code: 
  
  
  
  '''simple docstring'''
  
  
  
  import inspect
  
  from diffusers.utils import is_flax_available
  from diffusers.utils.testing_utils import require_flax
  
  
  if is_flax_available():
   import jax
  @require_flax
  class        UpperCAmelCase__  :
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[Any]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_init_args_and_inputs_for_common()
  
    __UpperCamelCase				      =  self.model_class(**lowercase			)
    __UpperCamelCase				      =  model.init(inputs_dict["""prng_key"""]		,	inputs_dict["""sample"""]			)
    jax.lax.stop_gradient(lowercase			)
  
    __UpperCamelCase				      =  model.apply(lowercase		,	inputs_dict["""sample"""]			)
  
    if isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  output.sample
  
    self.assertIsNotNone(lowercase			)
    __UpperCamelCase				      =  inputs_dict["""sample"""].shape
    self.assertEqual(output.shape		,	lowercase		,	"""Input and output shapes do not match"""			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Tuple:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_init_args_and_inputs_for_common()
  
    __UpperCamelCase				      =  1_6
    __UpperCamelCase				      =  (1_6, 3_2)
  
    __UpperCamelCase				      =  self.model_class(**lowercase			)
    __UpperCamelCase				      =  model.init(inputs_dict["""prng_key"""]		,	inputs_dict["""sample"""]			)
    jax.lax.stop_gradient(lowercase			)
  
    __UpperCamelCase				      =  model.apply(lowercase		,	inputs_dict["""sample"""]			)
  
    if isinstance(lowercase		,	lowercase			):
     __UpperCamelCase				      =  output.sample
  
    self.assertIsNotNone(lowercase			)
    __UpperCamelCase				      =  inputs_dict["""sample"""].shape
    self.assertEqual(output.shape		,	lowercase		,	"""Input and output shapes do not match"""			)
  
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  """kwargs""" in inspect.signature(self.model_class.__init__			).parameters
    __UpperCamelCase				      =  len(self.model_class._deprecated_kwargs			) > 0
  
    if has_kwarg_in_model_class and not has_deprecated_kwarg:
     raise ValueError(
         f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
         """ under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"""
         """ no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="""
         """ [<deprecated_argument>]`"""			)
  
    if not has_kwarg_in_model_class and has_deprecated_kwarg:
     raise ValueError(
         f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
         """ under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"""
         f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
         """ from `_deprecated_kwargs = [<deprecated_argument>]`"""			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  