


"""simple docstring"""







import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel






class 						_A  :







    """simple docstring"""




    def __init__(					self				:    Dict				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Any=13				, __UpperCAmelCase				:    Tuple=7				, __UpperCAmelCase				:    Optional[int]=True				, __UpperCAmelCase				:    int=True				, __UpperCAmelCase				:    Any=True				, __UpperCAmelCase				:    Optional[Any]=True				, __UpperCAmelCase				:    int=99				, __UpperCAmelCase				:    Dict=32				, __UpperCAmelCase				:    Union[str, Any]=5				, __UpperCAmelCase				:    str=4				, __UpperCAmelCase				:    Optional[Any]=4				, __UpperCAmelCase				:    Dict="gelu"				, __UpperCAmelCase				:    Union[str, Any]=0.0				, __UpperCAmelCase				:    Optional[Any]=0.1				, __UpperCAmelCase				:    int=True				, __UpperCAmelCase				:    str=512				, __UpperCAmelCase				:    Any=16				, __UpperCAmelCase				:    Optional[Any]=2				, __UpperCAmelCase				:    int=0.02				, __UpperCAmelCase				:    List[Any]=3				, __UpperCAmelCase				:    Union[str, Any]=4				, __UpperCAmelCase				:    List[str]=None				, ):
      a						:		str      							=			parent
      a						:		Union[str, Any]      							=			batch_size
      a						:		Optional[Any]      							=			seq_length
      a						:		int      							=			is_training
      a						:		int      							=			use_input_mask
      a						:		Optional[Any]      							=			use_token_type_ids
      a						:		Any      							=			use_labels
      a						:		Optional[int]      							=			vocab_size
      a						:		Optional[int]      							=			hidden_size
      a						:		Any      							=			num_hidden_layers
      a						:		str      							=			num_attention_heads
      a						:		int      							=			intermediate_multiple_size
      a						:		Any      							=			hidden_act
      a						:		Union[str, Any]      							=			hidden_dropout
      a						:		int      							=			attention_dropout
      a						:		str      							=			weight_tying
      a						:		Optional[int]      							=			max_position_embeddings
      a						:		Optional[int]      							=			type_vocab_size
      a						:		int      							=			type_sequence_label_size
      a						:		Optional[int]      							=			initializer_range
      a						:		str      							=			num_labels
      a						:		List[Any]      							=			num_choices
      a						:		Any      							=			scope






    def 	__snake_case							(					self				:    List[str]):
      a						:		str      							=			ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size)

      a						:		Union[str, Any]      							=			None
      if self.use_input_mask:
        a						:		Union[str, Any]      							=			random_attention_mask([self.batch_size, self.seq_length])

      a						:		Union[str, Any]      							=			None
      if self.use_labels:
        a						:		Tuple      							=			ids_tensor([self.batch_size, self.seq_length]				, self.num_labels)

      a						:		Any      							=			self.get_config()

      return config, input_ids, input_mask, token_labels






    def 	__snake_case							(					self				:    int):
      return GPTNeoXJapaneseConfig(
          vocab_size=self.vocab_size				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_multiple_size=self.intermediate_multiple_size				, hidden_act=self.hidden_act				, hidden_dropout=self.hidden_dropout				, attention_dropout=self.attention_dropout				, weight_tying=self.weight_tying				, max_position_embeddings=self.max_position_embeddings				, type_vocab_size=self.type_vocab_size				, is_decoder=__UpperCAmelCase				, initializer_range=self.initializer_range				, )






    def 	__snake_case							(					self				:    Union[str, Any]):
      a							,		a							,		a							,		a						:		Tuple      							=			self.prepare_config_and_inputs()

      a						:		str      							=			True

      return config, input_ids, input_mask, token_labels






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    List[str]):
      a						:		str      							=			GPTNeoXJapaneseModel(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a						:		str      							=			model(__UpperCAmelCase				, attention_mask=__UpperCAmelCase)
      a						:		Any      							=			model(__UpperCAmelCase)
      self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size))






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    str):
      a						:		Optional[Any]      							=			True
      a						:		Dict      							=			GPTNeoXJapaneseModel(__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a						:		Tuple      							=			model(__UpperCAmelCase				, attention_mask=__UpperCAmelCase)
      self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size))






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    Optional[int]):
      a						:		List[Any]      							=			GPTNeoXJapaneseForCausalLM(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a						:		List[Any]      							=			model(__UpperCAmelCase				, attention_mask=__UpperCAmelCase				, labels=__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size))






