
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFResNetForImageClassification, TFResNetModel
   from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor






class   UpperCAmelCase		:



   def __init__(self		:					List[str]    ,						snake_case__		:					str    ,						snake_case__		:					List[str]=3    ,						snake_case__		:					int=32    ,						snake_case__		:					Optional[int]=3    ,						snake_case__		:					Tuple=10    ,						snake_case__		:					Dict=[10, 20, 30, 40]    ,						snake_case__		:					List[Any]=[1, 1, 2, 1]    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Tuple="relu"    ,						snake_case__		:					Optional[int]=3    ,						snake_case__		:					Any=None    ,						)		->	int:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      parent
          snake_case       :   List[str]							=      batch_size
          snake_case       :   Optional[Any]							=      image_size
          snake_case       :   str							=      num_channels
          snake_case       :   Dict							=      embeddings_size
          snake_case       :   List[str]							=      hidden_sizes
          snake_case       :   List[Any]							=      depths
          snake_case       :   Union[str, Any]							=      is_training
          snake_case       :   List[Any]							=      use_labels
          snake_case       :   Optional[Any]							=      hidden_act
          snake_case       :   Union[str, Any]							=      num_labels
          snake_case       :   Union[str, Any]							=      scope
          snake_case       :   List[str]							=      len(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Tuple							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

          snake_case       :   Tuple							=      None
          if self.use_labels:
                 snake_case       :   Tuple							=      ids_tensor([self.batch_size]    ,						self.num_labels )

          snake_case       :   Union[str, Any]							=      self.get_config()

          return config, pixel_values, labels



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Dict:


          '''simple docstring'''


          return ResNetConfig(
              num_channels=self.num_channels    ,						embeddings_size=self.embeddings_size    ,						hidden_sizes=self.hidden_sizes    ,						depths=self.depths    ,						hidden_act=self.hidden_act    ,						num_labels=self.num_labels    ,						image_size=self.image_size    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					str    ,						snake_case__		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case       :   int							=      TFResNetModel(config=snake_case__ )
          snake_case       :   List[str]							=      model(snake_case__ )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape    ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					Dict    ,						snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.num_labels
          snake_case       :   Dict							=      TFResNetForImageClassification(snake_case__ )
          snake_case       :   List[Any]							=      model(snake_case__    ,						labels=snake_case__ )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   int							=      self.prepare_config_and_inputs()
          snake_case				,					snake_case				,					snake_case       :   Tuple							=      config_and_inputs
          snake_case       :   Optional[int]							=      {"pixel_values": pixel_values}
          return config, inputs_dict






@require_tf
class   UpperCAmelCase		(						A_       ,A_       ,unittest.TestCase ):
   A__					:		Optional[int]						     =     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
   A__					:		Optional[Any]						     =     (
       {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
       if is_tf_available()
       else {}
   )

   A__					:		Union[str, Any]						     =     False
   A__					:		Union[str, Any]						     =     False
   A__					:		List[Any]						     =     False
   A__					:		Tuple						     =     False
   A__					:		Optional[Any]						     =     False



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   int							=      TFResNetModelTester(self )
          snake_case       :   Optional[int]							=      ConfigTester(self    ,						config_class=snake_case__    ,						has_text_modality=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	int:


          '''simple docstring'''


          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Union[str, Any]:


          '''simple docstring'''


          return



   @unittest.skip(reason="ResNet does not use inputs_embeds" )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	List[str]:


          '''simple docstring'''


          pass



   @unittest.skip(reason="ResNet does not support input and output embeddings" )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	str:


          '''simple docstring'''


          pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Tuple:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[Any]							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case       :   Dict							=      model_class(snake_case__ )
                 snake_case       :   Optional[int]							=      inspect.signature(model.call )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 snake_case       :   Union[str, Any]							=      [*signature.parameters.keys()]

                 snake_case       :   Tuple							=      ["pixel_values"]
                 self.assertListEqual(arg_names[:1]    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          def check_hidden_states_output(snake_case__		:					Union[str, Any]    ,						snake_case__		:					str    ,						snake_case__		:					List[str] ):
                 snake_case       :   Dict							=      model_class(snake_case__ )
                 snake_case       :   Optional[int]							=      model(**self._prepare_for_class(snake_case__    ,						snake_case__ ) )

                 snake_case       :   Tuple							=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 snake_case       :   Optional[Any]							=      self.model_tester.num_stages
                 self.assertEqual(len(snake_case__ )    ,						expected_num_stages + 1 )

