



'''simple docstring'''


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:       Tuple      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Union[str, Any]      	=	torch.device("cpu")







def 				__lowerCamelCase				(   )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase					=  Image.open(requests.get(__snake_case    ,						stream=__snake_case    ).raw    )
   return im







def 				__lowerCamelCase				(   A__    )       ->	Any:






   """simple docstring"""





   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]    )

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]    )

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]    )

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]    )







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  dct.pop(__snake_case    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   A__    )       ->	List[Any]:






   """simple docstring"""
   UpperCamelCase					=  []
   for k in state_dict.keys():
      UpperCamelCase					=  k
      if ".pwconv" in k:
         UpperCamelCase					=  k_new.replace('.pwconv'    ,						'.point_wise_conv'    )
      if ".dwconv" in k:
         UpperCamelCase					=  k_new.replace('.dwconv'    ,						'.depth_wise_conv'    )
      if ".Proj." in k:
         UpperCamelCase					=  k_new.replace('.Proj.'    ,						'.proj.'    )
      if "patch_embed" in k_new:
         UpperCamelCase					=  k_new.replace('patch_embed'    ,						'swiftformer.patch_embed.patch_embedding'    )
      if "network" in k_new:
         UpperCamelCase					=  k_new.split('.'    )
         if ls[2].isdigit():
            UpperCamelCase					=  'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]    )
         else:
            UpperCamelCase					=  k_new.replace('network'    ,						'swiftformer.encoder.network'    )
      rename_keys.append((k, k_new)    )
   return rename_keys







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCamelCase					=  1_000
   UpperCamelCase					=  'huggingface/label-files'
   UpperCamelCase					=  'imagenet-1k-id2label.json'
   UpperCamelCase					=  json.load(open(hf_hub_download(__snake_case    ,						__snake_case    ,						repo_type='dataset'    )    ,						'r'    )    )
   UpperCamelCase					=  {int(__snake_case    ): v for k, v in idalabel.items()}
   UpperCamelCase					=  idalabel
   UpperCamelCase					=  {v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      UpperCamelCase					=  [3, 3, 6, 4]
      UpperCamelCase					=  [48, 56, 112, 220]

   elif swiftformer_name == "swiftformer_s":
      UpperCamelCase					=  [3, 3, 9, 6]
      UpperCamelCase					=  [48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      UpperCamelCase					=  [4, 3, 10, 5]
      UpperCamelCase					=  [48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      UpperCamelCase					=  [4, 4, 12, 6]
      UpperCamelCase					=  [64, 128, 320, 512]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith('https'    ):
         UpperCamelCase					=  torch.hub.load_state_dict_from_url(__snake_case    ,						map_location='cpu'    ,						check_hash=__snake_case    )
      else:
         UpperCamelCase					=  torch.load(__snake_case    ,						map_location='cpu'    )
   UpperCamelCase					=  checkpoint

   UpperCamelCase					=  create_rename_keys(__snake_case    )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(__snake_case    ,						__snake_case    ,						__snake_case    )

   # load HuggingFace model
   UpperCamelCase					=  SwiftFormerForImageClassification(__snake_case    ).eval()
   hf_model.load_state_dict(__snake_case    )

   # prepare test inputs
   UpperCamelCase					=  prepare_img()
   UpperCamelCase					=  ViTImageProcessor.from_pretrained('preprocessor_config'    )
   UpperCamelCase					=  processor(images=__snake_case    ,						return_tensors='pt'    )

   # compare outputs from both models
   UpperCamelCase					=  get_expected_output(__snake_case    )
   UpperCamelCase					=  hf_model(inputs['pixel_values']    ).logits

   assert hf_logits.shape == torch.Size([1, 1_000]    )
   assert torch.allclose(hf_logits[0, 0:5]    ,						__snake_case    ,						atol=1e-3    )

   Path(__snake_case    ).mkdir(exist_ok=__snake_case    )
   print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""    )
   hf_model.save_pretrained(__snake_case    )


if __name__ == "__main__":
   _lowerCamelCase	:       Dict      	=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--swiftformer_name",
       default="swiftformer_xs",
       choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
       type=str,
       help="Name of the SwiftFormer model you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="./converted_outputs/",
       type=str,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

   _lowerCamelCase	:       List[str]      	=	parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



"""simple docstring"""





from collections import defaultdict






class     _UpperCAmelCase:



		def __init__(					self				,		__a				,		__a)       ->       Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  total  # total no of tasks (N)

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

							_UpperCamelCase							 =  defaultdict(__a)  # 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
							_UpperCamelCase							 =  (1 << len(__a)) - 1



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       Dict:






							'''simple docstring'''
							# if mask == self.finalmask all persons are distributed tasks, return 1
							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
							_UpperCamelCase							 =  self.count_ways_until(__a				,		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.
							_UpperCamelCase							 =  total_ways_util

							return self.dp[mask][task_no]




		def 						UpperCAmelCase							(					self				,		__a)       ->       int:






							'''simple docstring'''
							# Store the list of persons for each task
							for i in range(len(__a)):
												for j in task_performed[i]:
																	self.task[j].append(__a)

        # 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__":
				_a														=		5  # total no of tasks (the value of N)

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






