

"""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        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'config.json'



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


         '''simple docstring'''





         pass

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'config.json'



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


         '''simple docstring'''





         pass

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'config.json'



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


         '''simple docstring'''





         pass

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'config.json'



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


         '''simple docstring'''





         pass

class        _UpperCamelCase	(						unittest.TestCase	):


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: Optional[int]      )	->			List[Any]:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: str      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Dict        =     SampleObject()
         __magic_name__			:		int        =     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
         __magic_name__			:		Dict        =     SampleObject(_name_or_path='''lalala'''      )
         __magic_name__			:		Dict        =     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
         __magic_name__			:		Optional[int]        =     SampleObject(c=6      )
         __magic_name__			:		Dict        =     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.
         __magic_name__			:		List[Any]        =     SampleObject(1       ,       c=6      )
         __magic_name__			:		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 							_UpperCAmelCase      ( self					: Optional[int]      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Any        =     SampleObject()
         __magic_name__			:		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(snake_case      )
                __magic_name__			:		Optional[int]        =     SampleObject.from_config(SampleObject.load_config(snake_case      )      )
                __magic_name__			:		Union[str, Any]        =     new_obj.config

         # unfreeze configs
         __magic_name__			:		Optional[Any]        =     dict(snake_case      )
         __magic_name__			:		int        =     dict(snake_case      )

         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					: Dict      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     logging.get_logger('''diffusers.configuration_utils'''      )
         # 30 for warning
         logger.setLevel(30      )

         with CaptureLogger(snake_case      ) as cap_logger:
                __magic_name__			:		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 							_UpperCAmelCase      ( self					: str      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Any        =     logging.get_logger('''diffusers.configuration_utils'''      )
         # 30 for warning
         logger.setLevel(30      )

         with CaptureLogger(snake_case      ) as cap_logger:
                __magic_name__			:		int        =     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					: Union[str, Any]      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     logging.get_logger('''diffusers.configuration_utils'''      )
         # 30 for warning
         logger.setLevel(30      )

         with CaptureLogger(snake_case      ) as cap_logger:
                __magic_name__			:		Union[str, Any]        =     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					: List[str]      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		List[str]        =     logging.get_logger('''diffusers.configuration_utils'''      )
         # 30 for warning
         logger.setLevel(30      )

         with CaptureLogger(snake_case      ) as cap_logger:
                __magic_name__			:		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 							_UpperCAmelCase      ( self					: List[Any]      )	->			str:


         '''simple docstring'''





         __magic_name__			:		List[Any]        =     logging.get_logger('''diffusers.configuration_utils'''      )
         # 30 for warning
         logger.setLevel(30      )

         with CaptureLogger(snake_case      ) as cap_logger:
                __magic_name__			:		List[str]        =     DDPMScheduler.from_pretrained(
                    '''hf-internal-testing/tiny-stable-diffusion-torch'''       ,       subfolder='''scheduler'''       ,       prediction_type='''sample'''       ,       beta_end=8       ,       )

         with CaptureLogger(snake_case      ) as cap_logger_a:
                __magic_name__			:		int        =     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 							_UpperCAmelCase      ( self					: Tuple      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     logging.get_logger('''diffusers.configuration_utils'''      )
         # 30 for warning
         logger.setLevel(30      )

         with CaptureLogger(snake_case      ) as cap_logger:
                __magic_name__			:		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 							_UpperCAmelCase      ( self					: List[str]      )	->			int:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     SampleObject()

         __magic_name__			:		Optional[int]        =     {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(snake_case      )

                # now loading it with SampleObject2 should put f into `_use_default_values`
                __magic_name__			:		Any        =     SampleObjecta.from_config(snake_case      )

                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`
         __magic_name__			:		List[str]        =     SampleObjecta.from_config(config.config      )
         assert new_config.f == [5, 4]

         config.config._use_default_values.pop()
         __magic_name__			:		List[Any]        =     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]



"""simple docstring"""
import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					List[str]   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		int        =     TaConfig.from_json_file(lowerCamelCase   )
       print(f"""Building PyTorch model from configuration: {config}"""   )
       __magic_name__			:		Union[str, Any]        =     TaForConditionalGeneration(lowerCamelCase   )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
       model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
 A									=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 A									=						parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
