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  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				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_						) ->				Tuple:






           """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						) ->				List[str]:






           """simple docstring"""
           # 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_										=  42
     A_										=  42
     A_										=  0
     A_										=  field(default_factory=__lowercase )
     A_										=  field(default_factory=__lowercase )



     def __call__(					self				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				Tracker(self.dest						)(A_						).parametrized
           _lowerCamelCase			    =				Tracker(self.src						)(A_						).parametrized

           _lowerCamelCase			    =				list(filter(lambda A_						: type(A_						) not in self.src_skip				,				A_						)						)
           _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      print(F'Converting {name}...'		)
      with torch.no_grad():
            _lowerCamelCase			    =				timm.create_model(__UpperCAmelCase ,						pretrained=__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ResNetForImageClassification(__UpperCAmelCase		).eval()
            _lowerCamelCase			    =				ModuleTransfer(src=__UpperCAmelCase ,						dest=__UpperCAmelCase		)
            _lowerCamelCase			    =				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."

      _lowerCamelCase			    =				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
            _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase = None ,						__UpperCAmelCase = True		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
      _lowerCamelCase			    =				1000
      _lowerCamelCase			    =				(1, num_labels)

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

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

      _lowerCamelCase			    =				partial(__UpperCAmelCase ,						num_labels=__UpperCAmelCase ,						idalabel=__UpperCAmelCase ,						labelaid=__UpperCAmelCase		)

      _lowerCamelCase			    =				{
          '''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__":
     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)
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)