


'''simple docstring'''






import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class   __UpperCamelCase		(  a_							):
							lowercase	:						List[str]      						=     (VQDiffusionScheduler,)





							def 						a__      (     self						:Tuple     ,**_UpperCamelCase						:List[str]	):
									snake_case_						:							List[str]					=  {
									    'num_vec_classes': 4_0_9_7,
									    'num_train_timesteps': 1_0_0,
									}

									config.update(**_UpperCamelCase	)
									return config





							def 						a__      (     self						:List[str]     ,_UpperCamelCase						:List[str]	):
									snake_case_						:							str					=  4
									snake_case_						:							Any					=  8
									snake_case_						:							str					=  8

									snake_case_						:							Tuple					=  torch.randint(0     ,_UpperCamelCase     ,(batch_size, height * width)	)

									return sample





							@property
							def 						a__      (     self						:Tuple	):
									assert False





							def 						a__      (     self						:Tuple     ,_UpperCamelCase						:Optional[int]	):
									def model(_UpperCamelCase						:Optional[int]     ,_UpperCamelCase						:Optional[Any]     ,*_UpperCamelCase						:str	):
											snake_case_						:							Any					=  sample.shape
											snake_case_						:							List[str]					=  torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)	)
											snake_case_						:							Any					=  F.log_softmax(logits.double()     ,dim=1	).float()
											return return_value

									return model





							def 						a__      (     self						:int	):
									for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=_UpperCamelCase	)





							def 						a__      (     self						:Dict	):
									for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
											self.check_over_configs(num_vec_classes=_UpperCamelCase	)





							def 						a__      (     self						:Optional[Any]	):
									for t in [0, 5_0, 9_9]:
											self.check_over_forward(time_step=_UpperCamelCase	)





							def 						a__      (     self						:Dict	):
									pass
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)



