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       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Any 			=       'config.json'

 @register_to_config
 def __init__(	self				:						Any				,	lowerCamelCase				:						Optional[int]=2				,	lowerCamelCase				:						Tuple=5				,	lowerCamelCase				:						Optional[Any]=(2, 5)				,	lowerCamelCase				:						int="for diffusion"				,	lowerCamelCase				:						Tuple=[1, 3]				,	)       ->							str:
        pass







class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Tuple 			=       'config.json'

 @register_to_config
 def __init__(	self				:						str				,	lowerCamelCase				:						Any=2				,	lowerCamelCase				:						Dict=5				,	lowerCamelCase				:						Union[str, Any]=(2, 5)				,	lowerCamelCase				:						List[str]="for diffusion"				,	lowerCamelCase				:						Any=[1, 3]				,	)       ->							List[str]:
        pass







class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Any 			=       'config.json'

 @register_to_config
 def __init__(	self				:						Optional[Any]				,	lowerCamelCase				:						Any=2				,	lowerCamelCase				:						str=5				,	lowerCamelCase				:						int=(2, 5)				,	lowerCamelCase				:						Dict="for diffusion"				,	lowerCamelCase				:						str=[1, 3]				,	lowerCamelCase				:						str=[1, 3]				,	)       ->							List[str]:
        pass







class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		List[Any] 			=       'config.json'

 @register_to_config
 def __init__(	self				:						Optional[Any]				,	lowerCamelCase				:						List[str]=2				,	lowerCamelCase				:						Optional[int]=5				,	lowerCamelCase				:						int=(2, 5)				,	lowerCamelCase				:						Any="for diffusion"				,	lowerCamelCase				:						Tuple=[1, 5]				,	lowerCamelCase				:						Optional[int]=[5, 4]				,	)       ->							int:
        pass







class       __lowerCAmelCase     (				unittest.TestCase  ):



 '''simple docstring'''

 def     __snake_case    (	self				:						str     )       ->							int:
        with self.assertRaises(lowerCamelCase     ):
               ConfigMixin.load_config('dummy_path'     )

 def     __snake_case    (	self				:						Union[str, Any]     )       ->							int:
        _UpperCamelCase   :				str       =     SampleObject()
        _UpperCamelCase   :				Tuple       =     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
        _UpperCamelCase   :				List[Any]       =     SampleObject(_name_or_path='lalala'     )
        _UpperCamelCase   :				Optional[Any]       =     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
        _UpperCamelCase   :				Union[str, Any]       =     SampleObject(c=6     )
        _UpperCamelCase   :				List[Any]       =     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.
        _UpperCamelCase   :				Optional[int]       =     SampleObject(1				,	c=6     )
        _UpperCamelCase   :				Optional[Any]       =     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     __snake_case    (	self				:						Any     )       ->							Any:
        _UpperCamelCase   :				List[str]       =     SampleObject()
        _UpperCamelCase   :				Union[str, Any]       =     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(lowerCamelCase     )
               _UpperCamelCase   :				List[Any]       =     SampleObject.from_config(SampleObject.load_config(lowerCamelCase     )     )
               _UpperCamelCase   :				List[str]       =     new_obj.config

        # unfreeze configs
        _UpperCamelCase   :				Optional[int]       =     dict(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     dict(lowerCamelCase     )

        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     __snake_case    (	self				:						Dict     )       ->							str:
        _UpperCamelCase   :				Optional[Any]       =     logging.get_logger('diffusers.configuration_utils'     )
        # 30 for warning
        logger.setLevel(3_0     )

        with CaptureLogger(lowerCamelCase     ) as cap_logger:
               _UpperCamelCase   :				int       =     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     __snake_case    (	self				:						List[Any]     )       ->							int:
        _UpperCamelCase   :				Any       =     logging.get_logger('diffusers.configuration_utils'     )
        # 30 for warning
        logger.setLevel(3_0     )

        with CaptureLogger(lowerCamelCase     ) as cap_logger:
               _UpperCamelCase   :				List[str]       =     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     __snake_case    (	self				:						List[Any]     )       ->							int:
        _UpperCamelCase   :				str       =     logging.get_logger('diffusers.configuration_utils'     )
        # 30 for warning
        logger.setLevel(3_0     )

        with CaptureLogger(lowerCamelCase     ) as cap_logger:
               _UpperCamelCase   :				Tuple       =     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     __snake_case    (	self				:						Optional[Any]     )       ->							Any:
        _UpperCamelCase   :				Tuple       =     logging.get_logger('diffusers.configuration_utils'     )
        # 30 for warning
        logger.setLevel(3_0     )

        with CaptureLogger(lowerCamelCase     ) as cap_logger:
               _UpperCamelCase   :				Optional[int]       =     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     __snake_case    (	self				:						str     )       ->							List[str]:
        _UpperCamelCase   :				str       =     logging.get_logger('diffusers.configuration_utils'     )
        # 30 for warning
        logger.setLevel(3_0     )

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

        with CaptureLogger(lowerCamelCase     ) as cap_logger_a:
               _UpperCamelCase   :				Optional[Any]       =     DDPMScheduler.from_pretrained('google/ddpm-celebahq-256'				,	beta_start=8_8     )

        assert ddpm.__class__ == DDPMScheduler
        assert ddpm.config.prediction_type == "sample"
        assert ddpm.config.beta_end == 8
        assert ddpm_a.config.beta_start == 8_8

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

 def     __snake_case    (	self				:						Optional[Any]     )       ->							Optional[Any]:
        _UpperCamelCase   :				Dict       =     logging.get_logger('diffusers.configuration_utils'     )
        # 30 for warning
        logger.setLevel(3_0     )

        with CaptureLogger(lowerCamelCase     ) as cap_logger:
               _UpperCamelCase   :				Any       =     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     __snake_case    (	self				:						Optional[int]     )       ->							Dict:
        # let's first save a config that should be in the form
        #    a=2,
        #    b=5,
        #    c=(2, 5),
        #    d="for diffusion",
        #    e=[1, 3],

        _UpperCamelCase   :				Any       =     SampleObject()

        _UpperCamelCase   :				Any       =     {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(lowerCamelCase     )

               # now loading it with SampleObject2 should put f into `_use_default_values`
               _UpperCamelCase   :				Dict       =     SampleObjecta.from_config(lowerCamelCase     )

               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`
        _UpperCamelCase   :				Optional[int]       =     SampleObjecta.from_config(config.config     )
        assert new_config.f == [5, 4]

        config.config._use_default_values.pop()
        _UpperCamelCase   :				Optional[int]       =     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


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)



