import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


snake_case__				= logging.get_logger(__name__)


snake_case__				= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case__				= {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

snake_case__				= {
    'RUCAIBox/mvp': 1024,
}



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_										=  ['input_ids', 'attention_mask']
     A_										=  MvpTokenizer



     def __init__(					self				,				A_=None				,				A_=None				,				A_=None				,				A_="replace"				,				A_="<s>"				,				A_="</s>"				,				A_="</s>"				,				A_="<s>"				,				A_="<unk>"				,				A_="<pad>"				,				A_="<mask>"				,				A_=False				,				A_=True				,				**A_				,				) ->				Dict:






           """simple docstring"""
           super().__init__(
               A_				,				A_				,				tokenizer_file=A_				,				errors=A_				,				bos_token=A_				,				eos_token=A_				,				sep_token=A_				,				cls_token=A_				,				unk_token=A_				,				pad_token=A_				,				mask_token=A_				,				add_prefix_space=A_				,				trim_offsets=A_				,				**A_				,				)

           _lowerCamelCase			    =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
           if pre_tok_state.get('''add_prefix_space'''				,				A_						) != add_prefix_space:
                 _lowerCamelCase			    =				getattr(A_				,				pre_tok_state.pop('''type'''						)						)
                 _lowerCamelCase			    =				add_prefix_space
                 _lowerCamelCase			    =				pre_tok_class(**A_						)

           _lowerCamelCase			    =				add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           _lowerCamelCase			    =				'''post_processor'''
           _lowerCamelCase			    =				getattr(self.backend_tokenizer				,				A_				,				A_						)
           if tokenizer_component_instance:
                 _lowerCamelCase			    =				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:
                       _lowerCamelCase			    =				tuple(state['''sep''']						)
                 if "cls" in state:
                       _lowerCamelCase			    =				tuple(state['''cls''']						)

                 _lowerCamelCase			    =				False

                 if state.get('''add_prefix_space'''				,				A_						) != add_prefix_space:
                       _lowerCamelCase			    =				add_prefix_space
                       _lowerCamelCase			    =				True

                 if state.get('''trim_offsets'''				,				A_						) != trim_offsets:
                       _lowerCamelCase			    =				trim_offsets
                       _lowerCamelCase			    =				True

                 if changes_to_apply:
                       _lowerCamelCase			    =				getattr(A_				,				state.pop('''type'''						)						)
                       _lowerCamelCase			    =				component_class(**A_						)
                       setattr(self.backend_tokenizer				,				A_				,				A_						)



     @property
     def 		UpperCamelCase_						(					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 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				AddedToken(A_				,				lstrip=A_				,				rstrip=A_						) if isinstance(A_				,				A_						) else value
           _lowerCamelCase			    =				value



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				BatchEncoding:






           """simple docstring"""
           _lowerCamelCase			    =				kwargs.get('''is_split_into_words'''				,				A_						)

           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(*A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				BatchEncoding:






           """simple docstring"""
           _lowerCamelCase			    =				kwargs.get('''is_split_into_words'''				,				A_						)

           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(*A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				Tuple[str]:






           """simple docstring"""
           _lowerCamelCase			    =				self._tokenizer.model.save(A_				,				name=A_						)
           return tuple(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_=None						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				[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 		UpperCamelCase_						(					self				,				A_				,				A_ = 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 + sep + token_ids_a + sep						) * [0]
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)