



"""simple docstring"""




import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCAmelCase__		:					Any							=logging.get_logger(__name__)






def      __lowercase       (			a__       )    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= torch.load(SCREAMING_SNAKE_CASE_							,       map_location='cpu'       )
							if "model" in sd.keys():
														__SCREAMING_SNAKE_CASE									= torch.load(SCREAMING_SNAKE_CASE_							,       map_location='cpu'       )['model']

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

							__SCREAMING_SNAKE_CASE									= {
							    '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:
																					__SCREAMING_SNAKE_CASE									= sd.pop(SCREAMING_SNAKE_CASE_       )

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

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

																					__SCREAMING_SNAKE_CASE									= 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
																					__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= torch.split(SCREAMING_SNAKE_CASE_							,       depth // 3							,       dim=0       )

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

							return sd






@torch.no_grad()
def      __lowercase       (			a__							,       a__							,       a__=None       )    ->			List[Any]:
							__SCREAMING_SNAKE_CASE									= load_checkpoint(SCREAMING_SNAKE_CASE_       )

							if config is not None:
														__SCREAMING_SNAKE_CASE									= OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
							else:
														__SCREAMING_SNAKE_CASE									= OPTConfig()

							__SCREAMING_SNAKE_CASE									= OPTModel(SCREAMING_SNAKE_CASE_       ).half().eval()
							model.load_state_dict(SCREAMING_SNAKE_CASE_       )

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


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











from collections import deque

from .hash_table import HashTable

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= deque([]		) if self.values[key] is None else self.values[key]
													self.values[key].appendleft(_A		)
													__SCREAMING_SNAKE_CASE									= self.values[key]






						def 	_A     (   self		):







													'''simple docstring'''

													return (
													    sum(self.charge_factor - len(_A		) for slot in self.values		)
													    / self.size_table
													    * self.charge_factor
													)






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if not (
													    len(self.values[key]		) == self.charge_factor and self.values.count(_A		) == 0
													):
																				return key
													return super()._collision_resolution(_A				,							_A		)






