





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 			__UpperCamelCase				(			lowercase__	:       Optional[Any]		,    lowercase__	:       Tuple		)	->      List[str]:
    '''simple docstring'''





    if text.startswith(UpperCAmelCase_		):
        return text[len(UpperCAmelCase_		) :]
    return text  # or whatever
def 			__UpperCamelCase				(			lowercase__	:       Union[str, Any]		)	->      Optional[int]:
    '''simple docstring'''





    return {remove_prefix(UpperCAmelCase_		,    """model."""		): v for k, v in sd.items()}
def 			__UpperCamelCase				(			lowercase__	:       List[str]		)	->      Any:
    '''simple docstring'''





    lowerCAmelCase_					:	Optional[int]						=   {}
    for k in state_dicts[0].keys():
        lowerCAmelCase_					:	Tuple						=   [sd[k] for sd in state_dicts]
        lowerCAmelCase_					:	Union[str, Any]						=   sum(UpperCAmelCase_		) / len(UpperCAmelCase_		)
        assert isinstance(UpperCAmelCase_		,    torch.Tensor		)
        lowerCAmelCase_					:	Union[str, Any]						=   new_t
    return new_sd
def 			__UpperCamelCase				(			lowercase__	:       int		,    lowercase__	:       List[str]		,    lowercase__	:       str		)	->      None:
    '''simple docstring'''





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

    if len(UpperCAmelCase_		) > 1:
        logger.info(f'averaging the weights of {ckpt_files}'		)

    lowerCAmelCase_					:	List[Any]						=   [sanitize(torch.load(UpperCAmelCase_		,    map_location="""cpu"""		)["""state_dict"""]		) for x in ckpt_files]
    lowerCAmelCase_					:	Optional[Any]						=   average_state_dicts(UpperCAmelCase_		)

    lowerCAmelCase_					:	str						=   hf_model.load_state_dict(UpperCAmelCase_		,    strict=UpperCAmelCase_		)
    assert not missing, f'missing keys: {missing}'
    hf_model.save_pretrained(UpperCAmelCase_		)
    try:
        lowerCAmelCase_					:	Union[str, Any]						=   AutoTokenizer.from_pretrained(UpperCAmelCase_		)
        tok.save_pretrained(UpperCAmelCase_		)
    except Exception:
        pass
        # dont copy tokenizer if cant


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









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


