'''simple docstring'''




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()
_A								=   logging.get_logger()


@dataclass
class       UpperCAmelCase__  :






   """simple docstring"""
   A							:       nn.Module
   A							:       List[nn.Module]     =    field(default_factory=_snake_case			)
   A							:       list     =    field(default_factory=_snake_case			)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     Dict:
        lowercase_					:       Union[str, Any]     				=					len(list(m.modules()				)				) == 1 or isinstance(_a       ,				nn.Convad				) or isinstance(_a       ,				nn.BatchNormad				)
        if has_not_submodules:
             self.traced.append(_a				)
   def __call__(self       ,				_a				)    ->     Optional[Any]:
        for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook				)				)
        self.module(_a				)
        [x.remove() for x in self.handles]
        return self





   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda _a				: len(list(x.state_dict().keys()				)				) > 0       ,				self.traced				)				)



@dataclass
class       UpperCAmelCase__  :






   """simple docstring"""
   A							:       nn.Module
   A							:       nn.Module
   A							:       int     =    1
   A							:       List     =    field(default_factory=_snake_case			)
   A							:       List     =    field(default_factory=_snake_case			)
   A							:       bool     =    True
   def __call__(self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       Dict     				=					Tracker(self.dest				)(_a				).parametrized
        lowercase_					:       str     				=					Tracker(self.src				)(_a				).parametrized

        lowercase_					:       List[Any]     				=					list(filter(lambda _a				: type(_a				) not in self.src_skip       ,				_a				)				)
        lowercase_					:       Dict     				=					list(filter(lambda _a				: type(_a				) not in self.dest_skip       ,				_a				)				)

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

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



class       UpperCAmelCase__  (       nn.Module			):






   """simple docstring"""
   def __init__(self       ,				_a				)    ->     str:
        super().__init__()

        lowercase_					:       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}'''
             lowercase_					:       int     				=					len(_a				) + 1
             feature_blocks.append((f'''res{block_index}''', v)				)

        lowercase_					:       Tuple     				=					nn.ModuleDict(_a				)





   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        return get_trunk_forward_outputs(
            _a       ,				out_feat_keys=_a       ,				feature_blocks=self._feature_blocks       ,				)



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   def 		_lowerCamelCase (self       ,				_a				)    ->     str:
        lowercase_					:       Dict     				=					x.split('-'				)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)





   def __getitem__(self       ,				_a				)    ->     Callable[[], Tuple[nn.Module, Dict]]:
        # default to timm!
        if x not in self:
             lowercase_					:       List[str]     				=					self.convert_name_to_timm(_a				)
             lowercase_					:       Union[str, Any]     				=					partial(lambda: (timm.create_model(_a       ,				pretrained=_a				).eval(), None)				)

        else:
             lowercase_					:       Tuple     				=					super().__getitem__(_a				)

        return val



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   def __getitem__(self       ,				_a				)    ->     Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x:
             lowercase_					:       List[str]     				=					RegNetModel
        else:
             lowercase_					:       Union[str, Any]     				=					RegNetForImageClassification
        return val



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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = True  ,       ):
     print(f'''Converting {name}...'''							)
     with torch.no_grad():
          lowercase_				,lowercase_					:       Dict     				=					from_model_func()
          lowercase_					:       Tuple     				=					our_model_func(SCREAMING_SNAKE_CASE_							).eval()
          lowercase_					:       Tuple     				=					ModuleTransfer(src=SCREAMING_SNAKE_CASE_  ,       dest=SCREAMING_SNAKE_CASE_  ,       raise_if_mismatch=SCREAMING_SNAKE_CASE_							)
          lowercase_					:       Union[str, Any]     				=					torch.randn((1, 3, 224, 224)							)
          module_transfer(SCREAMING_SNAKE_CASE_							)

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

     lowercase_					:       int     				=					our_model(SCREAMING_SNAKE_CASE_  ,       output_hidden_states=SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Union[str, Any]     				=					(
         our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							) else our_outputs.last_hidden_state
     )

     lowercase_					:       Any     				=					from_model(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       int     				=					from_output[-1] if type(SCREAMING_SNAKE_CASE_							) is list else from_output

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

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

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

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

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_ = None  ,       SCREAMING_SNAKE_CASE_ = True							):
     lowercase_					:       Optional[int]     				=					'imagenet-1k-id2label.json'
     lowercase_					:       Union[str, Any]     				=					1_000
     lowercase_					:       Dict     				=					(1, num_labels)

