



"""simple docstring"""






import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig






def 					snake_case__       (		__lowerCamelCase    :   List[Any]							,		__lowerCamelCase    :   Any							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =s.rsplit(__lowerCamelCase							,		__lowerCamelCase     )
			return new.join(__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   List[str]     ):
			"""simple docstring"""
			# encoder.embeddings are double copied in original FLAVA
			return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()     )






def 					snake_case__       (		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     ={}

			lowerCamelCase__		:		Optional[int]		     =['''group_1''', '''group_2''', '''group_3''', '''group_4''']
			for key, value in state_dict.items():
						for group_key in group_keys:
									if group_key in key:
												lowerCamelCase__		:		List[str]		     =key.replace(f'''{group_key}.'''							,		f'''{group_key}.group.'''     )

						if "res_path" in key:
									lowerCamelCase__		:		Dict		     =key.replace('''res_path.'''							,		'''res_path.path.'''     )

						if key.endswith('''.w'''     ):
									lowerCamelCase__		:		Tuple		     =rreplace(__lowerCamelCase							,		'''.w'''							,		'''.weight'''							,		1     )
						if key.endswith('''.b'''     ):
									lowerCamelCase__		:		Optional[Any]		     =rreplace(__lowerCamelCase							,		'''.b'''							,		'''.bias'''							,		1     )

						lowerCamelCase__		:		Union[str, Any]		     =value.float()

			return upgrade






@torch.no_grad()
def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   Union[str, Any]=None							,		__lowerCamelCase    :   List[Any]=True     ):
			"""simple docstring"""
			from dall_e import Encoder

			lowerCamelCase__		:		Any		     =Encoder()
			if os.path.exists(__lowerCamelCase     ):
						lowerCamelCase__		:		str		     =torch.load(__lowerCamelCase     )
			else:
						lowerCamelCase__		:		Optional[int]		     =torch.hub.load_state_dict_from_url(__lowerCamelCase     )

			if isinstance(__lowerCamelCase							,		__lowerCamelCase     ):
						lowerCamelCase__		:		Any		     =ckpt.state_dict()
			encoder.load_state_dict(__lowerCamelCase     )

			if config_path is not None:
						lowerCamelCase__		:		Tuple		     =FlavaImageCodebookConfig.from_pretrained(__lowerCamelCase     )
			else:
						lowerCamelCase__		:		List[Any]		     =FlavaImageCodebookConfig()

			lowerCamelCase__		:		Union[str, Any]		     =FlavaImageCodebook(__lowerCamelCase     ).eval()
			lowerCamelCase__		:		Tuple		     =encoder.state_dict()

			lowerCamelCase__		:		Dict		     =upgrade_state_dict(__lowerCamelCase     )
			hf_model.load_state_dict(__lowerCamelCase     )
			lowerCamelCase__		:		Optional[int]		     =hf_model.state_dict()
			lowerCamelCase__		:		Optional[int]		     =count_parameters(__lowerCamelCase     )
			lowerCamelCase__		:		Dict		     =count_parameters(__lowerCamelCase     )

			assert torch.allclose(__lowerCamelCase							,		__lowerCamelCase							,		atol=1e-3     )

			if save_checkpoint:
						hf_model.save_pretrained(__lowerCamelCase     )
			else:
						return hf_state_dict


if __name__ == "__main__":
		_lowercase			:			Dict										=						argparse.ArgumentParser()
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		_lowercase			:			Optional[int]										=						parser.parse_args()

		convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









"""simple docstring"""






from collections import defaultdict

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		List[Any]		     =total  # total no of tasks (N)

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

								lowerCamelCase__		:		Any		     =defaultdict(lowerCamelCase					)  # 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
								lowerCamelCase__		:		List[Any]		     =(1 << len(lowerCamelCase					)) - 1



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Any					)-> Any:
								# 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
								lowerCamelCase__		:		Optional[int]		     =self.count_ways_until(lowerCamelCase,  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.
								lowerCamelCase__		:		int		     =total_ways_util

								return self.dp[mask][task_no]




					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Dict					)-> int:
								# Store the list of persons for each task
								for i in range(len(lowerCamelCase					)					):
											for j in task_performed[i]:
														self.task[j].append(lowerCamelCase					)

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

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




