




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			=							logging.get_logger()





@dataclass
class   __a      :
 __lowercase			:							nn.Module
 __lowercase			:							List[nn.Module]       					=			field(default_factory=UpperCAmelCase_				)
 __lowercase			:							list       					=			field(default_factory=UpperCAmelCase_				)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        lowercase__:       Tuple        =       len(list(m.modules()			)			) == 1 or isinstance(__lowercase    ,			nn.Convad			) or isinstance(__lowercase    ,			nn.BatchNormad			)
        if has_not_submodules:
               self.traced.append(__lowercase			)
 def __call__(       self    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        for m in self.module.modules():
               self.handles.append(m.register_forward_hook(self._forward_hook			)			)
        self.module(__lowercase			)
        [x.remove() for x in self.handles]
        return self
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda lowerCAmelCase__			: len(list(x.state_dict().keys()			)			) > 0    ,			self.traced			)			)





@dataclass
class   __a      :
 __lowercase			:							nn.Module
 __lowercase			:							nn.Module
 __lowercase			:							int       					=			0
 __lowercase			:							List       					=			field(default_factory=UpperCAmelCase_				)
 __lowercase			:							List       					=			field(default_factory=UpperCAmelCase_				)
 def __call__(       self    ,			lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[Any]        =       Tracker(self.dest			)(__lowercase			).parametrized
        lowercase__:       Optional[int]        =       Tracker(self.src			)(__lowercase			).parametrized

        lowercase__:       str        =       list(filter(lambda lowerCAmelCase__			: type(__lowercase			) not in self.src_skip    ,			__lowercase			)			)
        lowercase__:       Optional[int]        =       list(filter(lambda lowerCAmelCase__			: type(__lowercase			) not in self.dest_skip    ,			__lowercase			)			)

        if len(__lowercase			) != len(__lowercase			):
               raise Exception(
                   F'Numbers of operations are different. Source module has {len(__lowercase			)} operations while'
                   F' destination module has {len(__lowercase			)}.'			)

        for dest_m, src_m in zip(__lowercase    ,			__lowercase			):
               dest_m.load_state_dict(src_m.state_dict()			)
               if self.verbose == 1:
                      print(F'Transfered from={src_m} to={dest_m}'			)





def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case = True					)  ->    Dict:
       print(f'Converting {name}...'					)
       with torch.no_grad():
              lowercase__:       Any        =       timm.create_model(snake_case      ,							pretrained=snake_case					).eval()
              lowercase__:       Optional[Any]        =       ResNetForImageClassification(snake_case					).eval()
              lowercase__:       int        =       ModuleTransfer(src=snake_case      ,							dest=snake_case					)
              lowercase__:       Dict        =       torch.randn((1, 3, 2_24, 2_24)					)
              module_transfer(snake_case					)

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

       lowercase__:       Tuple        =       f'resnet{"-".join(name.split("resnet"					)					)}'
       print(snake_case					)

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

              # we can use the convnext one
              lowercase__:       Union[str, 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=snake_case      ,							)

              print(f'Pushed {checkpoint_name}'					)




def        snake_case_					(  snake_case      ,							snake_case = None      ,							snake_case = True					)  ->    int:
       lowercase__:       str        =       '''imagenet-1k-id2label.json'''
       lowercase__:       int        =       10_00
       lowercase__:       Optional[Any]        =       (1, num_labels)

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

       lowercase__:       str        =       idalabel
       lowercase__:       List[Any]        =       {v: k for k, v in idalabel.items()}

       lowercase__:       List[str]        =       partial(snake_case      ,							num_labels=snake_case      ,							idalabel=snake_case      ,							labelaid=snake_case					)

       lowercase__:       List[str]        =       {
           '''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(snake_case      ,							names_to_config[model_name]      ,							snake_case      ,							snake_case					)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)
       return config, expected_shape


if __name__ == "__main__":
 __lowerCAmelCase			=							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			=							parser.parse_args()
 __lowerCAmelCase			=							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 typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class   __a      (   tf.keras.layers.Layer				):
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None    ,			lowerCAmelCase__ = None			) ->    int:




        '''simple docstring'''
        super().__init__()
        lowercase__:       Union[str, Any]        =       pad_token_id
        lowercase__:       List[str]        =       max_length
        lowercase__:       int        =       vocab
        lowercase__:       List[Any]        =       merges
        lowercase__:       str        =       BytePairTokenizer(lowerCAmelCase__    ,			lowerCAmelCase__    ,			sequence_length=lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Tuple        =       [' '.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
        lowercase__:       List[Any]        =       tokenizer.get_vocab()
        return cls(lowerCAmelCase__    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       GPTaTokenizer.from_pretrained(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
        return cls.from_tokenizer(lowerCAmelCase__    ,			*lowerCAmelCase__    ,			**lowerCAmelCase__			)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        return cls(**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return {
            "vocab": self.vocab,
            "merges": self.merges,
            "max_length": self.max_length,
            "pad_token_id": self.pad_token_id,
        }
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = None			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.tf_tokenizer(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       tf.ones_like(lowerCAmelCase__			)

        if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__:       int        =       max_length if max_length is not None else self.max_length

               if max_length is not None:
                      lowercase__      ,						lowercase__:       List[Any]        =       pad_model_inputs(
                          lowerCAmelCase__    ,			max_seq_length=lowerCAmelCase__    ,			pad_value=self.pad_token_id			)

        return {"attention_mask": attention_mask, "input_ids": input_ids}


