'''simple docstring'''





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MaskFormerSwinBackbone
    from transformers.models.maskformer import MaskFormerSwinModel




class        __A      :



    def __init__(self    :    Dict   ,			__a    :    Dict   ,			__a    :    Any=13   ,			__a    :    Any=32   ,			__a    :    Optional[Any]=2   ,			__a    :    Union[str, Any]=3   ,			__a    :    List[Any]=16   ,			__a    :    int=[1, 2, 1]   ,			__a    :    Dict=[2, 2, 4]   ,			__a    :    Dict=2   ,			__a    :    Tuple=2.0   ,			__a    :    Optional[int]=True   ,			__a    :    Union[str, Any]=0.0   ,			__a    :    Any=0.0   ,			__a    :    Union[str, Any]=0.1   ,			__a    :    int="gelu"   ,			__a    :    Optional[int]=False   ,			__a    :    List[Any]=True   ,			__a    :    List[str]=0.02   ,			__a    :    int=1E-5   ,			__a    :    List[str]=True   ,			__a    :    Union[str, Any]=None   ,			__a    :    List[Any]=True   ,			__a    :    Optional[Any]=10   ,			__a    :    Optional[Any]=8   ,			__a    :    Any=["stage1", "stage2", "stage3"]   ,			__a    :    Tuple=[1, 2, 3]   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   patch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   embed_dim
         UpperCAmelCase_           =   depths
         UpperCAmelCase_           =   num_heads
         UpperCAmelCase_           =   window_size
         UpperCAmelCase_           =   mlp_ratio
         UpperCAmelCase_           =   qkv_bias
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   drop_path_rate
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   use_absolute_embeddings
         UpperCAmelCase_           =   patch_norm
         UpperCAmelCase_           =   layer_norm_eps
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   scope
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   encoder_stride
         UpperCAmelCase_           =   out_features
         UpperCAmelCase_           =   out_indices



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase_           =   None
         if self.use_labels:
              UpperCAmelCase_           =   ids_tensor([self.batch_size]   ,			self.type_sequence_label_size )

         UpperCAmelCase_           =   self.get_config()

         return config, pixel_values, labels



    def 					_lowercase						(self    :    List[str] ):
         return MaskFormerSwinConfig(
             image_size=self.image_size   ,			patch_size=self.patch_size   ,			num_channels=self.num_channels   ,			embed_dim=self.embed_dim   ,			depths=self.depths   ,			num_heads=self.num_heads   ,			window_size=self.window_size   ,			mlp_ratio=self.mlp_ratio   ,			qkv_bias=self.qkv_bias   ,			hidden_dropout_prob=self.hidden_dropout_prob   ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,			drop_path_rate=self.drop_path_rate   ,			hidden_act=self.hidden_act   ,			use_absolute_embeddings=self.use_absolute_embeddings   ,			path_norm=self.patch_norm   ,			layer_norm_eps=self.layer_norm_eps   ,			initializer_range=self.initializer_range   ,			encoder_stride=self.encoder_stride   ,			out_features=self.out_features   ,			out_indices=self.out_indices   ,			)



    def 					_lowercase						(self    :    Tuple   ,			__a    :    List[Any]   ,			__a    :    List[str]   ,			__a    :    Tuple ):
         UpperCAmelCase_           =   MaskFormerSwinModel(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase_           =   model(snake_case__ )

         UpperCAmelCase_           =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
         UpperCAmelCase_           =   int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, expected_seq_len, expected_dim) )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    List[Any]   ,			__a    :    Union[str, Any] ):
         UpperCAmelCase_           =   MaskFormerSwinBackbone(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase_           =   model(snake_case__ )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps )   ,			len(config.out_features ) )
         self.parent.assertListEqual(list(result.feature_maps[0].shape )   ,			[13, 16, 16, 16] )

         # verify channels
         self.parent.assertEqual(len(model.channels )   ,			len(config.out_features ) )
         self.parent.assertListEqual(model.channels   ,			[16, 32, 64] )

         # verify ValueError
         with self.parent.assertRaises(snake_case__ ):
              UpperCAmelCase_           =   ["stem"]
              UpperCAmelCase_           =   MaskFormerSwinBackbone(config=snake_case__ )






