import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     return 1.0 / (1.0 + np.exp(-_outputs ))







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		np.max(_outputs     ,  axis=-1     ,  keepdims=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		np.exp(_outputs - maxes )
     return shifted_exp / shifted_exp.sum(axis=-1     ,  keepdims=lowercase__ )

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'sigmoid'
  lowercase_			=     'softmax'
  lowercase_			=     'none'




@add_end_docstrings(
    UpperCamelCase_   ,						r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '   ,						)
class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     False
  lowercase_			=     ClassificationFunction.NONE




  def __init__(      self     :	Tuple     , **a_     :	Dict				)->       List[str]:




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

       self.check_model_type(
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
           if self.framework == 'tf'
           else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	str=None     , a_     :	Any=None     , a_     :	Optional[Any]=""     , **a_     :	str				)->       Optional[Any]:




       """simple docstring"""
       # Using "" as default argument because we're going to use `top_k=None` in user code to declare
       # "No top_k"
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer_kwargs

       SCREAMING_SNAKE_CASE__				:				Tuple			=		{}
       if hasattr(self.model.config     , 'return_all_scores'				) and return_all_scores is None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.model.config.return_all_scores

       if isinstance(a_     , a_				) or top_k is None:
            SCREAMING_SNAKE_CASE__				:				int			=		top_k
            SCREAMING_SNAKE_CASE__				:				List[str]			=		False
       elif return_all_scores is not None:
            warnings.warn(
                '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
                ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'     , a_     , )
            if return_all_scores:
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		None
            else:
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		1

       if isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				int			=		ClassificationFunction[function_to_apply.upper()]

       if function_to_apply is not None:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		function_to_apply
       return preprocess_params, {}, postprocess_params




  def __call__(      self     :	List[str]     , *a_     :	List[Any]     , **a_     :	List[str]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		super().__call__(*a_     , **a_				)
       # TODO try and retrieve it in a nicer way from _sanitize_parameters.
       SCREAMING_SNAKE_CASE__				:				int			=		'top_k' not in kwargs
       if isinstance(args[0]     , a_				) and _legacy:
            # This pipeline is odd, and return a list when single item is run
            return [result]
       else:
            return result




  def 	__lowercase(      self     :	str     , a_     :	Optional[Any]     , **a_     :	List[Any]				)->       Dict[str, GenericTensor]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.framework
       if isinstance(a_     , a_				):
            return self.tokenizer(**a_     , return_tensors=a_     , **a_				)
       elif isinstance(a_     , a_				) and len(a_				) == 1 and isinstance(inputs[0]     , a_				) and len(inputs[0]				) == 2:
            # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
            return self.tokenizer(
                text=inputs[0][0]     , text_pair=inputs[0][1]     , return_tensors=a_     , **a_				)
       elif isinstance(a_     , a_				):
            # This is likely an invalid usage of the pipeline attempting to pass text pairs.
            raise ValueError(
                'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
                ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'				)
       return self.tokenizer(a_     , return_tensors=a_     , **a_				)




  def 	__lowercase(      self     :	List[str]     , a_     :	Tuple				)->       List[Any]:




       """simple docstring"""
       return self.model(**a_				)




  def 	__lowercase(      self     :	str     , a_     :	Tuple     , a_     :	Union[str, Any]=None     , a_     :	str=1     , a_     :	Union[str, Any]=True				)->       Any:




       """simple docstring"""
       # `_legacy` is used to determine if we're running the naked pipeline and in backward
       # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
       # the more natural result containing the list.
       # Default value before `set_parameters`
       if function_to_apply is None:
            if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		ClassificationFunction.SIGMOID
            elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		ClassificationFunction.SOFTMAX
            elif hasattr(self.model.config     , 'function_to_apply'				) and function_to_apply is None:
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		self.model.config.function_to_apply
            else:
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		ClassificationFunction.NONE

