




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()
lowerCamelCase				:		Union[str, Any]     =   logging.get_logger()







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						42
 UpperCamelCase						   =						field(default_factory=UpperCamelCase   )
 UpperCamelCase						   =						field(default_factory=UpperCamelCase   )

 def 				a__ (				self					:  int						,							A_					:  Dict						,							A_					:  Tensor						,							A_					:  Tensor  )   ->	Union[str, 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					:  Any						,							A_					:  Tensor  )   ->	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 				a__ (				self					:  int  )   ->	List[Any]:

       """simple docstring"""






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







@dataclass
class   A:



 '''simple docstring'''


 UpperCamelCase						   =						42
 UpperCamelCase						   =						42
 UpperCamelCase						   =						0
 UpperCamelCase						   =						field(default_factory=UpperCamelCase   )
 UpperCamelCase						   =						field(default_factory=UpperCamelCase   )

 def __call__(				self					:  int						,							A_					:  Tensor  )   ->	Optional[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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str	,							lowercase	:       ResNetConfig	,							lowercase	:       Path	,							lowercase	:       bool = True     ):


      '''simple docstring'''



      print(f"""Converting {name}..."""     )
      with torch.no_grad():
            lowerCamelCase_		     =    timm.create_model(lowercase	,							pretrained=lowercase     ).eval()
            lowerCamelCase_		     =    ResNetForImageClassification(lowercase     ).eval()
            lowerCamelCase_		     =    ModuleTransfer(src=lowercase	,							dest=lowercase     )
            lowerCamelCase_		     =    torch.randn((1, 3, 2_24, 2_24)     )
            module_transfer(lowercase     )

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

      lowerCamelCase_		     =    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
            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=lowercase	,							)

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Path	,							lowercase	:       str = None	,							lowercase	:       bool = True     ):


      '''simple docstring'''



      lowerCamelCase_		     =    'imagenet-1k-id2label.json'
      lowerCamelCase_		     =    10_00
      lowerCamelCase_		     =    (1, num_labels)

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

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

      lowerCamelCase_		     =    partial(lowercase	,							num_labels=lowercase	,							idalabel=lowercase	,							labelaid=lowercase     )

      lowerCamelCase_		     =    {
          'resnet18': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]	,							hidden_sizes=[64, 1_28, 2_56, 5_12]	,							layer_type='basic'     ),
          'resnet26': ImageNetPreTrainedConfig(
              depths=[2, 2, 2, 2]	,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,							layer_type='bottleneck'     ),
          'resnet34': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]	,							hidden_sizes=[64, 1_28, 2_56, 5_12]	,							layer_type='basic'     ),
          'resnet50': ImageNetPreTrainedConfig(
              depths=[3, 4, 6, 3]	,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,							layer_type='bottleneck'     ),
          'resnet101': ImageNetPreTrainedConfig(
              depths=[3, 4, 23, 3]	,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,							layer_type='bottleneck'     ),
          'resnet152': ImageNetPreTrainedConfig(
              depths=[3, 8, 36, 3]	,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]	,							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__":
 lowerCamelCase				:		Union[str, 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.",
 )

 lowerCamelCase				:		str     =   parser.parse_args()
 lowerCamelCase				:		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)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_fnet import FNetTokenizer
else:
 lowerCamelCase				:		List[Any]     =   None

lowerCamelCase				:		int     =   logging.get_logger(__name__)
lowerCamelCase				:		Optional[int]     =   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:		Optional[int]     =   {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

lowerCamelCase				:		List[Any]     =   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowerCamelCase				:		Any     =   "▁"







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						['''input_ids''', '''token_type_ids''']
 UpperCamelCase						   =						FNetTokenizer

 def __init__(				self					:  Optional[int]						,							A_					:  Any=None						,							A_					:  int=None						,							A_					:  int=False						,							A_					:  Optional[int]=True						,							A_					:  List[Any]=True						,							A_					:  Tuple="<unk>"						,							A_					:  Optional[int]="[SEP]"						,							A_					:  List[Any]="<pad>"						,							A_					:  Optional[int]="[CLS]"						,							A_					:  Optional[Any]="[MASK]"						,							**A_					:  Dict						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    (
           AddedToken(A_						,							lstrip=A_						,							rstrip=A_						,							normalized=A_  )
           if isinstance(A_						,							A_  )
           else mask_token
       )

       super().__init__(
           A_						,							tokenizer_file=A_						,							do_lower_case=A_						,							remove_space=A_						,							keep_accents=A_						,							unk_token=A_						,							sep_token=A_						,							pad_token=A_						,							cls_token=A_						,							mask_token=A_						,							**A_						,							)

       lowerCamelCase_		     =    do_lower_case
       lowerCamelCase_		     =    remove_space
       lowerCamelCase_		     =    keep_accents
       lowerCamelCase_		     =    vocab_file
       lowerCamelCase_		     =    False if not self.vocab_file else True

 def 				a__ (				self					:  List[Any]						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [self.sep_token_id]
       lowerCamelCase_		     =    [self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep

 def 				a__ (				self					:  Tuple						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [self.sep_token_id]
       lowerCamelCase_		     =    [self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





 def 				a__ (				self					:  Dict						,							A_					:  str						,							A_					:  Optional[str] = None  )   ->	Tuple[str]:

       """simple docstring"""






       if not os.path.isdir(A_  ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
             return
       lowerCamelCase_		     =    os.path.join(
           A_						,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(A_  ):
             copyfile(self.vocab_file						,							A_  )

       return (out_vocab_file,)