    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         UpperCAmelCase_           =   config_and_inputs
         UpperCAmelCase_           =   {"pixel_values": pixel_values}
         return config, inputs_dict




@require_torch
class        __A      ( A_		,	A_		,	unittest.TestCase	):
    a__       :						List[str]          =  (
        (
            MaskFormerSwinModel,
            MaskFormerSwinBackbone,
        )
        if is_torch_available()
        else ()
    )
    a__       :						str          =  {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
    a__       :						Optional[Any]          =  False
    a__       :						List[Any]          =  False
    a__       :						List[str]          =  False
    a__       :						List[str]          =  False
    a__       :						Union[str, Any]          =  False



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   MaskFormerSwinModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=snake_case__   ,			embed_dim=37 )



    @require_torch_multi_gpu
    @unittest.skip(
        reason=(
            "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
            " `nn.DataParallel`"
        ) )
    def 					_lowercase						(self    :    str ):
         pass



    def 					_lowercase						(self    :    str ):
         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 					_lowercase						(self    :    Tuple ):
         return



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__ )



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*snake_case__ )



    @unittest.skip("Swin does not use inputs_embeds" )
    def 					_lowercase						(self    :    int ):
         pass



    @unittest.skip("Swin does not support feedforward chunking" )
    def 					_lowercase						(self    :    List[str] ):
         pass



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(snake_case__ )
              self.assertIsInstance(model.get_input_embeddings()   ,			(nn.Module) )
              UpperCAmelCase_           =   model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case__   ,			nn.Linear ) )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(snake_case__ )
              UpperCAmelCase_           =   inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_           =   [*signature.parameters.keys()]

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



    @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions" )
    def 					_lowercase						(self    :    List[Any] ):
         pass



    @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone" )
    def 					_lowercase						(self    :    Tuple ):
         pass



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    List[Any]   ,			__a    :    str   ,			__a    :    List[Any]   ,			__a    :    Tuple ):
         UpperCAmelCase_           =   model_class(snake_case__ )
         model.to(snake_case__ )
         model.eval()

         with torch.no_grad():
              UpperCAmelCase_           =   model(**self._prepare_for_class(snake_case__   ,			snake_case__ ) )

         UpperCAmelCase_           =   outputs.hidden_states

         UpperCAmelCase_           =   getattr(
             self.model_tester   ,			"expected_num_hidden_layers"   ,			len(self.model_tester.depths ) + 1 )
         self.assertEqual(len(snake_case__ )   ,			snake_case__ )

         # Swin has a different seq_length
         UpperCAmelCase_           =   (
             config.patch_size
             if isinstance(config.patch_size   ,			collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase_           =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:] )   ,			[num_patches, self.model_tester.embed_dim]   ,			)



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase_           =   (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size   ,			collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   True
              self.check_hidden_states_output(snake_case__   ,			snake_case__   ,			snake_case__   ,			snake_case__ )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_           =   True

              self.check_hidden_states_output(snake_case__   ,			snake_case__   ,			snake_case__   ,			snake_case__ )



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_           =   3

         UpperCAmelCase_           =   (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size   ,			collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         UpperCAmelCase_           =   (
             config.patch_size
             if isinstance(config.patch_size   ,			collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase_           =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCAmelCase_           =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   True
              self.check_hidden_states_output(snake_case__   ,			snake_case__   ,			snake_case__   ,			(padded_height, padded_width) )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_           =   True
              self.check_hidden_states_output(snake_case__   ,			snake_case__   ,			snake_case__   ,			(padded_height, padded_width) )



    @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints" )
    def 					_lowercase						(self    :    Optional[int] ):
         pass



    @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
    def 					_lowercase						(self    :    str ):
         pass



    @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
    def 					_lowercase						(self    :    int ):
         pass






