






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCamelCase	:							Tuple				=logging.get_logger(__name__)


lowerCamelCase	:							Optional[Any]				={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase	:							Union[str, Any]				={
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase	:							Tuple				={
    '''allenai/led-base-16384''': 16384,
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = VOCAB_FILES_NAMES
 _lowerCAmelCase       :       Optional[Any]        = PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase       :       str        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase       :       Optional[int]        = LEDTokenizer
 _lowerCAmelCase       :       Optional[int]        = ['''input_ids''', '''attention_mask''']







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   List[Any]=None							,   SCREAMING_SNAKE_CASE					:   str=None							,   SCREAMING_SNAKE_CASE					:   Any="replace"							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]="<s>"							,   SCREAMING_SNAKE_CASE					:   List[str]="</s>"							,   SCREAMING_SNAKE_CASE					:   int="</s>"							,   SCREAMING_SNAKE_CASE					:   Any="<s>"							,   SCREAMING_SNAKE_CASE					:   Tuple="<unk>"							,   SCREAMING_SNAKE_CASE					:   List[Any]="<pad>"							,   SCREAMING_SNAKE_CASE					:   Optional[Any]="<mask>"							,   SCREAMING_SNAKE_CASE					:   int=False							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      super().__init__(
          SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   tokenizer_file=SCREAMING_SNAKE_CASE							,   errors=SCREAMING_SNAKE_CASE							,   bos_token=SCREAMING_SNAKE_CASE							,   eos_token=SCREAMING_SNAKE_CASE							,   sep_token=SCREAMING_SNAKE_CASE							,   cls_token=SCREAMING_SNAKE_CASE							,   unk_token=SCREAMING_SNAKE_CASE							,   pad_token=SCREAMING_SNAKE_CASE							,   mask_token=SCREAMING_SNAKE_CASE							,   add_prefix_space=SCREAMING_SNAKE_CASE							,   trim_offsets=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   )

      UpperCamelCase__			:   List[str]       				=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
      if pre_tok_state.get("add_prefix_space"							,   SCREAMING_SNAKE_CASE						) != add_prefix_space:
           UpperCamelCase__			:   List[str]       				=  getattr(SCREAMING_SNAKE_CASE							,   pre_tok_state.pop("type"						)						)
           UpperCamelCase__			:   Union[str, Any]       				=  add_prefix_space
           UpperCamelCase__			:   Dict       				=  pre_tok_class(**SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      UpperCamelCase__			:   List[Any]       				=  "post_processor"
      UpperCamelCase__			:   Optional[Any]       				=  getattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      if tokenizer_component_instance:
           UpperCamelCase__			:   List[Any]       				=  json.loads(tokenizer_component_instance.__getstate__()						)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
                UpperCamelCase__			:   str       				=  tuple(state["sep"]						)
           if "cls" in state:
                UpperCamelCase__			:   List[Any]       				=  tuple(state["cls"]						)

           UpperCamelCase__			:   List[str]       				=  False

           if state.get("add_prefix_space"							,   SCREAMING_SNAKE_CASE						) != add_prefix_space:
                UpperCamelCase__			:   Optional[int]       				=  add_prefix_space
                UpperCamelCase__			:   List[str]       				=  True

           if state.get("trim_offsets"							,   SCREAMING_SNAKE_CASE						) != trim_offsets:
                UpperCamelCase__			:   str       				=  trim_offsets
                UpperCamelCase__			:   Optional[int]       				=  True

           if changes_to_apply:
                UpperCamelCase__			:   Any       				=  getattr(SCREAMING_SNAKE_CASE							,   state.pop("type"						)						)
                UpperCamelCase__			:   Optional[Any]       				=  component_class(**SCREAMING_SNAKE_CASE						)
                setattr(self.backend_tokenizer							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      if self._mask_token is None:
           if self.verbose:
                logger.error("Using mask_token, but it is not set yet."						)
           return None
      return str(self._mask_token						)







 @mask_token.setter
 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  AddedToken(SCREAMING_SNAKE_CASE							,   lstrip=SCREAMING_SNAKE_CASE							,   rstrip=SCREAMING_SNAKE_CASE						) if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) else value
      UpperCamelCase__			:   Tuple       				=  value







 def 					__lowercase					(		self					:   Any							,   *SCREAMING_SNAKE_CASE					:   Any							,   **SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)

      if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

      return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]							,   *SCREAMING_SNAKE_CASE					:   List[Any]							,   **SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  kwargs.get("is_split_into_words"							,   SCREAMING_SNAKE_CASE						)

      if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

      return super()._encode_plus(*SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Optional[str] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE							,   name=SCREAMING_SNAKE_CASE						)
      return tuple(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[int]							,   SCREAMING_SNAKE_CASE					:   Optional[List[int]] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  [self.sep_token_id]
      UpperCamelCase__			:   Optional[Any]       				=  [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 + sep + token_ids_a + sep						) * [0]







