




'''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 	lowerCamelCase_				:




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case			,       _snake_case      			=			self.prepare_init_args_and_inputs_for_common()

							_snake_case      			=			self.model_class(**_lowerCamelCase					)
							_snake_case      			=			model.init(inputs_dict["prng_key"],    inputs_dict["sample"]					)
							jax.lax.stop_gradient(_lowerCamelCase					)

							_snake_case      			=			model.apply(_lowerCamelCase,    inputs_dict["sample"]					)

							if isinstance(_lowerCamelCase,    _lowerCamelCase					):
									_snake_case      			=			output.sample

							self.assertIsNotNone(_lowerCamelCase					)
							_snake_case      			=			inputs_dict["sample"].shape
							self.assertEqual(output.shape,    _lowerCamelCase,    "Input and output shapes do not match"					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case			,       _snake_case      			=			self.prepare_init_args_and_inputs_for_common()

							_snake_case      			=			16
							_snake_case      			=			(16, 32)

							_snake_case      			=			self.model_class(**_lowerCamelCase					)
							_snake_case      			=			model.init(inputs_dict["prng_key"],    inputs_dict["sample"]					)
							jax.lax.stop_gradient(_lowerCamelCase					)

							_snake_case      			=			model.apply(_lowerCamelCase,    inputs_dict["sample"]					)

							if isinstance(_lowerCamelCase,    _lowerCamelCase					):
									_snake_case      			=			output.sample

							self.assertIsNotNone(_lowerCamelCase					)
							_snake_case      			=			inputs_dict["sample"].shape
							self.assertEqual(output.shape,    _lowerCamelCase,    "Input and output shapes do not match"					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			"kwargs" in inspect.signature(self.model_class.__init__					).parameters
							_snake_case      			=			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 List, Union

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


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"led"
   A_		:      Any							=					{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }


   def __init__(      self					:	Dict,			_lowerCamelCase					:	str=5_02_65,			_lowerCamelCase					:	List[Any]=1_63_84,			_lowerCamelCase					:	int=10_24,			_lowerCamelCase					:	List[Any]=12,			_lowerCamelCase					:	int=40_96,			_lowerCamelCase					:	str=16,			_lowerCamelCase					:	Dict=12,			_lowerCamelCase					:	Optional[int]=40_96,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	int=0.0,			_lowerCamelCase					:	str=0.0,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Tuple="gelu",			_lowerCamelCase					:	Tuple=10_24,			_lowerCamelCase					:	Dict=0.1,			_lowerCamelCase					:	Tuple=0.0,			_lowerCamelCase					:	Union[str, Any]=0.0,			_lowerCamelCase					:	Optional[int]=0.02,			_lowerCamelCase					:	List[str]=2,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Dict=1,			_lowerCamelCase					:	str=0,			_lowerCamelCase					:	Union[str, Any]=2,			_lowerCamelCase					:	Union[List[int], int] = 5_12,			**_lowerCamelCase					:	Any,			):

     '''simple docstring'''
     __A		     =					vocab_size
     __A		     =					max_encoder_position_embeddings
     __A		     =					max_decoder_position_embeddings
     __A		     =					d_model
     __A		     =					encoder_ffn_dim
     __A		     =					encoder_layers
     __A		     =					encoder_attention_heads
     __A		     =					decoder_ffn_dim
     __A		     =					decoder_layers
     __A		     =					decoder_attention_heads
     __A		     =					dropout
     __A		     =					attention_dropout
     __A		     =					activation_dropout
     __A		     =					activation_function
     __A		     =					init_std
     __A		     =					encoder_layerdrop
     __A		     =					decoder_layerdrop
     __A		     =					classifier_dropout
     __A		     =					use_cache
     __A		     =					encoder_layers
     __A		     =					attention_window

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




