





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()
_snake_case    :       Dict		       =    logging.get_logger()

def     a_       (					lowerCAmelCase_ :     int,      lowerCAmelCase_ :     str,      lowerCAmelCase_ :     LevitConfig,      lowerCAmelCase_ :     Path,      lowerCAmelCase_ :     bool = True      ):
   print(F"""Converting {name}..."""      )

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

      from_model.eval()
      __lowerCAmelCase				      =      LevitForImageClassificationWithTeacher(lowerCAmelCase_      ).eval()
      __lowerCAmelCase				      =      OrderedDict()

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

      __lowerCAmelCase				      =      torch.randn((2, 3, 224, 224)      )
      __lowerCAmelCase				      =      from_model(lowerCAmelCase_      )
      __lowerCAmelCase				      =      our_model(lowerCAmelCase_      ).logits

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

   __lowerCAmelCase				      =      name
   print(lowerCAmelCase_      )

   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     a_       (					lowerCAmelCase_ :     Path,      lowerCAmelCase_ :     str = None,      lowerCAmelCase_ :     bool = True      ):
   __lowerCAmelCase				      =      'imagenet-1k-id2label.json'
   __lowerCAmelCase				      =      1000
   __lowerCAmelCase				      =      (1, num_labels)

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

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

   __lowerCAmelCase				      =      partial(lowerCAmelCase_,      num_labels=lowerCAmelCase_,      idalabel=lowerCAmelCase_,      labelaid=lowerCAmelCase_      )

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

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


if __name__ == "__main__":
     _snake_case    :       List[str]		       =    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',
     )

     _snake_case    :       List[Any]		       =    parser.parse_args()
     _snake_case    :       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"""



from collections import defaultdict
class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			total  # total no of tasks (N)

										# DP table will have a dimension of (2^M)*N
										# initially all values are set to -1
										A__       :     Optional[int]								=			[
										    [-1 for i in range(total + 1							)] for j in range(2 ** len(snake_case_							)							)
										]

										A__       :     Optional[int]								=			defaultdict(snake_case_							)  # stores the list of persons for each task

										# final_mask is used to check if all persons are included by setting all bits
										# to 1
										A__       :     Optional[int]								=			(1 << len(snake_case_							)) - 1





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if mask == self.final_mask:
																	return 1

										# if not everyone gets the task and no more tasks are available, return 0
										if task_no > self.total_tasks:
																	return 0

										# if case already considered
										if self.dp[mask][task_no] != -1:
																	return self.dp[mask][task_no]

										# Number of ways when we don't this task in the arrangement
										A__       :     Optional[int]								=			self.count_ways_until(snake_case_   ,      task_no + 1							)

										# now assign the tasks one by one to all possible persons and recursively
										# assign for the remaining tasks.
										if task_no in self.task:
																	for p in self.task[task_no]:
																								# if p is already given a task
																								if mask & (1 << p):
																															continue

																								# assign this task to p and change the mask value. And recursively
																								# assign tasks with the new mask value.
																								total_ways_util += self.count_ways_until(mask | (1 << p)   ,      task_no + 1							)

        # save the value.
										A__       :     List[str]								=			total_ways_util

										return self.dp[mask][task_no]





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for i in range(len(snake_case_							)							):
																	for j in task_performed[i]:
																								self.task[j].append(snake_case_							)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
										return self.count_ways_until(0   ,      1							)


if __name__ == "__main__":
				_UpperCamelCase								=		5  # total no of tasks (the value of N)

				# the list of tasks that can be done by M persons.
				_UpperCamelCase								=		[[1, 3, 4], [1, 2, 5], [3, 4]]
				print(
				    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
				        task_performed
				    )
				)





