





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_  							=				logging.get_logger()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    LevitConfig	,     __UpperCAmelCase:    Path	,     __UpperCAmelCase:    bool = True   )   ->			List[str]:
    print(f"Converting {name}..."   )

    with torch.no_grad():
        if hidden_sizes == 128:
            if name[-1] == "S":
                UpperCamelCase__							:       int      =   timm.create_model('''levit_128s'''	,     pretrained=__UpperCAmelCase   )
            else:
                UpperCamelCase__							:       int      =   timm.create_model('''levit_128'''	,     pretrained=__UpperCAmelCase   )
        if hidden_sizes == 192:
            UpperCamelCase__							:       int      =   timm.create_model('''levit_192'''	,     pretrained=__UpperCAmelCase   )
        if hidden_sizes == 256:
            UpperCamelCase__							:       Tuple      =   timm.create_model('''levit_256'''	,     pretrained=__UpperCAmelCase   )
        if hidden_sizes == 384:
            UpperCamelCase__							:       Optional[int]      =   timm.create_model('''levit_384'''	,     pretrained=__UpperCAmelCase   )

        from_model.eval()
        UpperCamelCase__							:       Any      =   LevitForImageClassificationWithTeacher(__UpperCAmelCase   ).eval()
        UpperCamelCase__							:       Tuple      =   OrderedDict()

        UpperCamelCase__							:       str      =   from_model.state_dict()
        UpperCamelCase__							:       int      =   list(from_model.state_dict().keys()   )
        UpperCamelCase__							:       str      =   list(our_model.state_dict().keys()   )
        print(len(__UpperCAmelCase   )	,     len(__UpperCAmelCase   )   )
        for i in range(len(__UpperCAmelCase   )   ):
            UpperCamelCase__							:       Tuple      =   weights[og_keys[i]]
        our_model.load_state_dict(__UpperCAmelCase   )

        UpperCamelCase__							:       List[str]      =   torch.randn((2, 3, 224, 224)   )
        UpperCamelCase__							:       Any      =   from_model(__UpperCAmelCase   )
        UpperCamelCase__							:       Union[str, Any]      =   our_model(__UpperCAmelCase   ).logits

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

    UpperCamelCase__							:       Optional[Any]      =   name
    print(__UpperCAmelCase   )

    if push_to_hub:
        our_model.save_pretrained(save_directory / checkpoint_name   )
        UpperCamelCase__							:       Optional[int]      =   LevitImageProcessor()
        image_processor.save_pretrained(save_directory / checkpoint_name   )

        print(f"Pushed {checkpoint_name}"   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Path	,     __UpperCAmelCase:    str = None	,     __UpperCAmelCase:    bool = True   )   ->			Optional[int]:
    UpperCamelCase__							:       int      =   '''imagenet-1k-id2label.json'''
    UpperCamelCase__							:       List[Any]      =   1000
    UpperCamelCase__							:       Optional[int]      =   (1, num_labels)

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

    UpperCamelCase__							:       Optional[int]      =   idalabel
    UpperCamelCase__							:       Tuple      =   {v: k for k, v in idalabel.items()}

    UpperCamelCase__							:       Dict      =   partial(__UpperCAmelCase	,     num_labels=__UpperCAmelCase	,     idalabel=__UpperCAmelCase	,     labelaid=__UpperCAmelCase   )

    UpperCamelCase__							:       str      =   {
        '''levit-128S''': 128,
        '''levit-128''': 128,
        '''levit-192''': 192,
        '''levit-256''': 256,
        '''levit-384''': 384,
    }

    UpperCamelCase__							:       str      =   {
        '''levit-128S''': ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]	,     num_attention_heads=[4, 6, 8]	,     depths=[2, 3, 4]	,     key_dim=[16, 16, 16]	,     drop_path_rate=0	,     ),
        '''levit-128''': ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]	,     num_attention_heads=[4, 8, 12]	,     depths=[4, 4, 4]	,     key_dim=[16, 16, 16]	,     drop_path_rate=0	,     ),
        '''levit-192''': ImageNetPreTrainedConfig(
            hidden_sizes=[192, 288, 384]	,     num_attention_heads=[3, 5, 6]	,     depths=[4, 4, 4]	,     key_dim=[32, 32, 32]	,     drop_path_rate=0	,     ),
        '''levit-256''': ImageNetPreTrainedConfig(
            hidden_sizes=[256, 384, 512]	,     num_attention_heads=[4, 6, 8]	,     depths=[4, 4, 4]	,     key_dim=[32, 32, 32]	,     drop_path_rate=0	,     ),
        '''levit-384''': ImageNetPreTrainedConfig(
            hidden_sizes=[384, 512, 768]	,     num_attention_heads=[6, 9, 12]	,     depths=[4, 4, 4]	,     key_dim=[32, 32, 32]	,     drop_path_rate=0.1	,     ),
    }

    if model_name:
        convert_weight_and_push(
            names_to_hidden_sizes[model_name]	,     __UpperCAmelCase	,     names_to_config[model_name]	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(names_to_hidden_sizes[model_name]	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    return config, expected_shape


if __name__ == "__main__":
  UpperCAmelCase_  							=				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 Levit* architecture,',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='levit-dump-folder/',
      type=Path,
      required=False,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
  parser.add_argument(
      '--no-push_to_hub',
      dest='push_to_hub',
      action='store_false',
      help='Do not push model and image processor to the hub',
  )

  UpperCAmelCase_  							=				parser.parse_args()
  UpperCAmelCase_  							=				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 logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__       )
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							bool     	=	field(default=__lowerCamelCase    ,       metadata={"help": "Whether to use SortishSampler or not."}       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }    ,       )
   a		:							Optional[Union[str, Path, GenerationConfig]]     	=	field(
       default=__lowerCamelCase    ,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }    ,       )




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   super().to_dict()
       for k, v in d.items():
           if isinstance(__magic_name__,	__magic_name__							):
               UpperCamelCase__							:       Tuple      =   v.to_dict()
       return d

