


'''simple docstring'''

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()
lowerCamelCase				:  List[str]    		=      logging.get_logger()

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A    ,					A = True			)			->			Dict:






       """simple docstring"""







       print(f"Converting {name}..."			)

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

              from_model.eval()
              lowercase__      =					LevitForImageClassificationWithTeacher(A			).eval()
              lowercase__      =					OrderedDict()

              lowercase__      =					from_model.state_dict()
              lowercase__      =					list(from_model.state_dict().keys()			)
              lowercase__      =					list(our_model.state_dict().keys()			)
              print(len(A			)    ,					len(A			)			)
              for i in range(len(A			)			):
                     lowercase__      =					weights[og_keys[i]]
              our_model.load_state_dict(A			)

              lowercase__      =					torch.randn((2, 3, 224, 224)			)
              lowercase__      =					from_model(A			)
              lowercase__      =					our_model(A			).logits

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

       lowercase__      =					name
       print(A			)

       if push_to_hub:
              our_model.save_pretrained(save_directory / checkpoint_name			)
              lowercase__      =					LevitImageProcessor()
              image_processor.save_pretrained(save_directory / checkpoint_name			)

              print(f"Pushed {checkpoint_name}"			)

def _SCREAMING_SNAKE_CASE (A    ,					A = None    ,					A = True			)			->			Optional[Any]:






       """simple docstring"""







       lowercase__      =					'''imagenet-1k-id2label.json'''
       lowercase__      =					1_000
       lowercase__      =					(1, num_labels)

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

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

       lowercase__      =					partial(A    ,					num_labels=A    ,					idalabel=A    ,					labelaid=A			)

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

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


if __name__ == "__main__":
 lowerCamelCase				:  int    		=      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',
 )

 lowerCamelCase				:  Optional[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)








'''simple docstring'''

import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	str       =				RoFormerTokenizer
      lowerCAmelCase__   :	Dict       =				RoFormerTokenizerFast
      lowerCAmelCase__   :	List[str]       =				True
      lowerCAmelCase__   :	List[str]       =				True



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             super().setUp()



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       int					):

             '''simple docstring'''







             return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					'''永和服装饰品有限公司,今天天气非常好'''
             lowercase__      =					'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
             return input_text, output_text



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					self.get_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)

             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)

             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.get_rust_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)
             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)
             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             pass




