




print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))






'''simple docstring'''



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowercase	:						Dict       =	logging.get_logger(__name__)

__lowercase	:						Optional[Any]       =	{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "umt5"
	A_				=    ["past_key_values"]




	def __init__(   self						,				__a=25_0112						,				__a=512						,				__a=64						,				__a=1024						,				__a=8						,				__a=None						,				__a=6						,				__a=32						,				__a=128						,				__a=0.1						,				__a=1E-6						,				__a=1.0						,				__a="gated-gelu"						,				__a=True						,				__a=True						,				__a="T5Tokenizer"						,				__a=True						,				__a=0						,				__a=1						,				__a=0						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    is_encoder_decoder=__a						,				tokenizer_class=__a						,				tie_word_embeddings=__a						,				pad_token_id=__a						,				eos_token_id=__a						,				decoder_start_token_id=__a						,				**__a						,				)
						__a			:       Any					    =  vocab_size
						__a			:       Any					    =  d_model
						__a			:       str					    =  d_kv
						__a			:       Dict					    =  d_ff
						__a			:       Union[str, Any]					    =  num_layers
						__a			:       int					    =  (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						__a			:       Optional[int]					    =  num_heads
						__a			:       Tuple					    =  relative_attention_num_buckets
						__a			:       Optional[Any]					    =  relative_attention_max_distance
						__a			:       Optional[int]					    =  dropout_rate
						__a			:       List[Any]					    =  layer_norm_epsilon
						__a			:       int					    =  initializer_factor
						__a			:       Union[str, Any]					    =  feed_forward_proj
						__a			:       Any					    =  use_cache

						__a			:       List[Any]					    =  self.feed_forward_proj.split('-'							)
						__a			:       Dict					    =  act_info[-1]
						__a			:       Dict					    =  act_info[0] == 'gated'

						if len(__a							) > 1 and act_info[0] != "gated" or len(__a							) > 2:
											raise ValueError(
											    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''							)

						if feed_forward_proj == "gated-gelu":
											__a			:       Optional[int]					    =  'gelu_new'




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.d_model




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_heads





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return self.num_layers





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       str					    =  {
						    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
						    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
						}
						if self.use_past:
											__a			:       Dict					    =  'past_encoder_sequence + sequence'
											__a			:       Tuple					    =  {0: 'batch'}
											__a			:       Tuple					    =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
											__a			:       List[Any]					    =  {0: 'batch', 1: 'decoder_sequence'}
											__a			:       int					    =  {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
											self.fill_with_past_key_values_(__a						,				direction='inputs'							)

						return common_inputs




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 13





	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 5E-4



