
from typing import Mapping

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


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						str         =							"mt5"
							lowerCAmelCase__		:						Optional[int]         =							["past_key_values"]





							def __init__(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[str]=25_01_12					,			_UpperCAmelCase  :       Optional[int]=5_12					,			_UpperCAmelCase  :       Any=64					,			_UpperCAmelCase  :       str=10_24					,			_UpperCAmelCase  :       int=8					,			_UpperCAmelCase  :       Optional[Any]=None					,			_UpperCAmelCase  :       Optional[Any]=6					,			_UpperCAmelCase  :       List[Any]=32					,			_UpperCAmelCase  :       int=1_28					,			_UpperCAmelCase  :       List[str]=0.1					,			_UpperCAmelCase  :       Any=1e-6					,			_UpperCAmelCase  :       int=1.0					,			_UpperCAmelCase  :       Dict="gated-gelu"					,			_UpperCAmelCase  :       Any=True					,			_UpperCAmelCase  :       str=True					,			_UpperCAmelCase  :       Any="T5Tokenizer"					,			_UpperCAmelCase  :       Optional[int]=False					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       Optional[int]=0					,			**_UpperCAmelCase  :       Union[str, Any]					,			)						->		List[Any]:



											"""simple docstring"""


											super().__init__(
											    is_encoder_decoder=_UpperCAmelCase					,			tokenizer_class=_UpperCAmelCase					,			tie_word_embeddings=_UpperCAmelCase					,			pad_token_id=_UpperCAmelCase					,			eos_token_id=_UpperCAmelCase					,			decoder_start_token_id=_UpperCAmelCase					,			**_UpperCAmelCase					,			)
											__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(_UpperCAmelCase    ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase    ) > 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":
															__lowercase								= 'gelu_new'





							@property
							def       a__			(			self  :       Dict    )						->		Any:



											"""simple docstring"""


											return self.d_model





							@property
							def       a__			(			self  :       int    )						->		Union[str, Any]:



											"""simple docstring"""


											return self.num_heads





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



											"""simple docstring"""


											return self.num_layers






class      A__       (						lowerCAmelCase__    ):





							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def       a__			(			self  :       int    )						->		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_(_UpperCAmelCase					,			direction='inputs'    )

											return common_inputs





							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
							def       a__			(			self  :       List[str]    )						->		int:



											"""simple docstring"""


											return 13





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



											"""simple docstring"""


											return 5e-4








from math import pi




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float      )					->						float:
				return degree / (180 / pi)


if __name__ == "__main__":
							from doctest import testmod

							testmod()