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Union[str, Any]):
      a						:		List[Any]      							=			True
      a						:		Any      							=			GPTNeoXJapaneseForCausalLM(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()

      # first forward pass
      a						:		Optional[Any]      							=			model(__UpperCAmelCase				, attention_mask=__UpperCAmelCase				, use_cache=__UpperCAmelCase)
      a						:		Optional[Any]      							=			outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      a						:		Union[str, Any]      							=			ids_tensor((self.batch_size, 3)				, config.vocab_size)
      a						:		Tuple      							=			ids_tensor((self.batch_size, 3)				, vocab_size=2)

      # append to next input_ids and
      a						:		List[Any]      							=			torch.cat([input_ids, next_tokens]				, dim=-1)
      a						:		Optional[int]      							=			torch.cat([input_mask, next_mask]				, dim=-1)

      a						:		Optional[Any]      							=			model(__UpperCAmelCase				, attention_mask=__UpperCAmelCase				, output_hidden_states=__UpperCAmelCase)
      a						:		List[Any]      							=			output_from_no_past["hidden_states"][0]
      a						:		Tuple      							=			model(
          __UpperCAmelCase				, attention_mask=__UpperCAmelCase				, past_key_values=__UpperCAmelCase				, output_hidden_states=__UpperCAmelCase				, )["hidden_states"][0]

      # select random slice
      a						:		List[Any]      							=			ids_tensor((1,)				, output_from_past.shape[-1]).item()
      a						:		List[str]      							=			output_from_no_past[:, -3:, random_slice_idx].detach()
      a						:		List[Any]      							=			output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(__UpperCAmelCase				, __UpperCAmelCase				, atol=1e-3))






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Tuple      							=			self.prepare_config_and_inputs()
      a							,		a							,		a							,		a						:		Any      							=			config_and_inputs
      a						:		int      							=			{"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict






@require_torch
class 						_A  (  _a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
    UpperCAmelCase       :							List[Any]	=       (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
    UpperCAmelCase       :							List[Any]	=       (
        {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
        if is_torch_available()
        else {}
    )
    UpperCAmelCase       :							str	=       False
    UpperCAmelCase       :							Optional[int]	=       False
    UpperCAmelCase       :							Optional[int]	=       False
    UpperCAmelCase       :							Dict	=       False






    def 	__snake_case							(					self				:    Any):
      a						:		Optional[int]      							=			GPTNeoXJapaneseModelTester(self)
      a						:		Optional[int]      							=			ConfigTester(self				, config_class=__UpperCAmelCase				, hidden_size=37)






    def 	__snake_case							(					self				:    List[str]):
      self.config_tester.run_common_tests()






    def 	__snake_case							(					self				:    Tuple):
      a							,		a							,		a							,		a						:		str      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    Dict):
      a							,		a							,		a							,		a						:		Tuple      							=			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    Optional[int]):
      # This regression test was failing with PyTorch < 1.3
      a							,		a							,		a							,		a						:		int      							=			self.model_tester.prepare_config_and_inputs_for_decoder()

      a						:		Union[str, Any]      							=			None

      self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    str):
      a							,		a							,		a							,		a						:		int      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Optional[Any]      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase)






    @slow
    def 	__snake_case							(					self				:    List[str]):
      a						:		Optional[int]      							=			"abeja/gpt-neox-japanese-2.7b"

      a						:		int      							=			["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

      a						:		Union[str, Any]      							=			[
          "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
          "100年後に必要とされる会社は、「人」が中心の会社です。",
          "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
          "国境の長いトンネルを抜けると、そこは雪国だった。",
          "美味しい日本食といえば、やっぱりお寿司ですよね。",
      ]

      a						:		Tuple      							=			GPTNeoXJapaneseTokenizer.from_pretrained(__UpperCAmelCase)
      a						:		List[Any]      							=			GPTNeoXJapaneseForCausalLM.from_pretrained(__UpperCAmelCase)

      a						:		int      							=			[]
      for prompt in prompts:
        a						:		List[str]      							=			tokenizer(__UpperCAmelCase				, return_tensors="pt").input_ids
        a						:		List[str]      							=			model.generate(__UpperCAmelCase				, max_length=50)
        a						:		Union[str, Any]      							=			tokenizer.batch_decode(__UpperCAmelCase				, skip_special_tokens=__UpperCAmelCase)
        predicted_outputs += generated_string
      self.assertListEqual(__UpperCAmelCase				, __UpperCAmelCase)








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)





