





'''simple docstring'''





import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		List[str]							=  """config.json"""



    @register_to_config
    def __init__(					self      ,							A_=2      ,							A_=5      ,							A_=(2, 5)      ,							A_="for diffusion"      ,							A_=[1, 3]      ,							):





        '''simple docstring'''

        pass





class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		Any							=  """config.json"""



    @register_to_config
    def __init__(					self      ,							A_=2      ,							A_=5      ,							A_=(2, 5)      ,							A_="for diffusion"      ,							A_=[1, 3]      ,							):





        '''simple docstring'''

        pass





class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		int							=  """config.json"""



    @register_to_config
    def __init__(					self      ,							A_=2      ,							A_=5      ,							A_=(2, 5)      ,							A_="for diffusion"      ,							A_=[1, 3]      ,							A_=[1, 3]      ,							):





        '''simple docstring'''

        pass





class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		List[Any]							=  """config.json"""



    @register_to_config
    def __init__(					self      ,							A_=2      ,							A_=5      ,							A_=(2, 5)      ,							A_="for diffusion"      ,							A_=[1, 5]      ,							A_=[5, 4]      ,							):





        '''simple docstring'''

        pass





class 				__snake_case  (     unittest.TestCase						):


    '''simple docstring'''







    def    lowercase_				(					self    ):





        '''simple docstring'''

        with self.assertRaises(A_    ):
            ConfigMixin.load_config('''dummy_path'''    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SampleObject()
        SCREAMING_SNAKE_CASE__       							= obj.config
        assert config["a"] == 2
        assert config["b"] == 5
        assert config["c"] == (2, 5)
        assert config["d"] == "for diffusion"
        assert config["e"] == [1, 3]

        # init ignore private arguments
        SCREAMING_SNAKE_CASE__       							= SampleObject(_name_or_path='''lalala'''    )
        SCREAMING_SNAKE_CASE__       							= obj.config
        assert config["a"] == 2
        assert config["b"] == 5
        assert config["c"] == (2, 5)
        assert config["d"] == "for diffusion"
        assert config["e"] == [1, 3]

        # can override default
        SCREAMING_SNAKE_CASE__       							= SampleObject(c=6    )
        SCREAMING_SNAKE_CASE__       							= obj.config
        assert config["a"] == 2
        assert config["b"] == 5
        assert config["c"] == 6
        assert config["d"] == "for diffusion"
        assert config["e"] == [1, 3]

        # can use positional arguments.
        SCREAMING_SNAKE_CASE__       							= SampleObject(1      ,							c=6    )
        SCREAMING_SNAKE_CASE__       							= obj.config
        assert config["a"] == 1
        assert config["b"] == 5
        assert config["c"] == 6
        assert config["d"] == "for diffusion"
        assert config["e"] == [1, 3]



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SampleObject()
        SCREAMING_SNAKE_CASE__       							= obj.config

        assert config["a"] == 2
        assert config["b"] == 5
        assert config["c"] == (2, 5)
        assert config["d"] == "for diffusion"
        assert config["e"] == [1, 3]

        with tempfile.TemporaryDirectory() as tmpdirname:
            obj.save_config(A_    )
            SCREAMING_SNAKE_CASE__       							= SampleObject.from_config(SampleObject.load_config(A_    )    )
            SCREAMING_SNAKE_CASE__       							= new_obj.config

        # unfreeze configs
        SCREAMING_SNAKE_CASE__       							= dict(A_    )
        SCREAMING_SNAKE_CASE__       							= dict(A_    )

        assert config.pop('''c'''    ) == (2, 5)  # instantiated as tuple
        assert new_config.pop('''c'''    ) == [2, 5]  # saved & loaded as list because of json
        config.pop('''_use_default_values'''    )
        assert config == new_config



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= logging.get_logger('''diffusers.configuration_utils'''    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(A_    ) as cap_logger:
            SCREAMING_SNAKE_CASE__       							= DDIMScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch'''      ,							subfolder='''scheduler'''    )

        assert ddim.__class__ == DDIMScheduler
        # no warning should be thrown
        assert cap_logger.out == ""



