



"""simple docstring"""







from typing import Mapping

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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """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      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     "umt5"
			A__	:					List[Any]							  =     ["past_key_values"]


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=250112						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=6						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=1e-6						,	SCREAMING_SNAKE_CASE__=1.0						,	SCREAMING_SNAKE_CASE__="gated-gelu"						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__="T5Tokenizer"						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Any:
				super().__init__(
				    is_encoder_decoder=SCREAMING_SNAKE_CASE__						,	tokenizer_class=SCREAMING_SNAKE_CASE__						,	tie_word_embeddings=SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	decoder_start_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__						,	)
				A__											=					vocab_size
				A__											=					d_model
				A__											=					d_kv
				A__											=					d_ff
				A__											=					num_layers
				A__											=					(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				A__											=					num_heads
				A__											=					relative_attention_num_buckets
				A__											=					relative_attention_max_distance
				A__											=					dropout_rate
				A__											=					layer_norm_epsilon
				A__											=					initializer_factor
				A__											=					feed_forward_proj
				A__											=					use_cache

				A__											=					self.feed_forward_proj.split("-"		)
				A__											=					act_info[-1]
				A__											=					act_info[0] == "gated"

				if len(SCREAMING_SNAKE_CASE__		) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE__		) > 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__											=					"gelu_new"


			@property
			def   snake_case__	(      self		)      ->	List[Any]:
				return self.d_model


			@property
			def   snake_case__	(      self		)      ->	Optional[int]:
				return self.num_heads







			@property
			def   snake_case__	(      self		)      ->	int:
				return self.num_layers






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def   snake_case__	(      self		)      ->	Mapping[str, Mapping[int, str]]:
				A__											=					{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
					A__											=					"past_encoder_sequence + sequence"
					A__											=					{0: "batch"}
					A__											=					{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
					A__											=					{0: "batch", 1: "decoder_sequence"}
					A__											=					{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
					self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__						,	direction="inputs"		)

				return common_inputs


			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def   snake_case__	(      self		)      ->	int:
				return 13







			@property
			def   snake_case__	(      self		)      ->	float:
				return 5e-4









"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       int     )	-> int:






	"""simple docstring"""





	assert (
	    isinstance(UpperCAmelCase_,	UpperCAmelCase_     ) and number_of_steps > 0
	), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
	if number_of_steps == 1:
		return 1
	A__							,							A__											=					1, 1
	for _ in range(number_of_steps - 1     ):
		A__							,							A__											=					current + previous, current
	return current


if __name__ == "__main__":
			import doctest

			doctest.testmod()




