


from typing import Mapping

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


lowerCAmelCase__       :	int     				=logging.get_logger(__name__)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""mt5"""
							_UpperCAmelCase     =		["""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__       ,		)
										SCREAMING_SNAKE_CASE_						:			Dict       						= vocab_size
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= d_model
										SCREAMING_SNAKE_CASE_						:			Any       						= d_kv
										SCREAMING_SNAKE_CASE_						:			Any       						= d_ff
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= num_layers
										SCREAMING_SNAKE_CASE_						:			str       						= (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										SCREAMING_SNAKE_CASE_						:			List[str]       						= num_heads
										SCREAMING_SNAKE_CASE_						:			Any       						= relative_attention_num_buckets
										SCREAMING_SNAKE_CASE_						:			str       						= relative_attention_max_distance
										SCREAMING_SNAKE_CASE_						:			List[str]       						= dropout_rate
										SCREAMING_SNAKE_CASE_						:			Any       						= layer_norm_epsilon
										SCREAMING_SNAKE_CASE_						:			Dict       						= initializer_factor
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= feed_forward_proj
										SCREAMING_SNAKE_CASE_						:			str       						= use_cache

										SCREAMING_SNAKE_CASE_						:			Tuple       						= self.feed_forward_proj.split('-'    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= act_info[-1]
										SCREAMING_SNAKE_CASE_						:			Dict       						= 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":
													SCREAMING_SNAKE_CASE_						:			List[str]       						= 'gelu_new'






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return self.d_model






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return self.num_heads






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return self.num_layers







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







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





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= {
										    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
										    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
										}
										if self.use_past:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= 'past_encoder_sequence + sequence'
													SCREAMING_SNAKE_CASE_						:			str       						= {0: 'batch'}
													SCREAMING_SNAKE_CASE_						:			str       						= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
										else:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= {0: 'batch', 1: 'decoder_sequence'}
													SCREAMING_SNAKE_CASE_						:			List[str]       						= {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    ):





										"""simple docstring"""




										return 1_3






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return 5E-4









from __future__ import annotations






def 		a__       (							A__							):
			if len(A__							) == 0:
						raise ValueError('find_max() arg is an empty sequence'							)
			SCREAMING_SNAKE_CASE_						:			str       						= nums[0]
			for x in nums:
						if x > max_num:
									SCREAMING_SNAKE_CASE_						:			Optional[int]       						= x
			return max_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)





