






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()
snake_case    				=		logging.get_logger()


@dataclass
class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     UpperCamelCase_							:		nn.Module
     UpperCamelCase_							:		List[nn.Module]			=	field(default_factory=lowerCAmelCase					)
     UpperCamelCase_							:		list			=	field(default_factory=lowerCAmelCase					)


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Tensor					, UpperCAmelCase_       :	Tensor      ):
       SCREAMING_SNAKE_CASE       :      int							=    len(list(m.modules()      )      ) == 1 or isinstance(UpperCAmelCase_					, nn.Convad      ) or isinstance(UpperCAmelCase_					, nn.BatchNormad      )
       if has_not_submodules:
         self.traced.append(UpperCAmelCase_      )


     def __call__(				self       :	Any					, UpperCAmelCase_       :	Tensor      ):
       for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook      )      )
       self.module(UpperCAmelCase_      )
       [x.remove() for x in self.handles]
       return self






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





@dataclass
class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     UpperCamelCase_							:		nn.Module
     UpperCamelCase_							:		nn.Module
     UpperCamelCase_							:		int			=	0
     UpperCamelCase_							:		List			=	field(default_factory=lowerCAmelCase					)
     UpperCamelCase_							:		List			=	field(default_factory=lowerCAmelCase					)


     def __call__(				self       :	Union[str, Any]					, UpperCAmelCase_       :	Tensor      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    Tracker(self.dest      )(UpperCAmelCase_      ).parametrized
       SCREAMING_SNAKE_CASE       :      List[str]							=    Tracker(self.src      )(UpperCAmelCase_      ).parametrized

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    list(filter(lambda UpperCAmelCase_      : type(UpperCAmelCase_      ) not in self.src_skip					, UpperCAmelCase_      )      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    list(filter(lambda UpperCAmelCase_      : type(UpperCAmelCase_      ) not in self.dest_skip					, UpperCAmelCase_      )      )

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

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





def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase = True				):

  """simple docstring"""






  print(F'''Converting {name}...'''				)
  with torch.no_grad():
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    timm.create_model(lowercase						,     pretrained=lowercase				).eval()
    SCREAMING_SNAKE_CASE       :      List[Any]							=    ResNetForImageClassification(lowercase				).eval()
    SCREAMING_SNAKE_CASE       :      str							=    ModuleTransfer(src=lowercase						,     dest=lowercase				)
    SCREAMING_SNAKE_CASE       :      Dict							=    torch.randn((1, 3, 224, 224)				)
    module_transfer(lowercase				)

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

  SCREAMING_SNAKE_CASE       :      Any							=    F'''resnet{'-'.join(name.split('resnet'				)				)}'''
  print(lowercase				)

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

    # we can use the convnext one
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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=lowercase						,     )

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


def   lowerCamelCase__			(     lowercase						,     lowercase = None						,     lowercase = True				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      int							=    "imagenet-1k-id2label.json"
  SCREAMING_SNAKE_CASE       :      List[str]							=    1000
  SCREAMING_SNAKE_CASE       :      Any							=    (1, num_labels)

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

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

  SCREAMING_SNAKE_CASE       :      Dict							=    partial(lowercase						,     num_labels=lowercase						,     idalabel=lowercase						,     labelaid=lowercase				)

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    {
      "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(lowercase						,     names_to_config[model_name]						,     lowercase						,     lowercase				)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(lowercase						,     lowercase						,     lowercase						,     lowercase				)
  return config, expected_shape


if __name__ == "__main__":
   snake_case    				=		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.""",
   )

   snake_case    				=		parser.parse_args()
   snake_case    				=		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)














snake_case    				=		"""Tobias Carryer"""

from time import time


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int=int(time()      )      ):  # noqa: B008
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    multiplier
       SCREAMING_SNAKE_CASE       :      Tuple							=    increment
       SCREAMING_SNAKE_CASE       :      int							=    modulo
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    seed






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   snake_case    				=		LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
   while True:
      print(lcg.next_number())






