
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_    ):
     UpperCAmelCase__							:				List[Any] 	=						"config.json"





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







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[str] 	=						"config.json"





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







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				Tuple 	=						"config.json"





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







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				Union[str, Any] 	=						"config.json"





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







class 	lowerCamelCase						(    unittest.TestCase    ):





     def 						UpperCAmelCase(self :       List[str]  )		->		Tuple:
         with self.assertRaises(_A  ):
             ConfigMixin.load_config("dummy_path"  )





     def 						UpperCAmelCase(self :       int  )		->		int:
         snake_case						=   SampleObject()
         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
         snake_case						=   SampleObject(_name_or_path="lalala"  )
         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
         snake_case						=   SampleObject(c=6  )
         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.
         snake_case						=   SampleObject(1							,       c=6  )
         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 						UpperCAmelCase(self :       Optional[Any]  )		->		Union[str, Any]:
         snake_case						=   SampleObject()
         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  )
             snake_case						=   SampleObject.from_config(SampleObject.load_config(_A  )  )
             snake_case						=   new_obj.config

         # unfreeze configs
         snake_case						=   dict(_A  )
         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 						UpperCAmelCase(self :       Any  )		->		Optional[int]:
         snake_case						=   logging.get_logger("diffusers.configuration_utils"  )
         # 30 for warning
         logger.setLevel(3_0  )

         with CaptureLogger(_A  ) as cap_logger:
             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 						UpperCAmelCase(self :       int  )		->		Dict:
         snake_case						=   logging.get_logger("diffusers.configuration_utils"  )
         # 30 for warning
         logger.setLevel(3_0  )

         with CaptureLogger(_A  ) as cap_logger:
             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 						UpperCAmelCase(self :       List[Any]  )		->		int:
         snake_case						=   logging.get_logger("diffusers.configuration_utils"  )
         # 30 for warning
         logger.setLevel(3_0  )

         with CaptureLogger(_A  ) as cap_logger:
             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 						UpperCAmelCase(self :       int  )		->		Tuple:
         snake_case						=   logging.get_logger("diffusers.configuration_utils"  )
         # 30 for warning
         logger.setLevel(3_0  )

         with CaptureLogger(_A  ) as cap_logger:
             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 						UpperCAmelCase(self :       Optional[int]  )		->		Dict:
         snake_case						=   logging.get_logger("diffusers.configuration_utils"  )
         # 30 for warning
         logger.setLevel(3_0  )

         with CaptureLogger(_A  ) as cap_logger:
             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:
             snake_case						=   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 						UpperCAmelCase(self :       int  )		->		Any:
         snake_case						=   logging.get_logger("diffusers.configuration_utils"  )
         # 30 for warning
         logger.setLevel(3_0  )

         with CaptureLogger(_A  ) as cap_logger:
             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 						UpperCAmelCase(self :       List[str]  )		->		Any:
         # 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],

         snake_case						=   SampleObject()

         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`
             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`
         snake_case						=   SampleObjecta.from_config(config.config  )
         assert new_config.f == [5, 4]

         config.config._use_default_values.pop()
         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]



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A       =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					["SplinterTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					[
       "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SplinterForQuestionAnswering",
       "SplinterForPreTraining",
       "SplinterLayer",
       "SplinterModel",
       "SplinterPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_splinter_fast import SplinterTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_splinter import (
          SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SplinterForPreTraining,
          SplinterForQuestionAnswering,
          SplinterLayer,
          SplinterModel,
          SplinterPreTrainedModel,
      )


else:
   import sys

   _A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

