





'''simple docstring'''



from typing import Mapping

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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Optional[Any]     =    "umt5"
	a      :			str     =    ["past_key_values"]


	def __init__(self       ,_lowerCamelCase=250112       ,_lowerCamelCase=512       ,_lowerCamelCase=64       ,_lowerCamelCase=1024       ,_lowerCamelCase=8       ,_lowerCamelCase=None       ,_lowerCamelCase=6       ,_lowerCamelCase=32       ,_lowerCamelCase=128       ,_lowerCamelCase=0.1       ,_lowerCamelCase=1E-6       ,_lowerCamelCase=1.0       ,_lowerCamelCase="gated-gelu"       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase="T5Tokenizer"       ,_lowerCamelCase=True       ,_lowerCamelCase=0       ,_lowerCamelCase=1       ,_lowerCamelCase=0       ,**_lowerCamelCase       ,)							->							str:







				'''simple docstring'''





				super().__init__(
				    is_encoder_decoder=_lowerCamelCase       ,tokenizer_class=_lowerCamelCase       ,tie_word_embeddings=_lowerCamelCase       ,pad_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,decoder_start_token_id=_lowerCamelCase       ,**_lowerCamelCase       ,)
				__lowercase			  =							vocab_size
				__lowercase			  =							d_model
				__lowercase			  =							d_kv
				__lowercase			  =							d_ff
				__lowercase			  =							num_layers
				__lowercase			  =							(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				__lowercase			  =							num_heads
				__lowercase			  =							relative_attention_num_buckets
				__lowercase			  =							relative_attention_max_distance
				__lowercase			  =							dropout_rate
				__lowercase			  =							layer_norm_epsilon
				__lowercase			  =							initializer_factor
				__lowercase			  =							feed_forward_proj
				__lowercase			  =							use_cache

				__lowercase			  =							self.feed_forward_proj.split('''-'''	)
				__lowercase			  =							act_info[-1]
				__lowercase			  =							act_info[0] == '''gated'''

				if len(_lowerCamelCase	) > 1 and act_info[0] != "gated" or len(_lowerCamelCase	) > 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":
							__lowercase			  =							'''gelu_new'''


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







				'''simple docstring'''





				return self.d_model


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				return self.num_heads


	@property
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				return self.num_layers






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def   _UpperCAmelCase       (self	)							->							Mapping[str, Mapping[int, str]]:







				'''simple docstring'''





				__lowercase			  =							{
				    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
				    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
				}
				if self.use_past:
							__lowercase			  =							'''past_encoder_sequence + sequence'''
							__lowercase			  =							{0: '''batch'''}
							__lowercase			  =							{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
				else:
							__lowercase			  =							{0: '''batch''', 1: '''decoder_sequence'''}
							__lowercase			  =							{0: '''batch''', 1: '''decoder_sequence'''}

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

				return common_inputs


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







				'''simple docstring'''





				return 13


	@property
	def   _UpperCAmelCase       (self	)							->							float:







				'''simple docstring'''





				return 5E-4












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					int = 6_0_0_8_5_1_4_7_5_1_4_3			):

			try:
						__lowercase			  =							int(lowerCamelCase_			)
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.'''			)
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.'''			)
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						while n % i == 0:
									__lowercase			  =							i
									n //= i
						i += 1
			if n > 1:
						__lowercase			  =							n
			return int(lowerCamelCase_			)


if __name__ == "__main__":
	print(f'''{solution() = }''')





