





'''simple docstring'''



from collections import defaultdict






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							total  # total no of tasks (N)

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

				__lowercase										=							defaultdict(UpperCAmelCase__ )  # 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
				__lowercase										=							(1 << len(UpperCAmelCase__ )) - 1


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''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
				__lowercase										=							self.count_ways_until(UpperCAmelCase__     ,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.
				__lowercase										=							total_ways_util

				return self.dp[mask][task_no]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





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

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

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












'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)
			if "model" in sd.keys():
						__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)['''model''']

			# pop unnecessary weights
			__lowercase										=							[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(lowerCamelCase_				)

			__lowercase										=							{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase										=							sd.pop(lowerCamelCase_				)

			__lowercase										=							list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase										=							sd[key]
									# We split QKV in separate Q,K,V

									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.q_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.k_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.v_proj.'''				)

									__lowercase										=							value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase						,		__lowercase						,		__lowercase										=							torch.split(lowerCamelCase_		,       depth // 3		,       dim=0				)

									__lowercase										=							q
									__lowercase										=							k
									__lowercase										=							v
									del sd[key]

			return sd




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]=None				):
			__lowercase										=							load_checkpoint(lowerCamelCase_				)

			if config is not None:
						__lowercase										=							OPTConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							OPTConfig()

			__lowercase										=							OPTModel(lowerCamelCase_				).half().eval()
			model.load_state_dict(lowerCamelCase_				)

			# Check results
			Path(lowerCamelCase_				).mkdir(exist_ok=lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