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Union[Dict[str, EncodedInput], BatchEncoding]							,   SCREAMING_SNAKE_CASE					:   Optional[int] = None							,   SCREAMING_SNAKE_CASE					:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,   SCREAMING_SNAKE_CASE					:   Optional[int] = None							,   SCREAMING_SNAKE_CASE					:   Optional[bool] = None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  super()._pad(
          encoded_inputs=SCREAMING_SNAKE_CASE							,   max_length=SCREAMING_SNAKE_CASE							,   padding_strategy=SCREAMING_SNAKE_CASE							,   pad_to_multiple_of=SCREAMING_SNAKE_CASE							,   return_attention_mask=SCREAMING_SNAKE_CASE							,   )

      # Load from model defaults
      if return_attention_mask is None:
           UpperCamelCase__			:   str       				=  "attention_mask" in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
           UpperCamelCase__			:   List[str]       				=  encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           UpperCamelCase__			:   Union[str, Any]       				=  len(encoded_inputs["global_attention_mask"]						) != len(SCREAMING_SNAKE_CASE						)

           if needs_to_be_padded:
                UpperCamelCase__			:   Union[str, Any]       				=  len(SCREAMING_SNAKE_CASE						) - len(encoded_inputs["global_attention_mask"]						)

                if self.padding_side == "right":
                     # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                     UpperCamelCase__			:   Tuple       				=  (
                         encoded_inputs["global_attention_mask"] + [-1] * difference
                     )
                elif self.padding_side == "left":
                     UpperCamelCase__			:   Optional[Any]       				=  [-1] * difference + encoded_inputs[
                         "global_attention_mask"
                     ]
                else:
                     raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

      return encoded_inputs







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	:							Optional[int]				=logging.get_logger()





@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       List[nn.Module]        = field(default_factory=A__       )
 _lowerCAmelCase       :       list        = field(default_factory=A__       )







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Tensor							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  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					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''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]						):


      '''simple docstring'''






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







@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       int        = 0
 _lowerCAmelCase       :       List        = field(default_factory=A__       )
 _lowerCAmelCase       :       List        = field(default_factory=A__       )







 def __call__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  Tracker(self.dest						)(SCREAMING_SNAKE_CASE						).parametrized
      UpperCamelCase__			:   Any       				=  Tracker(self.src						)(SCREAMING_SNAKE_CASE						).parametrized

      UpperCamelCase__			:   str       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.src_skip							,   SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   List[Any]       				=  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 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = True			) ->	Optional[int]:
     print(f'Converting {name}...'			)
     with torch.no_grad():
          UpperCamelCase__			:   Union[str, Any]       				=  timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase			).eval()
          UpperCamelCase__			:   List[Any]       				=  ResNetForImageClassification(__lowerCAmelCase			).eval()
          UpperCamelCase__			:   Optional[Any]       				=  ModuleTransfer(src=__lowerCAmelCase					,    dest=__lowerCAmelCase			)
          UpperCamelCase__			:   int       				=  torch.randn((1, 3, 224, 224)			)
          module_transfer(__lowerCAmelCase			)

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

     UpperCamelCase__			:   List[Any]       				=  f'resnet{"-".join(name.split("resnet"			)			)}'
     print(__lowerCAmelCase			)

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

          # we can use the convnext one
          UpperCamelCase__			:   List[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=__lowerCAmelCase					,    )

          print(f'Pushed {checkpoint_name}'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = None					,    __lowerCAmelCase = True			) ->	Dict:
     UpperCamelCase__			:   Dict       				=  "imagenet-1k-id2label.json"
     UpperCamelCase__			:   Optional[int]       				=  1000
     UpperCamelCase__			:   Any       				=  (1, num_labels)

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

     UpperCamelCase__			:   Tuple       				=  idalabel
     UpperCamelCase__			:   Union[str, Any]       				=  {v: k for k, v in idalabel.items()}

     UpperCamelCase__			:   Union[str, Any]       				=  partial(__lowerCAmelCase					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid=__lowerCAmelCase			)

     UpperCamelCase__			:   Union[str, Any]       				=  {
         "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(__lowerCAmelCase					,    names_to_config[model_name]					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     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	:							int				=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)