





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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase        =				logging.get_logger()



@dataclass
class 							_UpperCamelCase					:
       _UpperCamelCase				:					nn.Module
       _UpperCamelCase				:					List[nn.Module]         =   field(default_factory=lowerCAmelCase_     )
       _UpperCamelCase				:					list         =   field(default_factory=lowerCAmelCase_     )

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Tensor					,   _SCREAMING_SNAKE_CASE: Tensor		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  len(list(m.modules()		)		) == 1 or isinstance(_SCREAMING_SNAKE_CASE					,   nn.Convad		) or isinstance(_SCREAMING_SNAKE_CASE					,   nn.BatchNormad		)
              if has_not_submodules:
                     self.traced.append(_SCREAMING_SNAKE_CASE		)

       def __call__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tensor		)       -> Optional[int]:




              """simple docstring"""


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



       @property
       def lowercase (		self: Optional[Any]		)       -> Any:




              """simple docstring"""


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



@dataclass
class 							_UpperCamelCase					:
       _UpperCamelCase				:					nn.Module
       _UpperCamelCase				:					nn.Module
       _UpperCamelCase				:					int         =   0
       _UpperCamelCase				:					List         =   field(default_factory=lowerCAmelCase_     )
       _UpperCamelCase				:					List         =   field(default_factory=lowerCAmelCase_     )

       def __call__(		self: str					,   _SCREAMING_SNAKE_CASE: Tensor		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  Tracker(self.dest		)(_SCREAMING_SNAKE_CASE		).parametrized
              UpperCamelCase_          =  Tracker(self.src		)(_SCREAMING_SNAKE_CASE		).parametrized

              UpperCamelCase_          =  list(filter(lambda _SCREAMING_SNAKE_CASE		: type(_SCREAMING_SNAKE_CASE		) not in self.src_skip					,   _SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  list(filter(lambda _SCREAMING_SNAKE_CASE		: type(_SCREAMING_SNAKE_CASE		) not in self.dest_skip					,   _SCREAMING_SNAKE_CASE		)		)

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

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = True  )     ->		List[Any]:
       print(F'''Converting {name}...'''  )
       with torch.no_grad():
              UpperCamelCase_          =  timm.create_model(UpperCamelCase_					,		pretrained=UpperCamelCase_  ).eval()
              UpperCamelCase_          =  ResNetForImageClassification(UpperCamelCase_  ).eval()
              UpperCamelCase_          =  ModuleTransfer(src=UpperCamelCase_					,		dest=UpperCamelCase_  )
              UpperCamelCase_          =  torch.randn((1, 3, 224, 224)  )
              module_transfer(UpperCamelCase_  )

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

       UpperCamelCase_          =  F'''resnet{"-".join(name.split("resnet"  )  )}'''
       print(UpperCamelCase_  )

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

              # we can use the convnext one
              UpperCamelCase_          =  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"  )
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name					,		commit_message="Add image processor"					,		use_temp_dir=UpperCamelCase_					,		)

              print(F'''Pushed {checkpoint_name}'''  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = True  )     ->		int:
       UpperCamelCase_          =  "imagenet-1k-id2label.json"
       UpperCamelCase_          =  1000
       UpperCamelCase_          =  (1, num_labels)

       UpperCamelCase_          =  "huggingface/label-files"
       UpperCamelCase_          =  num_labels
       UpperCamelCase_          =  json.load(open(hf_hub_download(UpperCamelCase_					,		UpperCamelCase_					,		repo_type="dataset"  )					,		"r"  )  )
       UpperCamelCase_          =  {int(UpperCamelCase_  ): v for k, v in idalabel.items()}

       UpperCamelCase_          =  idalabel
       UpperCamelCase_          =  {v: k for k, v in idalabel.items()}

       UpperCamelCase_          =  partial(UpperCamelCase_					,		num_labels=UpperCamelCase_					,		idalabel=UpperCamelCase_					,		labelaid=UpperCamelCase_  )

       UpperCamelCase_          =  {
           "resnet18": ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]					,		hidden_sizes=[64, 128, 256, 512]					,		layer_type="basic"  ),
           "resnet26": ImageNetPreTrainedConfig(
               depths=[2, 2, 2, 2]					,		hidden_sizes=[256, 512, 1024, 2048]					,		layer_type="bottleneck"  ),
           "resnet34": ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]					,		hidden_sizes=[64, 128, 256, 512]					,		layer_type="basic"  ),
           "resnet50": ImageNetPreTrainedConfig(
               depths=[3, 4, 6, 3]					,		hidden_sizes=[256, 512, 1024, 2048]					,		layer_type="bottleneck"  ),
           "resnet101": ImageNetPreTrainedConfig(
               depths=[3, 4, 23, 3]					,		hidden_sizes=[256, 512, 1024, 2048]					,		layer_type="bottleneck"  ),
           "resnet152": ImageNetPreTrainedConfig(
               depths=[3, 8, 36, 3]					,		hidden_sizes=[256, 512, 1024, 2048]					,		layer_type="bottleneck"  ),
       }

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


if __name__ == "__main__":
     _UpperCAmelCase        =				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 resnet* architecture,'
             ' currently: resnet18,26,34,50,101,152. 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.',
     )

     _UpperCAmelCase        =				parser.parse_args()
     _UpperCAmelCase        =				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)










import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Optional[int]:
       return x + 2







class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Optional[int]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

              UpperCamelCase_          =  "x = y"
              UpperCamelCase_          =  {"y": 5}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 5, "y": 5}		)

       def lowercase (		self: List[str]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  "y = add_two(x)"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

              # Won't work without the tool
              with CaptureStdout() as out:
                     UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result is None
              assert "tried to execute add_two" in out.out

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




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

       def lowercase (		self: Dict		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)

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




              """simple docstring"""


              UpperCamelCase_          =  "x = 3\ny = 5"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "text = f'This is x: {x}.'"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == "This is x: 3."
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "text": "This is x: 3."}		)

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




              """simple docstring"""


              UpperCamelCase_          =  "if x <= 3:\n    y = 2\nelse:\n    y = 5"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 2}		)

              UpperCamelCase_          =  {"x": 8}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 8, "y": 5}		)

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




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [3, 5]		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

       def lowercase (		self: List[str]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "y = x"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 3}		)

       def lowercase (		self: str		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]\ntest_list[1]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)



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




              """simple docstring"""


              UpperCamelCase_          =  "x = 0\nfor i in range(3):\n    x = i"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"range": range}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 2, "i": 2}		)

