


"""simple docstring"""






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property




class 				A_	(    unittest.TestCase      ):


  @cached_property
  def 				_lowercase (       self:		List[str]  ):







     '''simple docstring'''







     return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"  )


  @cached_property
  def 				_lowercase (       self:		Union[str, Any]  ):







     '''simple docstring'''







     return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"  )


  def 				_lowercase (       self:		List[str]  ):







     '''simple docstring'''







     _lowerCamelCase					:							str				=			self.tokenizer_ab
     _lowerCamelCase					:							Union[str, Any]				=			' I am a small frog.'
     _lowerCamelCase					:							Optional[int]				=			tok([src_text]				,padding=__lowerCAmelCase				,truncation=__lowerCAmelCase  )['input_ids']
     _lowerCamelCase					:							int				=			tok.batch_decode(__lowerCAmelCase				,skip_special_tokens=__lowerCAmelCase				,clean_up_tokenization_spaces=__lowerCAmelCase  )[0]
     assert src_text == decoded


  def 				_lowercase (       self:		Tuple  ):







     '''simple docstring'''







     _lowerCamelCase					:							Dict				=			self.rust_tokenizer_ab
     _lowerCamelCase					:							Union[str, Any]				=			' I am a small frog.'
     _lowerCamelCase					:							Dict				=			tok([src_text]				,padding=__lowerCAmelCase				,truncation=__lowerCAmelCase  )['input_ids']
     _lowerCamelCase					:							List[str]				=			tok.batch_decode(__lowerCAmelCase				,skip_special_tokens=__lowerCAmelCase				,clean_up_tokenization_spaces=__lowerCAmelCase  )[0]
     assert src_text == decoded


  def 				_lowercase (       self:		Dict  ):







     '''simple docstring'''







     assert self.tokenizer_ab.add_prefix_space
     assert self.tokenizer_ab([" Sam", "Sam"]  ).input_ids == [[5_502, 2], [5_502, 2]]






  def 				_lowercase (       self:		Union[str, Any]  ):







     '''simple docstring'''







     assert self.rust_tokenizer_ab.add_prefix_space
     assert self.rust_tokenizer_ab([" Sam", "Sam"]  ).input_ids == [[5_502, 2], [5_502, 2]]
import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



