



import argparse
from pathlib import Path

import torch

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


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






def      _A				(UpperCamelCase		:      Tuple )  ->Optional[int]:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		torch.load(UpperCamelCase      ,			map_location="""cpu""" )
			if "model" in sd.keys():
						lowerCamelCase__ :      Dict								=		torch.load(UpperCamelCase      ,			map_location="""cpu""" )["""model"""]

			# pop unnecessary weights
			lowerCamelCase__ :      Optional[int]								=		[
			    """decoder.version""",
			    """decoder.output_projection.weight""",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(UpperCamelCase )

			lowerCamelCase__ :      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:
									lowerCamelCase__ :      List[Any]								=		sd.pop(UpperCamelCase )

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

									lowerCamelCase__ :      Any								=		key.replace(""".qkv_proj."""      ,			""".q_proj.""" )
									lowerCamelCase__ :      int								=		key.replace(""".qkv_proj."""      ,			""".k_proj.""" )
									lowerCamelCase__ :      Tuple								=		key.replace(""".qkv_proj."""      ,			""".v_proj.""" )

									lowerCamelCase__ :      List[str]								=		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
									lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Dict								=		torch.split(UpperCamelCase      ,			depth // 3      ,			dim=0 )

									lowerCamelCase__ :      Dict								=		q
									lowerCamelCase__ :      Any								=		k
									lowerCamelCase__ :      int								=		v
									del sd[key]

			return sd






@torch.no_grad()
def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      Union[str, Any]=None )  ->Any:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		load_checkpoint(UpperCamelCase )

			if config is not None:
						lowerCamelCase__ :      Union[str, Any]								=		OPTConfig.from_pretrained(UpperCamelCase )
			else:
						lowerCamelCase__ :      Any								=		OPTConfig()

			lowerCamelCase__ :      Dict								=		OPTModel(UpperCamelCase ).half().eval()
			model.load_state_dict(UpperCamelCase )

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


if __name__ == "__main__":
				_lowercase     							=						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.''')
				_lowercase     							=						parser.parse_args()
				convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








from __future__ import annotations






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      list[str] | None = None )  ->list[list[str]]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		word_bank or []
			# create a table
			lowerCamelCase__ :      int								=		len(UpperCamelCase ) + 1

			lowerCamelCase__ :      list[list[list[str]]]								=		[]
			for _ in range(UpperCamelCase ):
						table.append([] )
			# seed value
			lowerCamelCase__ :      Tuple								=		[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(UpperCamelCase ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(UpperCamelCase )] == word:
															lowerCamelCase__ :      list[list[str]]								=		[
															    [word, *way] for way in table[i]
															]
															# adds the word to every combination the current position holds
															# now,push that combination to the table[i+len(word)]
															table[i + len(UpperCamelCase )] += new_combinations

    # combinations are in reverse order so reverse for better output
			for combination in table[len(UpperCamelCase )]:
						combination.reverse()

			return table[len(UpperCamelCase )]


if __name__ == "__main__":
				print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
				print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
				print(
				    all_construct(
				        '''hexagonosaurus''',
				        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
				    )
				)



