



"""simple docstring"""


from typing import Mapping

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


snake_case_								=			logging.get_logger(__name__)







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""mt5"""
						__UpperCamelCase													=				["""past_key_values"""]






						def __init__(   self					:Any  ,    lowercase_					:Optional[Any]=25_01_12  ,    lowercase_					:List[str]=5_12  ,    lowercase_					:Any=64  ,    lowercase_					:List[str]=10_24  ,    lowercase_					:int=8  ,    lowercase_					:int=None  ,    lowercase_					:int=6  ,    lowercase_					:Any=32  ,    lowercase_					:List[Any]=1_28  ,    lowercase_					:int=0.1  ,    lowercase_					:List[str]=1E-6  ,    lowercase_					:Optional[Any]=1.0  ,    lowercase_					:Union[str, Any]="gated-gelu"  ,    lowercase_					:Optional[int]=True  ,    lowercase_					:Tuple=True  ,    lowercase_					:Union[str, Any]="T5Tokenizer"  ,    lowercase_					:Tuple=False  ,    lowercase_					:str=0  ,    lowercase_					:Optional[Any]=1  ,    lowercase_					:Optional[int]=0  ,    **lowercase_					:Tuple  ,    )							->     List[str]:
												super().__init__(
												    is_encoder_decoder=lowercase_  ,    tokenizer_class=lowercase_  ,    tie_word_embeddings=lowercase_  ,    pad_token_id=lowercase_  ,    eos_token_id=lowercase_  ,    decoder_start_token_id=lowercase_  ,    **lowercase_  ,    )
												UpperCAmelCase			 =					vocab_size
												UpperCAmelCase			 =					d_model
												UpperCAmelCase			 =					d_kv
												UpperCAmelCase			 =					d_ff
												UpperCAmelCase			 =					num_layers
												UpperCAmelCase			 =					(
												    num_decoder_layers if num_decoder_layers is not None else self.num_layers
												)  # default = symmetry
												UpperCAmelCase			 =					num_heads
												UpperCAmelCase			 =					relative_attention_num_buckets
												UpperCAmelCase			 =					relative_attention_max_distance
												UpperCAmelCase			 =					dropout_rate
												UpperCAmelCase			 =					layer_norm_epsilon
												UpperCAmelCase			 =					initializer_factor
												UpperCAmelCase			 =					feed_forward_proj
												UpperCAmelCase			 =					use_cache

												UpperCAmelCase			 =					self.feed_forward_proj.split('-'   )
												UpperCAmelCase			 =					act_info[-1]
												UpperCAmelCase			 =					act_info[0] == 'gated'

												if len(lowercase_   ) > 1 and act_info[0] != "gated" or len(lowercase_   ) > 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":
																		UpperCAmelCase			 =					'gelu_new'






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[int]:
												return self.d_model






						@property
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[int]:
												return self.num_heads






						@property
						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Dict:
												return self.num_layers







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Mapping[str, Mapping[int, str]]:
												UpperCAmelCase			 =					{
												    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
												    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
												}
												if self.use_past:
																		UpperCAmelCase			 =					'past_encoder_sequence + sequence'
																		UpperCAmelCase			 =					{0: 'batch'}
																		UpperCAmelCase			 =					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
												else:
																		UpperCAmelCase			 =					{0: 'batch', 1: 'decoder_sequence'}
																		UpperCAmelCase			 =					{0: 'batch', 1: 'decoder_sequence'}

												if self.use_past:
																		self.fill_with_past_key_values_(lowercase_  ,    direction='inputs'   )

												return common_inputs






						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def       UpperCAmelCase__	(   self					:Any   )							->     int:
												return 13






						@property
						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     float:
												return 5E-4










'''simple docstring'''




