



"""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__              =							logging.get_logger()






def 			snake_case_    ( A_   :						int,							A_   :						str,							A_   :						LevitConfig,							A_   :						Path,							A_   :						bool = True		):


       '''simple docstring'''





       print(F'''Converting {name}...'''		)

       with torch.no_grad():
              if hidden_sizes == 1_28:
                     if name[-1] == "S":
                            _lowerCamelCase		:							int            =					timm.create_model('''levit_128s''',							pretrained=A_		)
                     else:
                            _lowerCamelCase		:							Tuple            =					timm.create_model('''levit_128''',							pretrained=A_		)
              if hidden_sizes == 1_92:
                     _lowerCamelCase		:							List[str]            =					timm.create_model('''levit_192''',							pretrained=A_		)
              if hidden_sizes == 2_56:
                     _lowerCamelCase		:							Union[str, Any]            =					timm.create_model('''levit_256''',							pretrained=A_		)
              if hidden_sizes == 3_84:
                     _lowerCamelCase		:							Union[str, Any]            =					timm.create_model('''levit_384''',							pretrained=A_		)

              from_model.eval()
              _lowerCamelCase		:							Any            =					LevitForImageClassificationWithTeacher(A_		).eval()
              _lowerCamelCase		:							int            =					OrderedDict()

              _lowerCamelCase		:							Any            =					from_model.state_dict()
              _lowerCamelCase		:							List[str]            =					list(from_model.state_dict().keys()		)
              _lowerCamelCase		:							List[str]            =					list(our_model.state_dict().keys()		)
              print(len(A_		),							len(A_		)		)
              for i in range(len(A_		)		):
                     _lowerCamelCase		:							Union[str, Any]            =					weights[og_keys[i]]
              our_model.load_state_dict(A_		)

              _lowerCamelCase		:							Optional[int]            =					torch.randn((2, 3, 2_24, 2_24)		)
              _lowerCamelCase		:							Union[str, Any]            =					from_model(A_		)
              _lowerCamelCase		:							Optional[Any]            =					our_model(A_		).logits

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

       _lowerCamelCase		:							int            =					name
       print(A_		)

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

              print(F'''Pushed {checkpoint_name}'''		)






def 			snake_case_    ( A_   :						Path,							A_   :						str = None,							A_   :						bool = True		):


       '''simple docstring'''





       _lowerCamelCase		:							Dict            =					'''imagenet-1k-id2label.json'''
       _lowerCamelCase		:							Dict            =					10_00
       _lowerCamelCase		:							Union[str, Any]            =					(1, num_labels)

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

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

       _lowerCamelCase		:							int            =					partial(A_,							num_labels=A_,							idalabel=A_,							labelaid=A_		)

       _lowerCamelCase		:							Optional[int]            =					{
           '''levit-128S''': 1_28,
           '''levit-128''': 1_28,
           '''levit-192''': 1_92,
           '''levit-256''': 2_56,
           '''levit-384''': 3_84,
       }

       _lowerCamelCase		:							Any            =					{
           '''levit-128S''': ImageNetPreTrainedConfig(
               hidden_sizes=[1_28, 2_56, 3_84],							num_attention_heads=[4, 6, 8],							depths=[2, 3, 4],							key_dim=[16, 16, 16],							drop_path_rate=0,							),
           '''levit-128''': ImageNetPreTrainedConfig(
               hidden_sizes=[1_28, 2_56, 3_84],							num_attention_heads=[4, 8, 12],							depths=[4, 4, 4],							key_dim=[16, 16, 16],							drop_path_rate=0,							),
           '''levit-192''': ImageNetPreTrainedConfig(
               hidden_sizes=[1_92, 2_88, 3_84],							num_attention_heads=[3, 5, 6],							depths=[4, 4, 4],							key_dim=[32, 32, 32],							drop_path_rate=0,							),
           '''levit-256''': ImageNetPreTrainedConfig(
               hidden_sizes=[2_56, 3_84, 5_12],							num_attention_heads=[4, 6, 8],							depths=[4, 4, 4],							key_dim=[32, 32, 32],							drop_path_rate=0,							),
           '''levit-384''': ImageNetPreTrainedConfig(
               hidden_sizes=[3_84, 5_12, 7_68],							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__              =							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__              =							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)









def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations(snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				return sum(count_of_possible_combinations(target - item							) for item in array							)

		return count_of_possible_combinations(snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:

		def count_of_possible_combinations_with_dp_array(
		    snake_case						,   snake_case							) -> int:
				if target < 0:
						return 0
				if target == 0:
						return 1
				if dp_array[target] != -1:
						return dp_array[target]
				__lowercase											=     sum(
				    count_of_possible_combinations_with_dp_array(target - item						,   snake_case							)
				    for item in array							)
				__lowercase											=     answer
				return answer

		__lowercase											=     [-1] * (target + 1)
		return count_of_possible_combinations_with_dp_array(snake_case						,   snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    int:
		__lowercase											=     [0] * (target + 1)
		__lowercase											=     1

		for i in range(1						,   target + 1							):
				for j in range(snake_case							):
						if i - array[j] >= 0:
								dp_array[i] += dp_array[i - array[j]]

		return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		SCREAMING_SNAKE_CASE_   :      Tuple			=   3
		SCREAMING_SNAKE_CASE_   :      Dict			=   5
		SCREAMING_SNAKE_CASE_   :      Any			=   [1, 2, 5]
		print(combination_sum_iv(n, array, target))