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= logging.get_logger('''diffusers.configuration_utils'''    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(A_    ) as cap_logger:
            SCREAMING_SNAKE_CASE__       							= EulerDiscreteScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch'''      ,							subfolder='''scheduler'''    )

        assert euler.__class__ == EulerDiscreteScheduler
        # no warning should be thrown
        assert cap_logger.out == ""



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= logging.get_logger('''diffusers.configuration_utils'''    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(A_    ) as cap_logger:
            SCREAMING_SNAKE_CASE__       							= EulerAncestralDiscreteScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch'''      ,							subfolder='''scheduler'''    )

        assert euler.__class__ == EulerAncestralDiscreteScheduler
        # no warning should be thrown
        assert cap_logger.out == ""



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= logging.get_logger('''diffusers.configuration_utils'''    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(A_    ) as cap_logger:
            SCREAMING_SNAKE_CASE__       							= PNDMScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch'''      ,							subfolder='''scheduler'''    )

        assert pndm.__class__ == PNDMScheduler
        # no warning should be thrown
        assert cap_logger.out == ""



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= logging.get_logger('''diffusers.configuration_utils'''    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(A_    ) as cap_logger:
            SCREAMING_SNAKE_CASE__       							= DDPMScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch'''      ,							subfolder='''scheduler'''      ,							prediction_type='''sample'''      ,							beta_end=8      ,							)

        with CaptureLogger(A_    ) as cap_logger_a:
            SCREAMING_SNAKE_CASE__       							= DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''      ,							beta_start=88    )

        assert ddpm.__class__ == DDPMScheduler
        assert ddpm.config.prediction_type == "sample"
        assert ddpm.config.beta_end == 8
        assert ddpm_a.config.beta_start == 88

        # no warning should be thrown
        assert cap_logger.out == ""
        assert cap_logger_a.out == ""



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= logging.get_logger('''diffusers.configuration_utils'''    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(A_    ) as cap_logger:
            SCREAMING_SNAKE_CASE__       							= DPMSolverMultistepScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch'''      ,							subfolder='''scheduler'''    )

        assert dpm.__class__ == DPMSolverMultistepScheduler
        # no warning should be thrown
        assert cap_logger.out == ""




    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SampleObject()

        SCREAMING_SNAKE_CASE__       							= {k: v for k, v in config.config.items() if not k.startswith('''_'''    )}

        # make sure that default config has all keys in `_use_default_values`
        assert set(config_dict.keys()    ) == set(config.config._use_default_values    )

        with tempfile.TemporaryDirectory() as tmpdirname:
            config.save_config(A_    )

            # now loading it with SampleObject2 should put f into `_use_default_values`
            SCREAMING_SNAKE_CASE__       							= SampleObjecta.from_config(A_    )

            assert "f" in config._use_default_values
            assert config.f == [1, 3]

        # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
        # **BECAUSE** it is part of `config._use_default_values`
        SCREAMING_SNAKE_CASE__       							= SampleObjecta.from_config(config.config    )
        assert new_config.f == [5, 4]

        config.config._use_default_values.pop()
        SCREAMING_SNAKE_CASE__       							= SampleObjecta.from_config(config.config    )
        assert new_config_a.f == [1, 3]

        # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
        assert new_config_a.e == [1, 3]







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


_A				:							Any							= get_logger(__name__)


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    if text.startswith(lowerCAmelCase_  ):
        return text[len(lowerCAmelCase_  ) :]
    return text  # or whatever


def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    return {remove_prefix(lowerCAmelCase_   ,       '''model.'''  ): v for k, v in sd.items()}


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k in state_dicts[0].keys():
        SCREAMING_SNAKE_CASE__       							= [sd[k] for sd in state_dicts]
        SCREAMING_SNAKE_CASE__       							= sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
        assert isinstance(lowerCAmelCase_   ,       torch.Tensor  )
        SCREAMING_SNAKE_CASE__       							= new_t
    return new_sd


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    SCREAMING_SNAKE_CASE__       							= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  )
    if os.path.isfile(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= [pl_ckpt_path]
    else:
        assert os.path.isdir(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= list(Path(lowerCAmelCase_  ).glob('''*.ckpt'''  )  )
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

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

    SCREAMING_SNAKE_CASE__       							= [sanitize(torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
    SCREAMING_SNAKE_CASE__       							= average_state_dicts(lowerCAmelCase_  )

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


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