                 # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:] )    ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,						)

          snake_case				,					snake_case       :   Union[str, Any]							=      self.model_tester.prepare_config_and_inputs_for_common()
          snake_case       :   Tuple							=      ["basic", "bottleneck"]
          for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                        snake_case       :   Any							=      layer_type
                        snake_case       :   Tuple							=      True
                        check_hidden_states_output(snake_case__    ,						snake_case__    ,						snake_case__ )

                        # check that output_hidden_states also work using config
                        del inputs_dict["output_hidden_states"]
                        snake_case       :   str							=      True

                        check_hidden_states_output(snake_case__    ,						snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   int							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case__ )



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Any:


          '''simple docstring'''


          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case       :   str							=      TFResNetModel.from_pretrained(snake_case__ )
                 self.assertIsNotNone(snake_case__ )



def 			UpperCamelCase					(    ):
       snake_case       :   str							=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image






@require_tf
@require_vision
class   UpperCAmelCase		(						unittest.TestCase ):



   @cached_property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[Any]:


          '''simple docstring'''


          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
              if is_vision_available()
              else None
          )



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

          snake_case       :   List[Any]							=      self.default_image_processor
          snake_case       :   Tuple							=      prepare_img()
          snake_case       :   Optional[Any]							=      image_processor(images=snake_case__    ,						return_tensors="tf" )

          # forward pass
          snake_case       :   Any							=      model(**snake_case__ )

          # verify the logits
          snake_case       :   Any							=      tf.TensorShape((1, 10_00) )
          self.assertEqual(outputs.logits.shape    ,						snake_case__ )

          snake_case       :   List[str]							=      tf.constant([-11.1069, -9.7877, -8.3777] )

          self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,						snake_case__    ,						atol=1e-4 ) )







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							=       logging.get_logger()






@dataclass
class   UpperCAmelCase		:
   A__					:		nn.Module
   A__					:		List[nn.Module]						     =     field(default_factory=A_ )
   A__					:		list						     =     field(default_factory=A_ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Tensor    ,						snake_case__		:					Tensor )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      len(list(m.modules() ) ) == 1 or isinstance(snake_case__    ,						nn.Convad ) or isinstance(snake_case__    ,						nn.BatchNormad )
          if has_not_submodules:
                 self.traced.append(snake_case__ )



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


          '''simple docstring'''


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



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[int]:


          '''simple docstring'''


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






@dataclass
class   UpperCAmelCase		:
   A__					:		nn.Module
   A__					:		nn.Module
   A__					:		int						     =     1
   A__					:		List						     =     field(default_factory=A_ )
   A__					:		List						     =     field(default_factory=A_ )
   A__					:		bool						     =     True



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


          '''simple docstring'''


          snake_case       :   str							=      Tracker(self.dest )(snake_case__ ).parametrized
          snake_case       :   Optional[int]							=      Tracker(self.src )(snake_case__ ).parametrized

          snake_case       :   List[str]							=      list(filter(lambda snake_case__ : type(snake_case__ ) not in self.src_skip    ,						snake_case__ ) )
          snake_case       :   Optional[Any]							=      list(filter(lambda snake_case__ : type(snake_case__ ) not in self.dest_skip    ,						snake_case__ ) )

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

          for dest_m, src_m in zip(snake_case__    ,						snake_case__ ):
                 dest_m.load_state_dict(src_m.state_dict() )
                 if self.verbose == 1:
                        print(f"""Transfered from={src_m} to={dest_m}""" )






class   UpperCAmelCase		(						nn.Module ):



   def __init__(self		:					Tuple    ,						snake_case__		:					nn.Module )		->	Optional[Any]:


          '''simple docstring'''


          super().__init__()

          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}"""
                 snake_case       :   Union[str, Any]							=      len(snake_case__ ) + 1
                 feature_blocks.append((f"""res{block_index}""", v) )

          snake_case       :   Optional[Any]							=      nn.ModuleDict(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Tensor )		->	Dict:


          '''simple docstring'''


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






class   UpperCAmelCase		(						A_ ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str )		->	str:


          '''simple docstring'''


          snake_case       :   List[Any]							=      x.split("-" )
          return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )



   def __getitem__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Callable[[], Tuple[nn.Module, Dict]]:


          '''simple docstring'''


          if x not in self:
                 snake_case       :   Dict							=      self.convert_name_to_timm(snake_case__ )
                 snake_case       :   Union[str, Any]							=      partial(lambda: (timm.create_model(snake_case__    ,						pretrained=snake_case__ ).eval(), None) )

          else:
                 snake_case       :   List[str]							=      super().__getitem__(snake_case__ )

          return val






class   UpperCAmelCase		(						A_ ):



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


          '''simple docstring'''


          if "seer" in x and "in1k" not in x:
                 snake_case       :   str							=      RegNetModel
          else:
                 snake_case       :   Optional[Any]							=      RegNetForImageClassification
          return val



def 			UpperCamelCase					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : List[Tuple[str, str]]    ):
       for from_key, to_key in keys:
              snake_case       :   str							=      from_state_dict[from_key].clone()
              print(f"""Copied key={from_key} to={to_key}"""    )
       return to_state_dict


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Callable[[], nn.Module]				,					__lowerCamelCase     : Callable[[], nn.Module]				,					__lowerCamelCase     : RegNetConfig				,					__lowerCamelCase     : Path				,					__lowerCamelCase     : bool = True				,					):
       print(f"""Converting {name}..."""    )
       with torch.no_grad():
              snake_case				,					snake_case       :   int							=      from_model_func()
              snake_case       :   str							=      our_model_func(__lowerCamelCase    ).eval()
              snake_case       :   int							=      ModuleTransfer(src=__lowerCamelCase				,					dest=__lowerCamelCase				,					raise_if_mismatch=__lowerCamelCase    )
              snake_case       :   Dict							=      torch.randn((1, 3, 224, 224)    )
              module_transfer(__lowerCamelCase    )

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

       snake_case       :   Any							=      our_model(__lowerCamelCase				,					output_hidden_states=__lowerCamelCase    )
       snake_case       :   Union[str, Any]							=      (
           our_outputs.logits if isinstance(__lowerCamelCase				,					__lowerCamelCase    ) else our_outputs.last_hidden_state
       )

       snake_case       :   Union[str, Any]							=      from_model(__lowerCamelCase    )
       snake_case       :   Dict							=      from_output[-1] if type(__lowerCamelCase    ) is list else from_output

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

       assert torch.allclose(__lowerCamelCase				,					__lowerCamelCase    ), "The model logits don't match the original one."

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

              snake_case       :   List[str]							=      224 if "seer" not in name else 384
              # we can use the convnext one
              snake_case       :   int							=      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"				,					size=__lowerCamelCase    )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / name				,					commit_message="Add image processor"				,					use_temp_dir=__lowerCamelCase				,					)

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


def 			UpperCamelCase					(    __lowerCamelCase     : Path				,					__lowerCamelCase     : str = None				,					__lowerCamelCase     : bool = True    ):
       snake_case       :   Union[str, Any]							=      "imagenet-1k-id2label.json"
       snake_case       :   List[str]							=      1000
       snake_case       :   List[str]							=      (1, num_labels)

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

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

       snake_case       :   Dict							=      partial(__lowerCamelCase				,					num_labels=__lowerCamelCase				,					idalabel=__lowerCamelCase				,					labelaid=__lowerCamelCase    )

       snake_case       :   Optional[Any]							=      {
           "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    ),
       }

       snake_case       :   Union[str, Any]							=      NameToOurModelFuncMap()
       snake_case       :   str							=      NameToFromModelFuncMap()
       # add seer weights logic

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

       # pretrained
       snake_case       :   List[Any]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   Optional[int]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   List[str]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )				,					)

       snake_case       :   Tuple							=      partial(
           __lowerCamelCase				,					"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
       snake_case       :   List[Any]							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   Tuple							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )				,					)

       snake_case       :   str							=      partial(
           __lowerCamelCase				,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"				,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )				,					)

       snake_case       :   Dict							=      partial(
           __lowerCamelCase				,					"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(
                  __lowerCamelCase				,					names_to_from_model_map[model_name]				,					names_to_ours_model_map[model_name]				,					names_to_config[model_name]				,					__lowerCamelCase				,					__lowerCamelCase				,					)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         __lowerCamelCase				,					names_to_from_model_map[model_name]				,					names_to_ours_model_map[model_name]				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					)
       return config, expected_shape


if __name__ == "__main__":
   __lowerCamelCase							=       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							=       parser.parse_args()

   __lowerCamelCase							=       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)





