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


from typing import Mapping

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


lowercase  : List[str]						   =							logging.get_logger(__name__)

lowercase  : Tuple						   =							{
    """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 __snake_case		(				lowerCAmelCase					):
    _a		:						List[str]=    "umt5"
    _a		:						Optional[Any]=    ["past_key_values"]
    def __init__(  self		,snake_case=250112		,snake_case=512		,snake_case=64		,snake_case=1024		,snake_case=8		,snake_case=None		,snake_case=6		,snake_case=32		,snake_case=128		,snake_case=0.1		,snake_case=1e-6		,snake_case=1.0		,snake_case="gated-gelu"		,snake_case=True		,snake_case=True		,snake_case="T5Tokenizer"		,snake_case=True		,snake_case=0		,snake_case=1		,snake_case=0		,**snake_case		,):



           '''simple docstring'''







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

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

           if len(snake_case				) > 1 and act_info[0] != "gated" or len(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":
                  lowercase							:					int	     =				"""gelu_new"""
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.d_model
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_heads
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_layers






class __snake_case		(				lowerCAmelCase					):
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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

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

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



           '''simple docstring'''







           return 13
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 5e-4