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

     lowercase_					:       Optional[int]     				=					idalabel
     lowercase_					:       int     				=					{v: k for k, v in idalabel.items()}

     lowercase_					:       List[str]     				=					partial(SCREAMING_SNAKE_CASE_  ,       num_labels=SCREAMING_SNAKE_CASE_  ,       idalabel=SCREAMING_SNAKE_CASE_  ,       labelaid=SCREAMING_SNAKE_CASE_							)

     lowercase_					:       int     				=					{
         '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, 1_008]  ,       groups_width=48  ,       layer_type='x'							),
         'regnet-x-040': ImageNetPreTrainedConfig(
             depths=[2, 5, 14, 2]  ,       hidden_sizes=[80, 240, 560, 1_360]  ,       groups_width=40  ,       layer_type='x'							),
         'regnet-x-064': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]  ,       hidden_sizes=[168, 392, 784, 1_624]  ,       groups_width=56  ,       layer_type='x'							),
         'regnet-x-080': ImageNetPreTrainedConfig(
             depths=[2, 5, 15, 1]  ,       hidden_sizes=[80, 240, 720, 1_920]  ,       groups_width=120  ,       layer_type='x'							),
         'regnet-x-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]  ,       hidden_sizes=[224, 448, 896, 2_240]  ,       groups_width=112  ,       layer_type='x'							),
         'regnet-x-160': ImageNetPreTrainedConfig(
             depths=[2, 6, 13, 1]  ,       hidden_sizes=[256, 512, 896, 2_048]  ,       groups_width=128  ,       layer_type='x'							),
         'regnet-x-320': ImageNetPreTrainedConfig(
             depths=[2, 7, 13, 1]  ,       hidden_sizes=[336, 672, 1_344, 2_520]  ,       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, 1_512]  ,       groups_width=24							),
         'regnet-y-040': ImageNetPreTrainedConfig(
             depths=[2, 6, 12, 2]  ,       hidden_sizes=[128, 192, 512, 1_088]  ,       groups_width=64							),
         'regnet-y-064': ImageNetPreTrainedConfig(
             depths=[2, 7, 14, 2]  ,       hidden_sizes=[144, 288, 576, 1_296]  ,       groups_width=72							),
         'regnet-y-080': ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]  ,       hidden_sizes=[168, 448, 896, 2_016]  ,       groups_width=56							),
         'regnet-y-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]  ,       hidden_sizes=[224, 448, 896, 2_240]  ,       groups_width=112							),
         'regnet-y-160': ImageNetPreTrainedConfig(
             depths=[2, 4, 11, 1]  ,       hidden_sizes=[224, 448, 1_232, 3_024]  ,       groups_width=112							),
         'regnet-y-320': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]  ,       hidden_sizes=[232, 696, 1_392, 3_712]  ,       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, 1_392, 3_712]  ,       groups_width=232							),
         'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]  ,       hidden_sizes=[328, 984, 1_968, 4_920]  ,       groups_width=328							),
         'regnet-y-1280-seer': RegNetConfig(
             depths=[2, 7, 17, 1]  ,       hidden_sizes=[528, 1_056, 2_904, 7_392]  ,       groups_width=264							),
         'regnet-y-2560-seer': RegNetConfig(
             depths=[3, 7, 16, 1]  ,       hidden_sizes=[640, 1_696, 2_544, 5_088]  ,       groups_width=640							),
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]  ,       hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,       groups_width=1_010							),
         # finetuned on imagenet
         'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]  ,       hidden_sizes=[232, 696, 1_392, 3_712]  ,       groups_width=232							),
         'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]  ,       hidden_sizes=[328, 984, 1_968, 4_920]  ,       groups_width=328							),
         'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]  ,       hidden_sizes=[528, 1_056, 2_904, 7_392]  ,       groups_width=264							),
         'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
             depths=[3, 7, 16, 1]  ,       hidden_sizes=[640, 1_696, 2_544, 5_088]  ,       groups_width=640							),
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]  ,       hidden_sizes=[2_020, 4_040, 11_110, 28_280]  ,       groups_width=1_010							),
     }

     lowercase_					:       Dict     				=					NameToOurModelFuncMap()
     lowercase_					:       Optional[int]     				=					NameToFromModelFuncMap()
     # add seer weights logic

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

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

     lowercase_					:       List[Any]     				=					partial(
         SCREAMING_SNAKE_CASE_  ,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'  ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)  ,       )

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

     lowercase_					:       Optional[Any]     				=					partial(
         SCREAMING_SNAKE_CASE_  ,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'  ,       lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27  ,       group_width=1_010  ,       w_a=1_744  ,       w_a=620.83  ,       w_m=2.52							)							)							)  ,       )

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

     lowercase_					:       List[str]     				=					partial(
         SCREAMING_SNAKE_CASE_  ,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'  ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()							)  ,       )

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

     lowercase_					:       str     				=					partial(
         SCREAMING_SNAKE_CASE_  ,       'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'  ,       lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27  ,       group_width=1_010  ,       w_a=1_744  ,       w_a=620.83  ,       w_m=2.52							)							)							)  ,       )

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


if __name__ == "__main__":
 _A								=   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.',
 )

 _A								=   parser.parse_args()

 _A								=   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)





'''simple docstring'''




from __future__ import annotations

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     # preprocessing the first row
     for i in range(1  ,       len(matrix[0]							)							):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1  ,       len(SCREAMING_SNAKE_CASE_							)							):
          for j in range(1  ,       len(matrix[0]							)							):
               matrix[i][j] += min(matrix[i - 1][j]  ,       matrix[i][j - 1]							)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




