





"""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()
SCREAMING_SNAKE_CASE__					=   logging.get_logger()


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						LevitConfig							,						SCREAMING_SNAKE_CASE     :						Path							,						SCREAMING_SNAKE_CASE     :						bool = True			):







      '''simple docstring'''




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

      with torch.no_grad():
            if hidden_sizes == 1_28:
                  if name[-1] == "S":
                        lowerCAmelCase  						=       timm.create_model("""levit_128s"""							,						pretrained=SCREAMING_SNAKE_CASE			)
                  else:
                        lowerCAmelCase  						=       timm.create_model("""levit_128"""							,						pretrained=SCREAMING_SNAKE_CASE			)
            if hidden_sizes == 1_92:
                  lowerCAmelCase  						=       timm.create_model("""levit_192"""							,						pretrained=SCREAMING_SNAKE_CASE			)
            if hidden_sizes == 2_56:
                  lowerCAmelCase  						=       timm.create_model("""levit_256"""							,						pretrained=SCREAMING_SNAKE_CASE			)
            if hidden_sizes == 3_84:
                  lowerCAmelCase  						=       timm.create_model("""levit_384"""							,						pretrained=SCREAMING_SNAKE_CASE			)

            from_model.eval()
            lowerCAmelCase  						=       LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE			).eval()
            lowerCAmelCase  						=       OrderedDict()

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

            lowerCAmelCase  						=       torch.randn((2, 3, 2_24, 2_24)			)
            lowerCAmelCase  						=       from_model(SCREAMING_SNAKE_CASE			)
            lowerCAmelCase  						=       our_model(SCREAMING_SNAKE_CASE			).logits

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

      lowerCAmelCase  						=       name
      print(SCREAMING_SNAKE_CASE			)

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

            print(F'Pushed {checkpoint_name}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Path							,						SCREAMING_SNAKE_CASE     :						str = None							,						SCREAMING_SNAKE_CASE     :						bool = True			):







      '''simple docstring'''




      lowerCAmelCase  						=       """imagenet-1k-id2label.json"""
      lowerCAmelCase  						=       10_00
      lowerCAmelCase  						=       (1, num_labels)

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

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

      lowerCAmelCase  						=       partial(SCREAMING_SNAKE_CASE							,						num_labels=SCREAMING_SNAKE_CASE							,						idalabel=SCREAMING_SNAKE_CASE							,						labelaid=SCREAMING_SNAKE_CASE			)

      lowerCAmelCase  						=       {
          """levit-128S""": 1_28,
          """levit-128""": 1_28,
          """levit-192""": 1_92,
          """levit-256""": 2_56,
          """levit-384""": 3_84,
      }

      lowerCAmelCase  						=       {
          """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]							,						SCREAMING_SNAKE_CASE							,						names_to_config[model_name]							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      return config, expected_shape


if __name__ == "__main__":
       SCREAMING_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 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",
       )

       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       SCREAMING_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)










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[Any]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		if n == 0:
				return 0
		__a           =			float('''-inf'''			)
		for i in range(1    ,					n + 1			):
				__a           =			max(
				    lowerCAmelCase__    ,					prices[i - 1] + naive_cut_rod_recursive(n - i    ,					lowerCAmelCase__			)			)

		return max_revue

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[str]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		return _top_down_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     list			) ->      Union[str, Any]:
		if max_rev[n] >= 0:
				return max_rev[n]
		elif n == 0:
				return 0
		else:
				__a           =			float('''-inf'''			)
				for i in range(1    ,					n + 1			):
						__a           =			max(
						    lowerCAmelCase__    ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)    ,					)

				__a           =			max_revenue

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      Dict:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)

		# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
		# length 0.
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		__a           =			0

		for i in range(1    ,					n + 1			):
				__a           =			max_rev[i]
				for j in range(1    ,					i + 1			):
						__a           =			max(lowerCAmelCase__    ,					prices[j - 1] + max_rev[i - j]			)

				__a           =			max_revenue_i

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      str:
		if n < 0:
				__a           =			f'''n must be greater than or equal to 0. Got n = {n}'''
				raise ValueError(lowerCAmelCase__			)

		if n > len(lowerCAmelCase__			):
				__a           =			(
				    '''Each integral piece of rod must have a corresponding price. '''
				    f'''Got n = {n} but length of prices = {len(lowerCAmelCase__			)}'''
				)
				raise ValueError(lowerCAmelCase__			)

def        lowercase							(		) ->      int:
		__a           =			[6, 10, 12, 15, 20, 23]
		__a           =			len(lowerCAmelCase__			)

		# the best revenue comes from cutting the rod into 6 pieces, each
		# of length 1 resulting in a revenue of 6 * 6 = 36.
		__a           =			36

		__a           =			top_down_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			bottom_up_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			naive_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__			)

		assert expected_max_revenue == max_rev_top_down
		assert max_rev_top_down == max_rev_bottom_up
		assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
				main()






