_    ,num_channels=config.hidden_sizes[i]    ,pool_size=config.pool_size    ,hidden_size=config.hidden_sizes[i]    ,intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio						)    ,drop_path=dpr[cur + j]    ,)						)
										blocks.append(nn.ModuleList(a_						)						)

						lowerCAmelCase__		  =  nn.ModuleList(a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False    ,a_=True						):







						"""simple docstring"""
						lowerCAmelCase__		  =  () if output_hidden_states else None

						lowerCAmelCase__		  =  pixel_values
						for idx, layers in enumerate(zip(self.patch_embeddings    ,self.block						)						):
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  layers
										# Get patch embeddings from hidden_states
										lowerCAmelCase__		  =  embedding_layer(a_						)
										# Send the embeddings through the blocks
										for _, blk in enumerate(a_						):
														lowerCAmelCase__		  =  blk(a_						)
														lowerCAmelCase__		  =  layer_outputs[0]

										if output_hidden_states:
														lowerCAmelCase__		  =  all_hidden_states + (hidden_states,)

						if not return_dict:
										return tuple(v for v in [hidden_states, all_hidden_states] if v is not None						)

						return BaseModelOutputWithNoAttention(last_hidden_state=a_    ,hidden_states=a_						)



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	PoolFormerConfig
		SCREAMING_SNAKE_CASE__								=	'poolformer'
		SCREAMING_SNAKE_CASE__								=	'pixel_values'
		SCREAMING_SNAKE_CASE__								=	True
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						if isinstance(a_    ,(nn.Linear, nn.Convad)						):
										module.weight.data.normal_(mean=0.0    ,std=self.config.initializer_range						)
										if module.bias is not None:
														module.bias.data.zero_()
						elif isinstance(a_    ,nn.LayerNorm						):
										module.bias.data.zero_()
										module.weight.data.fill_(1.0						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=False						):







						"""simple docstring"""
						if isinstance(a_    ,a_						):
										lowerCAmelCase__		  =  value



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

_lowerCAmelCase	:   Optional[int]    =   R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n"


@add_start_docstrings(
    'The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'							, SCREAMING_SNAKE_CASE							, )
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						super().__init__(a_						)
						lowerCAmelCase__		  =  config

						lowerCAmelCase__		  =  PoolFormerEncoder(a_						)

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







						"""simple docstring"""
						return self.embeddings.patch_embeddings



		@add_start_docstrings_to_model_forward(a_						)
		@add_code_sample_docstrings(
		    checkpoint=_CHECKPOINT_FOR_DOC    ,output_type=a_    ,config_class=_CONFIG_FOR_DOC    ,modality='vision'    ,expected_output=_EXPECTED_OUTPUT_SHAPE    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_ = None    ,a_ = None    ,a_ = None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCAmelCase__		  =  return_dict if return_dict is not None else self.config.use_return_dict

						if pixel_values is None:
										raise ValueError('You have to specify pixel_values'						)

						lowerCAmelCase__		  =  self.encoder(
						    a_    ,output_hidden_states=a_    ,return_dict=a_    ,)
						lowerCAmelCase__		  =  encoder_outputs[0]

						if not return_dict:
										return (sequence_output, None) + encoder_outputs[1:]

						return BaseModelOutputWithNoAttention(
						    last_hidden_state=a_    ,hidden_states=encoder_outputs.hidden_states    ,)



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						super().__init__()
						lowerCAmelCase__		  =  nn.Linear(config.hidden_size    ,config.hidden_size						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.dense(a_						)
						return output



@add_start_docstrings(
    '\n    PoolFormer Model transformer with an image classification head on top\n    '							, SCREAMING_SNAKE_CASE							, )
class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						super().__init__(a_						)
						lowerCAmelCase__		  =  config.num_labels
						lowerCAmelCase__		  =  PoolFormerModel(a_						)

						# Final norm
						lowerCAmelCase__		  =  PoolFormerGroupNorm(config.hidden_sizes[-1]						)
						# Classifier head
						lowerCAmelCase__		  =  (
						    nn.Linear(config.hidden_sizes[-1]    ,config.num_labels						) if config.num_labels > 0 else nn.Identity()
						)

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



		@add_start_docstrings_to_model_forward(a_						)
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,output_type=a_    ,config_class=_CONFIG_FOR_DOC    ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  return_dict if return_dict is not None else self.config.use_return_dict

						lowerCAmelCase__		  =  self.poolformer(
						    a_    ,output_hidden_states=a_    ,return_dict=a_    ,)

						lowerCAmelCase__		  =  outputs[0]

						lowerCAmelCase__		  =  self.classifier(self.norm(a_						).mean([-2, -1]						)						)

						lowerCAmelCase__		  =  None
						if labels is not None:
										if self.config.problem_type is None:
														if self.num_labels == 1:
																		lowerCAmelCase__		  =  'regression'
														elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																		lowerCAmelCase__		  =  'single_label_classification'
														else:
																		lowerCAmelCase__		  =  'multi_label_classification'

