






'''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_				   =   logging.get_logger(__name__)

def    __lowercase		(     __lowercase			)			->		str:

      '''simple docstring'''


      _A     	=      torch.load(lowerCamelCase_  ,    map_location="cpu"			)
      if "model" in sd.keys():
            _A     	=      torch.load(lowerCamelCase_  ,    map_location="cpu"			)["model"]

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

      _A     	=      {
          "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:
                  _A     	=      sd.pop(lowerCamelCase_			)

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

                  _A     	=      key.replace(".qkv_proj."  ,    ".q_proj."			)
                  _A     	=      key.replace(".qkv_proj."  ,    ".k_proj."			)
                  _A     	=      key.replace(".qkv_proj."  ,    ".v_proj."			)

                  _A     	=      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
                  _A     	=      torch.split(lowerCamelCase_  ,    depth // 3  ,    dim=0			)

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

      return sd

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

      '''simple docstring'''


      _A     	=      load_checkpoint(lowerCamelCase_			)

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

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

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


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










"""simple docstring"""







from __future__ import annotations

A		=						'#'

class       _a  :



			def __init__(			self							:				List[Any]		)							->					None:
									snake_case   :			dict						    = {}



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				str		)							->					None:
									snake_case   :			Optional[int]						    = self._trie
									for char in text:
															if char not in trie:
																					snake_case   :			str						    = {}
															snake_case   :			Optional[Any]						    = trie[char]
									snake_case   :			str						    = True



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				str		)							->					tuple | list:
									snake_case   :			Dict						    = self._trie
									for char in prefix:
															if char in trie:
																					snake_case   :			Tuple						    = trie[char]
															else:
																					return []
									return self._elements(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				dict		)							->					tuple:
									snake_case   :			int						    = []
									for c, v in d.items():
															snake_case   :			int						    = [" "] if c == END else [(c + s) for s in self._elements(_lowercase		)]
															result.extend(_lowercase		)
									return tuple(_lowercase		)



A		=						Trie()
A		=						('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
			trie.insert_word(word)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = trie.find_word(lowerCamelCase_ )
						return tuple(string + word for word in suffixes )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						print(autocomplete_using_trie("de" ) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



