






"""simple docstring"""


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 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							Optional[int]			=     """mt5"""
					lowercase_						:							Optional[int]			=     ["""past_key_values"""]
					def __init__(       self       :  List[str]    ,   UpperCAmelCase       :  Optional[int]=25_0112    ,   UpperCAmelCase       :  Dict=512    ,   UpperCAmelCase       :  Tuple=64    ,   UpperCAmelCase       :  str=1024    ,   UpperCAmelCase       :  Union[str, Any]=8    ,   UpperCAmelCase       :  List[str]=None    ,   UpperCAmelCase       :  int=6    ,   UpperCAmelCase       :  Dict=32    ,   UpperCAmelCase       :  int=128    ,   UpperCAmelCase       :  str=0.1    ,   UpperCAmelCase       :  Tuple=1e-6    ,   UpperCAmelCase       :  Dict=1.0    ,   UpperCAmelCase       :  Dict="gated-gelu"    ,   UpperCAmelCase       :  Dict=True    ,   UpperCAmelCase       :  List[str]=True    ,   UpperCAmelCase       :  Dict="T5Tokenizer"    ,   UpperCAmelCase       :  Optional[Any]=False    ,   UpperCAmelCase       :  str=0    ,   UpperCAmelCase       :  List[str]=1    ,   UpperCAmelCase       :  Optional[Any]=0    ,   **UpperCAmelCase       :  Dict    ,   )  ->					str:
											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    ,   )
											lowerCAmelCase      :List[Any]				  = vocab_size
											lowerCAmelCase      :Optional[int]				  = d_model
											lowerCAmelCase      :str				  = d_kv
											lowerCAmelCase      :List[str]				  = d_ff
											lowerCAmelCase      :int				  = num_layers
											lowerCAmelCase      :List[str]				  = (
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											lowerCAmelCase      :List[Any]				  = num_heads
											lowerCAmelCase      :Dict				  = relative_attention_num_buckets
											lowerCAmelCase      :List[str]				  = relative_attention_max_distance
											lowerCAmelCase      :Optional[Any]				  = dropout_rate
											lowerCAmelCase      :Dict				  = layer_norm_epsilon
											lowerCAmelCase      :List[str]				  = initializer_factor
											lowerCAmelCase      :List[Any]				  = feed_forward_proj
											lowerCAmelCase      :Tuple				  = use_cache

											lowerCAmelCase      :Any				  = self.feed_forward_proj.split('-'      )
											lowerCAmelCase      :Tuple				  = act_info[-1]
											lowerCAmelCase      :str				  = 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":
																	lowerCAmelCase      :str				  = 'gelu_new'
					@property
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Dict:
											return self.d_model
					@property
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Optional[int]:
											return self.num_heads
					@property
					def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					str:
											return self.num_layers




class 	__UpperCamelCase    (    UpperCamelCase	):
					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Mapping[str, Mapping[int, str]]:
											lowerCAmelCase      :Optional[Any]				  = {
											    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
											    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
											}
											if self.use_past:
																	lowerCAmelCase      :Optional[int]				  = 'past_encoder_sequence + sequence'
																	lowerCAmelCase      :Optional[Any]				  = {0: 'batch'}
																	lowerCAmelCase      :Tuple				  = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																	lowerCAmelCase      :Tuple				  = {0: 'batch', 1: 'decoder_sequence'}
																	lowerCAmelCase      :List[Any]				  = {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 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					int:
											return 13
					@property
					def 					UpperCAmelCase__   (       self       :  str      )  ->					float:
											return 5e-4







"""simple docstring"""


import safs




class 	__UpperCamelCase    (    safs.SaFileSystem	):
					pass