


'''simple docstring'''






import argparse
from pathlib import Path

import torch

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


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


def  _snake_case       (					A	)						->   int:
							lowerCAmelCase__	       =     torch.load(A			,   map_location='''cpu'''	)
							if "model" in sd.keys():
														lowerCAmelCase__	       =     torch.load(A			,   map_location='''cpu'''	)['''model''']

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

							lowerCAmelCase__	       =     {
							    '''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__	       =     sd.pop(A	)

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

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

																					lowerCAmelCase__	       =     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__	       =     torch.split(A			,   depth // 3			,   dim=0	)

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

							return sd


@torch.no_grad()
def  _snake_case       (					A			,   A			,   A=None	)						->   Optional[int]:
							lowerCAmelCase__	       =     load_checkpoint(A	)

							if config is not None:
														lowerCAmelCase__	       =     OPTConfig.from_pretrained(A	)
							else:
														lowerCAmelCase__	       =     OPTConfig()

							lowerCAmelCase__	       =     OPTModel(A	).half().eval()
							model.load_state_dict(A	)

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


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



'''simple docstring'''






def  _snake_case       (					A = 10	)						->   str:
							if not isinstance(A			,   A	) or n < 0:
														raise ValueError('''Invalid input'''	)
							lowerCAmelCase__	       =     10**n
							lowerCAmelCase__	       =     28433 * (pow(2			,   7830457			,   A	)) + 1
							return str(number % modulus	)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print(f"""{solution(10) = }""")