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :     Union[str, Any]       =       logging.get_logger(__name__)

a  :     int       =       {
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class      UpperCamelCase_					(				__magic_name__     ):
	lowercase		=					'hubert'





	def __init__(    self						,						A=32						,						A=768						,						A=12						,						A=12						,						A=3072						,						A="gelu"						,						A=0.1						,						A=0.1						,						A=0.1						,						A=True						,						A=0.0						,						A=0.1						,						A=0.1						,						A=0.0_2						,						A=1e-5						,						A="group"						,						A="gelu"						,						A=(512, 512, 512, 512, 512, 512, 512)						,						A=(5, 2, 2, 2, 2, 2, 2)						,						A=(10, 3, 3, 3, 3, 2, 2)						,						A=False						,						A=128						,						A=16						,						A=False						,						A=True						,						A=0.0_5						,						A=10						,						A=2						,						A=0.0						,						A=10						,						A=0						,						A="sum"						,						A=False						,						A=False						,						A=256						,						A=0						,						A=1						,						A=2						,						**A						,						)     ->     List[str]:
								super().__init__(**A						,						pad_token_id=A						,						bos_token_id=A						,						eos_token_id=A       )
								UpperCAmelCase	:						List[Any]					=						hidden_size
								UpperCAmelCase	:						List[Any]					=						feat_extract_norm
								UpperCAmelCase	:						Optional[Any]					=						feat_extract_activation
								UpperCAmelCase	:						Optional[int]					=						list(A       )
								UpperCAmelCase	:						int					=						list(A       )
								UpperCAmelCase	:						Any					=						list(A       )
								UpperCAmelCase	:						Any					=						conv_bias
								UpperCAmelCase	:						Dict					=						num_conv_pos_embeddings
								UpperCAmelCase	:						Tuple					=						num_conv_pos_embedding_groups
								UpperCAmelCase	:						str					=						len(self.conv_dim       )
								UpperCAmelCase	:						Optional[Any]					=						num_hidden_layers
								UpperCAmelCase	:						int					=						intermediate_size
								UpperCAmelCase	:						Any					=						hidden_act
								UpperCAmelCase	:						Tuple					=						num_attention_heads
								UpperCAmelCase	:						int					=						hidden_dropout
								UpperCAmelCase	:						Any					=						attention_dropout
								UpperCAmelCase	:						Any					=						activation_dropout
								UpperCAmelCase	:						str					=						feat_proj_layer_norm
								UpperCAmelCase	:						int					=						feat_proj_dropout
								UpperCAmelCase	:						List[Any]					=						final_dropout
								UpperCAmelCase	:						int					=						layerdrop
								UpperCAmelCase	:						Dict					=						layer_norm_eps
								UpperCAmelCase	:						Union[str, Any]					=						initializer_range
								UpperCAmelCase	:						Any					=						vocab_size
								UpperCAmelCase	:						Optional[int]					=						do_stable_layer_norm
								UpperCAmelCase	:						Optional[int]					=						use_weighted_layer_sum
								UpperCAmelCase	:						Dict					=						classifier_proj_size

								if (
								    (len(self.conv_stride       ) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
								    or (len(self.conv_dim       ) != self.num_feat_extract_layers)
								):
															raise ValueError(
															    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
															    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
															    f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
															    f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								UpperCAmelCase	:						Union[str, Any]					=						apply_spec_augment
								UpperCAmelCase	:						Tuple					=						mask_time_prob
								UpperCAmelCase	:						Optional[Any]					=						mask_time_length
								UpperCAmelCase	:						List[Any]					=						mask_time_min_masks
								UpperCAmelCase	:						Any					=						mask_feature_prob
								UpperCAmelCase	:						Any					=						mask_feature_length
								UpperCAmelCase	:						Tuple					=						mask_feature_min_masks

								# ctc loss
								UpperCAmelCase	:						int					=						ctc_loss_reduction
								UpperCAmelCase	:						Any					=						ctc_zero_infinity






	@property
	def 					_lowercase(    self       )     ->     Union[str, Any]:
								return functools.reduce(operator.mul						,						self.conv_stride						,						1       )