       SCREAMING_SNAKE_CASE__				:				int			=		model_outputs['logits'][0]
       SCREAMING_SNAKE_CASE__				:				Tuple			=		outputs.numpy()

       if function_to_apply == ClassificationFunction.SIGMOID:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		sigmoid(a_				)
       elif function_to_apply == ClassificationFunction.SOFTMAX:
            SCREAMING_SNAKE_CASE__				:				str			=		softmax(a_				)
       elif function_to_apply == ClassificationFunction.NONE:
            SCREAMING_SNAKE_CASE__				:				Dict			=		outputs
       else:
            raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}'''				)

       if top_k == 1 and _legacy:
            return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

       SCREAMING_SNAKE_CASE__				:				Any			=		[
           {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(a_				)
       ]
       if not _legacy:
            dict_scores.sort(key=lambda a_				: x["score"]     , reverse=a_				)
            if top_k is not None:
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		dict_scores[:top_k]
       return dict_scores




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()
SCREAMING_SNAKE_CASE__		:					Tuple						=					logging.get_logger()

@dataclass
class       snake_case					:
  lowercase_			=     42
  lowercase_			=     field(default_factory=UpperCamelCase_						)
  lowercase_			=     field(default_factory=UpperCamelCase_						)




  def 	__lowercase(      self     :	Dict     , a_     :	Dict     , a_     :	Tensor     , a_     :	Tensor				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		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     :	Tuple     , a_     :	Tensor				)->       Any:




       """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 	__lowercase(      self     :	Tuple				)->       int:




       """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					:
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     1
  lowercase_			=     field(default_factory=UpperCamelCase_						)
  lowercase_			=     field(default_factory=UpperCamelCase_						)
  lowercase_			=     True




  def __call__(      self     :	List[Any]     , a_     :	Tensor				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		Tracker(self.dest				)(a_				).parametrized
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		Tracker(self.src				)(a_				).parametrized

       SCREAMING_SNAKE_CASE__				:				List[str]			=		list(filter(lambda a_				: type(a_				) not in self.src_skip     , a_				)				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		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     :	List[Any]     , a_     :	nn.Module				)->       Dict:




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

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

       SCREAMING_SNAKE_CASE__				:				Any			=		nn.ModuleDict(a_				)




  def 	__lowercase(      self     :	Tuple     , a_     :	Tensor				)->       Dict:




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




class       snake_case					(      UpperCamelCase_						):




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	str				)->       str:




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




  def __getitem__(      self     :	Union[str, Any]     , a_     :	str				)->       Callable[[], Tuple[nn.Module, Dict]]:




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

       else:
            SCREAMING_SNAKE_CASE__				:				List[str]			=		super().__getitem__(a_				)

       return val




class       snake_case					(      UpperCamelCase_						):




  def __getitem__(      self     :	Any     , a_     :	str				)->       Callable[[], nn.Module]:




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







def     _a				(      lowercase__  :			Any     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			List[Tuple[str, str]] ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Callable[[], nn.Module]     ,  lowercase__  :			Callable[[], nn.Module]     ,  lowercase__  :			RegNetConfig     ,  lowercase__  :			Path     ,  lowercase__  :			bool = True     ,  ):







     '''simple docstring'''


     print(f'''Converting {name}...''' )
     with torch.no_grad():
          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		from_model_func()
          SCREAMING_SNAKE_CASE__				:				int			=		our_model_func(lowercase__ ).eval()
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		ModuleTransfer(src=lowercase__     ,  dest=lowercase__     ,  raise_if_mismatch=lowercase__ )
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.randn((1, 3, 2_24, 2_24) )
          module_transfer(lowercase__ )

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

     SCREAMING_SNAKE_CASE__				:				Tuple			=		our_model(lowercase__     ,  output_hidden_states=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		(
         our_outputs.logits if isinstance(lowercase__     ,  lowercase__ ) else our_outputs.last_hidden_state
     )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		from_model(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		from_output[-1] if type(lowercase__ ) is list else from_output

