






"""simple docstring"""





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_	:				Optional[int]										= logging.get_logger()


@dataclass
class      _lowercase :
      _a   :     nn.Module
      _a   :     List[nn.Module]								=   field(default_factory=lowerCAmelCase						)
      _a   :     list								=   field(default_factory=lowerCAmelCase						)


      def _UpperCamelCase						(    self				:	Tuple							,   a				:	Optional[int]							,   a				:	Tensor							,   a				:	Tensor						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=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				:	Tuple							,   a				:	Tensor						):





            """simple docstring"""







            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 _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







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



@dataclass
class      _lowercase :
      _a   :     nn.Module
      _a   :     nn.Module
      _a   :     int								=   0
      _a   :     List								=   field(default_factory=lowerCAmelCase						)
      _a   :     List								=   field(default_factory=lowerCAmelCase						)


      def __call__(    self				:	List[str]							,   a				:	Tensor						):





            """simple docstring"""







            __snake_case      :    List[str]  			=Tracker(self.dest						)(a						).parametrized
            __snake_case      :    List[Any]  			=Tracker(self.src						)(a						).parametrized

            __snake_case      :    Union[str, Any]  			=list(filter(lambda a						: type(a						) not in self.src_skip							,   a						)						)
            __snake_case      :    Dict  			=list(filter(lambda a						: type(a						) not in self.dest_skip							,   a						)						)

            if len(a						) != len(a						):
                  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}'''						)



def     __lowercase					(    a :				str       ,		a :				ResNetConfig       ,		a :				Path       ,		a :				bool = True )					->					Dict:
      print(f'''Converting {name}...''' )
      with torch.no_grad():
            __snake_case      :    Optional[Any]  			=timm.create_model(a       ,		pretrained=a ).eval()
            __snake_case      :    Optional[int]  			=ResNetForImageClassification(a ).eval()
            __snake_case      :    Tuple  			=ModuleTransfer(src=a       ,		dest=a )
            __snake_case      :    Any  			=torch.randn((1, 3, 224, 224) )
            module_transfer(a )

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

      __snake_case      :    Optional[int]  			=f'''resnet{"-".join(name.split("resnet" ) )}'''
      print(a )

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

            # we can use the convnext one
            __snake_case      :    Dict  			=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=a       ,		)

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



def     __lowercase					(    a :				Path       ,		a :				str = None       ,		a :				bool = True )					->					Tuple:
      __snake_case      :    int  			='''imagenet-1k-id2label.json'''
      __snake_case      :    Optional[Any]  			=1_000
      __snake_case      :    Union[str, Any]  			=(1, num_labels)

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

      __snake_case      :    Any  			=idalabel
      __snake_case      :    str  			={v: k for k, v in idalabel.items()}

      __snake_case      :    Optional[int]  			=partial(a       ,		num_labels=a       ,		idalabel=a       ,		labelaid=a )

      __snake_case      :    List[str]  			={
          '''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, 1_024, 2_048]       ,		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, 1_024, 2_048]       ,		layer_type='''bottleneck''' ),
          '''resnet101''': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]       ,		hidden_sizes=[256, 512, 1_024, 2_048]       ,		layer_type='''bottleneck''' ),
          '''resnet152''': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]       ,		hidden_sizes=[256, 512, 1_024, 2_048]       ,		layer_type='''bottleneck''' ),
      }

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


if __name__ == "__main__":
    UpperCamelCase_	:				Optional[Any]										= 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_	:				Union[str, Any]										= parser.parse_args()
    UpperCamelCase_	:				Path										= 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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_	:				Optional[int]										= logging.get_logger(__name__)

UpperCamelCase_	:				Union[str, Any]										= {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}


class      _lowercase (     lowerCAmelCase						):
      _a   :     str								=   '''ctrl'''
      _a   :     Optional[int]								=   ['''past_key_values''']
      _a   :     List[Any]								=   {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }


      def __init__(    self				:	str							,   a				:	Optional[int]=2_4_6_5_3_4							,   a				:	Optional[int]=2_5_6							,   a				:	Optional[int]=1_2_8_0							,   a				:	Any=8_1_9_2							,   a				:	int=4_8							,   a				:	Optional[Any]=1_6							,   a				:	str=0.1							,   a				:	str=0.1							,   a				:	Any=1e-6							,   a				:	Optional[int]=0.0_2							,   a				:	int=True							,   **a				:	str							,   ):





            """simple docstring"""







            __snake_case      :    Tuple  			=vocab_size
            __snake_case      :    Optional[Any]  			=n_positions
            __snake_case      :    List[Any]  			=n_embd
            __snake_case      :    Any  			=n_layer
            __snake_case      :    Any  			=n_head
            __snake_case      :    str  			=dff
            __snake_case      :    List[str]  			=resid_pdrop
            __snake_case      :    str  			=embd_pdrop
            __snake_case      :    Union[str, Any]  			=layer_norm_epsilon
            __snake_case      :    List[Any]  			=initializer_range

            __snake_case      :    Any  			=use_cache

            super().__init__(**a						)