    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(__a    :    Union[str, Any] ):
              UpperCAmelCase_           =   0
              return t

         def check_equivalence(__a    :    Union[str, Any]   ,			__a    :    int   ,			__a    :    List[str]   ,			__a    :    Optional[int]={} ):
              with torch.no_grad():
                   UpperCAmelCase_           =   model(**snake_case__   ,			return_dict=snake_case__   ,			**snake_case__ )
                   UpperCAmelCase_           =   model(**snake_case__   ,			return_dict=snake_case__   ,			**snake_case__ ).to_tuple()

                   def recursive_check(__a    :    List[str]   ,			__a    :    Optional[Any] ):
                        if isinstance(snake_case__   ,			(List, Tuple) ):
                             for tuple_iterable_value, dict_iterable_value in zip(snake_case__   ,			snake_case__ ):
                                  recursive_check(snake_case__   ,			snake_case__ )
                        elif isinstance(snake_case__   ,			snake_case__ ):
                             for tuple_iterable_value, dict_iterable_value in zip(
                                 tuple_object.values()   ,			dict_object.values() ):
                                  recursive_check(snake_case__   ,			snake_case__ )
                        elif tuple_object is None:
                             return
                        else:
                             self.assertTrue(
                                 torch.allclose(
                                     set_nan_tensor_to_zero(snake_case__ )   ,			set_nan_tensor_to_zero(snake_case__ )   ,			atol=1E-5 )   ,			msg=(
                                     "Tuple and dict output are not equal. Difference:"
                                     f""" {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:"""
                                     f""" {torch.isnan(snake_case__ ).any()} and `inf`: {torch.isinf(snake_case__ )}. Dict has"""
                                     f""" `nan`: {torch.isnan(snake_case__ ).any()} and `inf`: {torch.isinf(snake_case__ )}."""
                                 )   ,			)

                   recursive_check(snake_case__   ,			snake_case__ )

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(snake_case__ )
              model.to(snake_case__ )
              model.eval()

              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__ )
              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__ )
              check_equivalence(snake_case__   ,			snake_case__   ,			snake_case__ )

              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__   ,			return_labels=snake_case__ )
              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__   ,			return_labels=snake_case__ )
              check_equivalence(snake_case__   ,			snake_case__   ,			snake_case__ )

              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__ )
              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__ )
              check_equivalence(snake_case__   ,			snake_case__   ,			snake_case__   ,			{"output_hidden_states": True} )

              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__   ,			return_labels=snake_case__ )
              UpperCAmelCase_           =   self._prepare_for_class(snake_case__   ,			snake_case__   ,			return_labels=snake_case__ )
              check_equivalence(snake_case__   ,			snake_case__   ,			snake_case__   ,			{"output_hidden_states": True} )




@require_torch
class        __A      ( unittest.TestCase		,	A_	):
    a__       :						int          =  (MaskFormerSwinBackbone,) if is_torch_available() else ()
    a__       :						int          =  MaskFormerSwinConfig



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   MaskFormerSwinModelTester(self )






    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase_           =   inputs_dict["pixel_values"].shape[0]

         for backbone_class in self.all_model_classes:
              UpperCAmelCase_           =   backbone_class(snake_case__ )
              backbone.to(snake_case__ )
              backbone.eval()

              UpperCAmelCase_           =   backbone(**snake_case__ )

              # Test default outputs and verify feature maps
              self.assertIsInstance(outputs.feature_maps   ,			snake_case__ )
              self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
              for feature_map, n_channels in zip(outputs.feature_maps   ,			backbone.channels ):
                   self.assertTrue(feature_map.shape[:2]   ,			(batch_size, n_channels) )
              self.assertIsNone(outputs.hidden_states )
              self.assertIsNone(outputs.attentions )

              # Test output_hidden_states=True
              UpperCAmelCase_           =   backbone(**snake_case__   ,			output_hidden_states=snake_case__ )
              self.assertIsNotNone(outputs.hidden_states )
              self.assertTrue(len(outputs.hidden_states )   ,			len(backbone.stage_names ) )
              # We skip the stem layer
              for hidden_states, n_channels in zip(outputs.hidden_states[1:]   ,			backbone.channels ):
                   for hidden_state in hidden_states:
                        # Hidden states are in the format (batch_size, (height * width), n_channels)
                        UpperCAmelCase_           =   hidden_state.shape
                        self.assertTrue((h_batch_size, h_n_channels)   ,			(batch_size, n_channels) )

            # Test output_attentions=True
              if self.has_attentions:
                   UpperCAmelCase_           =   backbone(**snake_case__   ,			output_attentions=snake_case__ )
                   self.assertIsNotNone(outputs.attentions )






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
__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)