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

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

          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		2_24 if 'seer' not in name else 3_84
          # we can use the convnext one
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     ,  size=lowercase__ )
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / name     ,  commit_message='Add image processor'     ,  use_temp_dir=lowercase__     ,  )

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







def     _a				(      lowercase__  :			Path     ,  lowercase__  :			str = None     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE__				:				Tuple			=		10_00
     SCREAMING_SNAKE_CASE__				:				Tuple			=		(1, num_labels)

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

     SCREAMING_SNAKE_CASE__				:				str			=		idalabel
     SCREAMING_SNAKE_CASE__				:				Tuple			=		{v: k for k, v in idalabel.items()}

     SCREAMING_SNAKE_CASE__				:				Any			=		partial(lowercase__     ,  num_labels=lowercase__     ,  idalabel=lowercase__     ,  labelaid=lowercase__ )

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		{
         'regnet-x-002': ImageNetPreTrainedConfig(
             depths=[1, 1, 4, 7]     ,  hidden_sizes=[24, 56, 1_52, 3_68]     ,  groups_width=8     ,  layer_type='x' ),
         'regnet-x-004': ImageNetPreTrainedConfig(
             depths=[1, 2, 7, 12]     ,  hidden_sizes=[32, 64, 1_60, 3_84]     ,  groups_width=16     ,  layer_type='x' ),
         'regnet-x-006': ImageNetPreTrainedConfig(
             depths=[1, 3, 5, 7]     ,  hidden_sizes=[48, 96, 2_40, 5_28]     ,  groups_width=24     ,  layer_type='x' ),
         'regnet-x-008': ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 5]     ,  hidden_sizes=[64, 1_28, 2_88, 6_72]     ,  groups_width=16     ,  layer_type='x' ),
         'regnet-x-016': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 2]     ,  hidden_sizes=[72, 1_68, 4_08, 9_12]     ,  groups_width=24     ,  layer_type='x' ),
         'regnet-x-032': ImageNetPreTrainedConfig(
             depths=[2, 6, 15, 2]     ,  hidden_sizes=[96, 1_92, 4_32, 10_08]     ,  groups_width=48     ,  layer_type='x' ),
         'regnet-x-040': ImageNetPreTrainedConfig(
             depths=[2, 5, 14, 2]     ,  hidden_sizes=[80, 2_40, 5_60, 13_60]     ,  groups_width=40     ,  layer_type='x' ),
         'regnet-x-064': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]     ,  hidden_sizes=[1_68, 3_92, 7_84, 16_24]     ,  groups_width=56     ,  layer_type='x' ),
         'regnet-x-080': ImageNetPreTrainedConfig(
             depths=[2, 5, 15, 1]     ,  hidden_sizes=[80, 2_40, 7_20, 19_20]     ,  groups_width=1_20     ,  layer_type='x' ),
         'regnet-x-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]     ,  hidden_sizes=[2_24, 4_48, 8_96, 22_40]     ,  groups_width=1_12     ,  layer_type='x' ),
         'regnet-x-160': ImageNetPreTrainedConfig(
             depths=[2, 6, 13, 1]     ,  hidden_sizes=[2_56, 5_12, 8_96, 20_48]     ,  groups_width=1_28     ,  layer_type='x' ),
         'regnet-x-320': ImageNetPreTrainedConfig(
             depths=[2, 7, 13, 1]     ,  hidden_sizes=[3_36, 6_72, 13_44, 25_20]     ,  groups_width=1_68     ,  layer_type='x' ),
         # y variant
         'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]     ,  hidden_sizes=[24, 56, 1_52, 3_68]     ,  groups_width=8 ),
         'regnet-y-004': ImageNetPreTrainedConfig(
             depths=[1, 3, 6, 6]     ,  hidden_sizes=[48, 1_04, 2_08, 4_40]     ,  groups_width=8 ),
         'regnet-y-006': ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 4]     ,  hidden_sizes=[48, 1_12, 2_56, 6_08]     ,  groups_width=16 ),
         'regnet-y-008': ImageNetPreTrainedConfig(
             depths=[1, 3, 8, 2]     ,  hidden_sizes=[64, 1_28, 3_20, 7_68]     ,  groups_width=16 ),
         'regnet-y-016': ImageNetPreTrainedConfig(
