

"""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	:


  """simple docstring"""





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


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		List[Any]        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		str        =     self.model_class(**snake_case      )
         __magic_name__			:		List[str]        =     model.init(inputs_dict['''prng_key''']       ,       inputs_dict['''sample''']      )
         jax.lax.stop_gradient(snake_case      )

         __magic_name__			:		List[str]        =     model.apply(snake_case       ,       inputs_dict['''sample''']      )

         if isinstance(snake_case       ,       snake_case      ):
                __magic_name__			:		Dict        =     output.sample

         self.assertIsNotNone(snake_case      )
         __magic_name__			:		Optional[Any]        =     inputs_dict['''sample'''].shape
         self.assertEqual(output.shape       ,       snake_case       ,       '''Input and output shapes do not match'''      )



  def 							_UpperCAmelCase      ( self					: Union[str, Any]      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__			:		Dict        =     self.prepare_init_args_and_inputs_for_common()

         __magic_name__			:		Tuple        =     16
         __magic_name__			:		Dict        =     (16, 32)

         __magic_name__			:		Any        =     self.model_class(**snake_case      )
         __magic_name__			:		Tuple        =     model.init(inputs_dict['''prng_key''']       ,       inputs_dict['''sample''']      )
         jax.lax.stop_gradient(snake_case      )

         __magic_name__			:		int        =     model.apply(snake_case       ,       inputs_dict['''sample''']      )

         if isinstance(snake_case       ,       snake_case      ):
                __magic_name__			:		Optional[int]        =     output.sample

         self.assertIsNotNone(snake_case      )
         __magic_name__			:		int        =     inputs_dict['''sample'''].shape
         self.assertEqual(output.shape       ,       snake_case       ,       '''Input and output shapes do not match'''      )




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


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     '''kwargs''' in inspect.signature(self.model_class.__init__      ).parameters
         __magic_name__			:		Optional[Any]        =     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>]`'''      )



"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=						logging.get_logger(__name__)

A									=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'prophetnet'
  snake_case_        =    ['past_key_values']
  snake_case_        =    {
      'num_attention_heads': 'num_encoder_attention_heads',
  }



  def __init__( self					: Union[str, Any]       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[Union[str, Callable]] = "gelu"       ,       snake_case					: Optional[int] = 3_0522       ,       snake_case					: Optional[int] = 1024       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[int] = 512       ,       snake_case					: Optional[float] = 0.02       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 2       ,       snake_case					: Optional[int] = 32       ,       snake_case					: Optional[int] = 128       ,       snake_case					: Optional[bool] = False       ,       snake_case					: Optional[float] = 0.0       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 1       ,       snake_case					: Optional[int] = 2       ,       **snake_case					: Optional[int]       ,       )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     vocab_size
         __magic_name__			:		Tuple        =     hidden_size
         __magic_name__			:		str        =     encoder_ffn_dim
         __magic_name__			:		Tuple        =     num_encoder_layers
         __magic_name__			:		int        =     num_encoder_attention_heads
         __magic_name__			:		int        =     decoder_ffn_dim
         __magic_name__			:		Optional[int]        =     num_decoder_layers
         __magic_name__			:		str        =     num_decoder_attention_heads
         __magic_name__			:		Optional[Any]        =     max_position_embeddings
         __magic_name__			:		Union[str, Any]        =     init_std  # Normal(0, this parameter)
         __magic_name__			:		Tuple        =     activation_function

         # parameters for prophetnet
         __magic_name__			:		int        =     ngram
         __magic_name__			:		str        =     num_buckets
         __magic_name__			:		Any        =     relative_max_distance
         __magic_name__			:		Optional[int]        =     disable_ngram_loss
         __magic_name__			:		str        =     eps

         # 3 Types of Dropout
         __magic_name__			:		Dict        =     attention_dropout
         __magic_name__			:		Optional[int]        =     activation_dropout
         __magic_name__			:		List[Any]        =     dropout

         __magic_name__			:		str        =     use_cache

         super().__init__(
             pad_token_id=snake_case       ,       bos_token_id=snake_case       ,       eos_token_id=snake_case       ,       is_encoder_decoder=snake_case       ,       add_cross_attention=snake_case       ,       decoder_start_token_id=snake_case       ,       **snake_case       ,       )



  @property
  def 							_UpperCAmelCase      ( self					: str      )	->			int:


         '''simple docstring'''





         return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: int      )	->			Optional[int]:


         '''simple docstring'''





         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''      )
