





from typing import Mapping

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


_a									=				logging.get_logger(__name__)






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""mt5"""
			lowerCAmelCase_												=						["""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=False	,  __lowerCAmelCase=0	,  __lowerCAmelCase=1	,  __lowerCAmelCase=0	,  **__lowerCAmelCase	,  ):



				'''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	,  )
				lowerCamelCase__        =		vocab_size
				lowerCamelCase__        =		d_model
				lowerCamelCase__        =		d_kv
				lowerCamelCase__        =		d_ff
				lowerCamelCase__        =		num_layers
				lowerCamelCase__        =		(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				lowerCamelCase__        =		num_heads
				lowerCamelCase__        =		relative_attention_num_buckets
				lowerCamelCase__        =		relative_attention_max_distance
				lowerCamelCase__        =		dropout_rate
				lowerCamelCase__        =		layer_norm_epsilon
				lowerCamelCase__        =		initializer_factor
				lowerCamelCase__        =		feed_forward_proj
				lowerCamelCase__        =		use_cache

				lowerCamelCase__        =		self.feed_forward_proj.split('''-'''							)
				lowerCamelCase__        =		act_info[-1]
				lowerCamelCase__        =		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\''''							)

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
					lowerCamelCase__        =		'''gelu_new'''





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return self.d_model





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return self.num_heads





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return self.num_layers






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







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



				'''simple docstring'''







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



				'''simple docstring'''







				return 1_3





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return 5E-4










from __future__ import annotations

import math
import random





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	lowerCamelCase__        =		d * e - 1
	lowerCamelCase__        =		0
	lowerCamelCase__        =		0
	while p == 0:
		lowerCamelCase__        =		random.randint(2	,n - 1							)
		lowerCamelCase__        =		k
		while True:
			if t % 2 == 0:
				lowerCamelCase__        =		t // 2
				lowerCamelCase__        =		(g**t) % n
				lowerCamelCase__        =		math.gcd(x - 1	,__snake_case							)
				if x > 1 and y > 1:
					lowerCamelCase__        =		y
					lowerCamelCase__        =		n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



