





"""simple docstring"""
from typing import Mapping

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


_snake_case					:		int							=			logging.get_logger(__name__)






class  _UpperCAmelCase      (     lowercase_			):
					UpperCamelCase								=							'''mt5'''
					UpperCamelCase								=							['''past_key_values''']


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

												A		=  self.feed_forward_proj.split("-"		)
												A		=  act_info[-1]
												A		=  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":
																			A		=  "gelu_new"


					@property
					def      lowerCamelCase	( self      :Dict		):
												return self.d_model


					@property
					def      lowerCamelCase	( self      :Tuple		):
												return self.num_heads


					@property
					def      lowerCamelCase	( self      :Optional[Any]		):
												return self.num_layers







class  _UpperCAmelCase      (     lowercase_			):


					@property
					# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
					def      lowerCamelCase	( self      :Any		):
												A		=  {
												    "input_ids": {0: "batch", 1: "encoder_sequence"},
												    "attention_mask": {0: "batch", 1: "encoder_sequence"},
												}
												if self.use_past:
																			A		=  "past_encoder_sequence + sequence"
																			A		=  {0: "batch"}
																			A		=  {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
																			A		=  {0: "batch", 1: "decoder_sequence"}
																			A		=  {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      lowerCamelCase	( self      :str		):
												return 13


					@property
					def      lowerCamelCase	( self      :Dict		):
												return 5e-4









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Optional[int]							=			logging.get_logger(__name__)

_snake_case					:		List[Any]							=			{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tvlt'''


					def __init__( self      :Dict					,	__UpperCamelCase      :Tuple=2_24					,	__UpperCamelCase      :Dict=20_48					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Optional[int]=[16, 16]					,	__UpperCamelCase      :Any=[16, 16]					,	__UpperCamelCase      :List[str]=3					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=8					,	__UpperCamelCase      :str=7_68					,	__UpperCamelCase      :List[str]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :Union[str, Any]=30_72					,	__UpperCamelCase      :List[Any]="gelu"					,	__UpperCamelCase      :List[str]=0.0					,	__UpperCamelCase      :Union[str, Any]=0.0					,	__UpperCamelCase      :List[str]=0.02					,	__UpperCamelCase      :Dict=1e-6					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :str=16					,	__UpperCamelCase      :List[str]=5_12					,	__UpperCamelCase      :int=8					,	__UpperCamelCase      :int=20_48					,	__UpperCamelCase      :int=0.75					,	__UpperCamelCase      :Tuple=0.15					,	__UpperCamelCase      :Optional[Any]="frame-level"					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :str=True					,	__UpperCamelCase      :Tuple="classification"					,	**__UpperCamelCase      :Any					,	):
												super().__init__(**__UpperCamelCase		)

												if audio_mask_type not in ("frame-level", "patch_level"):
																			raise ValueError(
																			    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
																			    f"got {audio_mask_type}"		)

												A		=  image_size
												A		=  spectrogram_length
												A		=  frequency_length
												A		=  image_patch_size
												A		=  audio_patch_size
												A		=  num_image_channels
												A		=  num_audio_channels
												A		=  num_frames

												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps
												A		=  qkv_bias
												A		=  use_mean_pooling

												A		=  decoder_num_attention_heads
												A		=  decoder_hidden_size
												A		=  decoder_num_hidden_layers
												A		=  decoder_intermediate_size
												A		=  pixel_mask_ratio
												A		=  audio_mask_ratio
												A		=  audio_mask_type

												A		=  task_matching
												A		=  task_mae
												A		=  loss_type


