





import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_a     :    List[str]         =						logging.get_logger(__name__)
def 							a_	(    __magic_name__   )				->			List[Any]:




				"""simple docstring"""






				snake_case   :					Optional[int]    						=				torch.load(_UpperCamelCase		,     map_location='''cpu'''   )
				if "model" in sd.keys():
								snake_case   :					Optional[int]    						=				torch.load(_UpperCamelCase		,     map_location='''cpu'''   )['''model''']

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

				snake_case   :					Optional[Any]    						=				{
				    '''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:
												snake_case   :					Optional[int]    						=				sd.pop(_UpperCamelCase   )

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

												snake_case   :					int    						=				key.replace('''.qkv_proj.'''		,     '''.q_proj.'''   )
												snake_case   :					Optional[Any]    						=				key.replace('''.qkv_proj.'''		,     '''.k_proj.'''   )
												snake_case   :					List[Any]    						=				key.replace('''.qkv_proj.'''		,     '''.v_proj.'''   )

												snake_case   :					Union[str, Any]    						=				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
												snake_case						,						snake_case						,						snake_case   :					Dict    						=				torch.split(_UpperCamelCase		,     depth // 3		,     dim=0   )

												snake_case   :					Tuple    						=				q
												snake_case   :					Tuple    						=				k
												snake_case   :					int    						=				v
												del sd[key]

				return sd
@torch.no_grad()
def 							a_	(    __magic_name__		,     __magic_name__		,     __magic_name__=None   )				->			Tuple:




				"""simple docstring"""






				snake_case   :					str    						=				load_checkpoint(_UpperCamelCase   )

				if config is not None:
								snake_case   :					int    						=				OPTConfig.from_pretrained(_UpperCamelCase   )
				else:
								snake_case   :					Dict    						=				OPTConfig()

				snake_case   :					Any    						=				OPTModel(_UpperCamelCase   ).half().eval()
				model.load_state_dict(_UpperCamelCase   )

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


if __name__ == "__main__":
			_a     :    List[Any]         =						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.')
			_a     :    Any         =						parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)




from collections import Counter
from timeit import timeit


def 		lowercase_	(  _UpperCamelCase = ""							,      ):


			'''simple docstring'''







			return sum(c % 2 for c in Counter(input_str.replace(''' '''							,      ''''''	).lower()	).values()	) < 2


def 		lowercase_	(  _UpperCamelCase = ""	):


			'''simple docstring'''







			if len(_UpperCamelCase	) == 0:
						return True
			__lowercase			       = input_str.replace(''' '''							,      ''''''	).lower()
			# character_freq_dict: Stores the frequency of every character in the input string
			__lowercase			       = {}

			for character in lower_case_input_str:
						__lowercase			       = character_freq_dict.get(_UpperCamelCase							,      0	) + 1
			__lowercase			       = 0

			for character_count in character_freq_dict.values():
						if character_count % 2:
									odd_char += 1
			if odd_char > 1:
						return False
			return True


def 		lowercase_	(  _UpperCamelCase = ""	):


			'''simple docstring'''







			print('''\nFor string = '''							,      _UpperCamelCase							,      ''':'''	)
			print(
			    '''> can_string_be_rearranged_as_palindrome_counter()'''							,      '''\tans ='''							,      can_string_be_rearranged_as_palindrome_counter(_UpperCamelCase	)							,      '''\ttime ='''							,      timeit(
			        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''							,      setup='''import __main__ as z'''							,      )							,      '''seconds'''							,      )
			print(
			    '''> can_string_be_rearranged_as_palindrome()'''							,      '''\tans ='''							,      can_string_be_rearranged_as_palindrome(_UpperCamelCase	)							,      '''\ttime ='''							,      timeit(
			        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''							,      setup='''import __main__ as z'''							,      )							,      '''seconds'''							,      )


if __name__ == "__main__":
	a			:			int												=					input(
	    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
	).strip()
	benchmark(check_str)
	a			:			Tuple												=					can_string_be_rearranged_as_palindrome_counter(check_str)
	print(f'''{check_str} can {'' if status else 'not '}be rearranged as a palindrome''')