										if self.config.problem_type == "regression":
														lowerCAmelCase__		  =  MSELoss()
														if self.num_labels == 1:
																		lowerCAmelCase__		  =  loss_fct(logits.squeeze()    ,labels.squeeze()						)
														else:
																		lowerCAmelCase__		  =  loss_fct(a_    ,a_						)
										elif self.config.problem_type == "single_label_classification":
														lowerCAmelCase__		  =  CrossEntropyLoss()
														lowerCAmelCase__		  =  loss_fct(logits.view(-1    ,self.num_labels						)    ,labels.view(-1						)						)
										elif self.config.problem_type == "multi_label_classification":
														lowerCAmelCase__		  =  BCEWithLogitsLoss()
														lowerCAmelCase__		  =  loss_fct(a_    ,a_						)

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

						return ImageClassifierOutputWithNoAttention(loss=a_    ,logits=a_    ,hidden_states=outputs.hidden_states						)





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()
_lowerCAmelCase	:   Optional[Any]    =   logging.get_logger()


@dataclass
class 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  len(list(m.modules()						)						) == 1 or isinstance(a_    ,nn.Convad						) or isinstance(a_    ,nn.BatchNormad						)
						if has_not_submodules:
										self.traced.append(a_						)
		def __call__(							self    ,a_						):







						"""simple docstring"""
						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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# 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 	__snake_case   :
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	42
		SCREAMING_SNAKE_CASE__								=	1
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE__								=	field(default_factory=SCREAMING_SNAKE_CASE )
		SCREAMING_SNAKE_CASE__								=	True
		def __call__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Tracker(self.dest						)(a_						).parametrized
						lowerCAmelCase__		  =  Tracker(self.src						)(a_						).parametrized

						lowerCAmelCase__		  =  list(filter(lambda a_						: type(a_						) not in self.src_skip    ,a_						)						)
						lowerCAmelCase__		  =  list(filter(lambda a_						: type(a_						) not in self.dest_skip    ,a_						)						)

						if len(a_						) != len(a_						) and self.raise_if_mismatch:
										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}'						)



class 	__snake_case   ( nn.Module ):
		def __init__(							self    ,a_						):







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

						lowerCAmelCase__		  =  []
						# - 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}'
										lowerCAmelCase__		  =  len(a_						) + 1
										feature_blocks.append((f'res{block_index}', v)						)

						lowerCAmelCase__		  =  nn.ModuleDict(a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						return get_trunk_forward_outputs(
						    a_    ,out_feat_keys=a_    ,feature_blocks=self._feature_blocks    ,)



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







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



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						# default to timm!
						if x not in self:
										lowerCAmelCase__		  =  self.convert_name_to_timm(a_						)
										lowerCAmelCase__		  =  partial(lambda: (timm.create_model(a_    ,pretrained=a_						).eval(), None)						)

						else:
										lowerCAmelCase__		  =  super().__getitem__(a_						)

						return val



class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						if "seer" in x and "in1k" not in x:
										lowerCAmelCase__		  =  RegNetModel
						else:
										lowerCAmelCase__		  =  RegNetForImageClassification
						return val







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     List[str]:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__ = True    ,			)	->     Optional[int]:



				"""simple docstring"""





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

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

				lowerCAmelCase__		  =  our_model(snake_case__    ,			output_hidden_states=snake_case__					)
				lowerCAmelCase__		  =  (
				    our_outputs.logits if isinstance(snake_case__    ,			snake_case__					) else our_outputs.last_hidden_state
				)

				lowerCAmelCase__		  =  from_model(snake_case__					)
				lowerCAmelCase__		  =  from_output[-1] if type(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:
								lowerCAmelCase__		  =  our_outputs.hidden_states[-1]

				assert torch.allclose(snake_case__    ,			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=snake_case__    ,			)

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

								print(f'Pushed {name}'					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__ = None    ,			snake_case__ = True					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  'imagenet-1k-id2label.json'
				lowerCAmelCase__		  =  1000
				lowerCAmelCase__		  =  (1, num_labels)

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

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

				lowerCAmelCase__		  =  partial(snake_case__    ,			num_labels=snake_case__    ,			idalabel=snake_case__    ,			labelaid=snake_case__					)

				lowerCAmelCase__		  =  {
				    '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					),
				}

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

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

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

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

				lowerCAmelCase__		  =  partial(
				    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()					)    ,			)

				lowerCAmelCase__		  =  partial(
				    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=620.83    ,			w_m=2.52					)					)					)    ,			)

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

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

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

				lowerCAmelCase__		  =  partial(
				    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=620.83    ,			w_m=2.52					)					)					)    ,			)

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


if __name__ == "__main__":
	_lowerCAmelCase	:   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.",
	)

	_lowerCAmelCase	:   int    =   parser.parse_args()

	_lowerCAmelCase	:   Path    =   args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