             depths=[2, 6, 17, 2]     ,  hidden_sizes=[48, 1_20, 3_36, 8_88]     ,  groups_width=24 ),
         'regnet-y-032': ImageNetPreTrainedConfig(
             depths=[2, 5, 13, 1]     ,  hidden_sizes=[72, 2_16, 5_76, 15_12]     ,  groups_width=24 ),
         'regnet-y-040': ImageNetPreTrainedConfig(
             depths=[2, 6, 12, 2]     ,  hidden_sizes=[1_28, 1_92, 5_12, 10_88]     ,  groups_width=64 ),
         'regnet-y-064': ImageNetPreTrainedConfig(
             depths=[2, 7, 14, 2]     ,  hidden_sizes=[1_44, 2_88, 5_76, 12_96]     ,  groups_width=72 ),
         'regnet-y-080': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]     ,  hidden_sizes=[1_68, 4_48, 8_96, 20_16]     ,  groups_width=56 ),
         'regnet-y-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]     ,  hidden_sizes=[2_24, 4_48, 8_96, 22_40]     ,  groups_width=1_12 ),
         'regnet-y-160': ImageNetPreTrainedConfig(
             depths=[2, 4, 11, 1]     ,  hidden_sizes=[2_24, 4_48, 12_32, 30_24]     ,  groups_width=1_12 ),
         'regnet-y-320': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]     ,  hidden_sizes=[2_32, 6_96, 13_92, 37_12]     ,  groups_width=2_32 ),
         # models created by SEER -> https://arxiv.org/abs/2202.08360
         'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]     ,  hidden_sizes=[2_32, 6_96, 13_92, 37_12]     ,  groups_width=2_32 ),
         'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]     ,  hidden_sizes=[3_28, 9_84, 19_68, 49_20]     ,  groups_width=3_28 ),
         'regnet-y-1280-seer': RegNetConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[5_28, 10_56, 29_04, 73_92]     ,  groups_width=2_64 ),
         'regnet-y-2560-seer': RegNetConfig(
             depths=[3, 7, 16, 1]     ,  hidden_sizes=[6_40, 16_96, 25_44, 50_88]     ,  groups_width=6_40 ),
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]     ,  groups_width=10_10 ),
         # finetuned on imagenet
         'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]     ,  hidden_sizes=[2_32, 6_96, 13_92, 37_12]     ,  groups_width=2_32 ),
         'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]     ,  hidden_sizes=[3_28, 9_84, 19_68, 49_20]     ,  groups_width=3_28 ),
         'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[5_28, 10_56, 29_04, 73_92]     ,  groups_width=2_64 ),
         'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
             depths=[3, 7, 16, 1]     ,  hidden_sizes=[6_40, 16_96, 25_44, 50_88]     ,  groups_width=6_40 ),
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]     ,  hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]     ,  groups_width=10_10 ),
     }

     SCREAMING_SNAKE_CASE__				:				List[Any]			=		NameToOurModelFuncMap()
     SCREAMING_SNAKE_CASE__				:				Dict			=		NameToFromModelFuncMap()
     # add seer weights logic

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

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

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

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

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'     ,  lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27     ,  group_width=10_10     ,  w_a=17_44     ,  w_a=620.83     ,  w_m=2.52 ) ) )     ,  )

     # IN1K finetuned
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		partial(
         lowercase__     ,  'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,  lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )     ,  )

     SCREAMING_SNAKE_CASE__				:				Any			=		partial(
         lowercase__     ,  '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=10_10     ,  w_a=17_44     ,  w_a=620.83     ,  w_m=2.52 ) ) )     ,  )

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					str						=					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.",
      )

      SCREAMING_SNAKE_CASE__		:					List[Any]						=					parser.parse_args()

      SCREAMING_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)



