






from typing import Mapping

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


A_						:						str						      =					logging.get_logger(__name__)

A_						:						int						      =					{
    '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 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						int										='''umt5'''
       a							:						Optional[Any]										=['''past_key_values''']





       def __init__(   self    ,     _lowerCamelCase=2_5_0_1_1_2    ,     _lowerCamelCase=5_1_2    ,     _lowerCamelCase=6_4    ,     _lowerCamelCase=1_0_2_4    ,     _lowerCamelCase=8    ,     _lowerCamelCase=None    ,     _lowerCamelCase=6    ,     _lowerCamelCase=3_2    ,     _lowerCamelCase=1_2_8    ,     _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    ,     ):
         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    ,     )
         UpperCamelCase_:   str        =							vocab_size
         UpperCamelCase_:   Any        =							d_model
         UpperCamelCase_:   Any        =							d_kv
         UpperCamelCase_:   Optional[Any]        =							d_ff
         UpperCamelCase_:   str        =							num_layers
         UpperCamelCase_:   Tuple        =							(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCamelCase_:   Optional[Any]        =							num_heads
         UpperCamelCase_:   List[str]        =							relative_attention_num_buckets
         UpperCamelCase_:   Union[str, Any]        =							relative_attention_max_distance
         UpperCamelCase_:   List[str]        =							dropout_rate
         UpperCamelCase_:   str        =							layer_norm_epsilon
         UpperCamelCase_:   Dict        =							initializer_factor
         UpperCamelCase_:   Optional[int]        =							feed_forward_proj
         UpperCamelCase_:   List[Any]        =							use_cache

         UpperCamelCase_:   Dict        =							self.feed_forward_proj.split('-'				)
         UpperCamelCase_:   List[str]        =							act_info[-1]
         UpperCamelCase_:   str        =							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":
           UpperCamelCase_:   int        =							'gelu_new'





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





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





       @property
       def      _a       (   self				):
         return self.num_layers







class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							{
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
           UpperCamelCase_:   Tuple        =							'past_encoder_sequence + sequence'
           UpperCamelCase_:   Any        =							{0: 'batch'}
           UpperCamelCase_:   Optional[int]        =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
           UpperCamelCase_:   Tuple        =							{0: 'batch', 1: 'decoder_sequence'}
           UpperCamelCase_:   Any        =							{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      _a       (   self				):
         return 1_3





       @property
       def      _a       (   self				):
         return 5e-4



'''simple docstring'''






from collections.abc import Callable

import numpy as np


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       np.array:
							lowerCAmelCase__			  =      int(np.ceil((x_end - xa) / step_size			)			)
							lowerCAmelCase__			  =      np.zeros((n + 1,)			)
							lowerCAmelCase__			  =      ya
							lowerCAmelCase__			  =      xa

							for k in range(A			):
														lowerCAmelCase__			  =      y[k] + step_size * ode_func(A					,       y[k]			)
														lowerCAmelCase__			  =      y[k] + (
														    (step_size / 2) * (ode_func(A					,       y[k]			) + ode_func(x + step_size					,       A			))
														)
														x += step_size

							return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()