






from typing import Mapping

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


__UpperCAmelCase							:   List[Any]							=  logging.get_logger(__name__)

__UpperCAmelCase							:   Dict							=  {
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class 					_snake_case						(			_A ):
	_A          =   'umt5'
	_A          =   ['past_key_values']






	def __init__(		self					,UpperCamelCase=250_112					,UpperCamelCase=512					,UpperCamelCase=64					,UpperCamelCase=1_024					,UpperCamelCase=8					,UpperCamelCase=None					,UpperCamelCase=6					,UpperCamelCase=32					,UpperCamelCase=128					,UpperCamelCase=0.1					,UpperCamelCase=1E-6					,UpperCamelCase=1.0					,UpperCamelCase="gated-gelu"					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase="T5Tokenizer"					,UpperCamelCase=True					,UpperCamelCase=0					,UpperCamelCase=1					,UpperCamelCase=0					,**UpperCamelCase					,)						->    Optional[Any]:
					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					,)
					snake_case__      :str         =   vocab_size
					snake_case__      :Any         =   d_model
					snake_case__      :Optional[Any]         =   d_kv
					snake_case__      :Dict         =   d_ff
					snake_case__      :Dict         =   num_layers
					snake_case__      :Dict         =   (
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					snake_case__      :Union[str, Any]         =   num_heads
					snake_case__      :Optional[Any]         =   relative_attention_num_buckets
					snake_case__      :Any         =   relative_attention_max_distance
					snake_case__      :List[Any]         =   dropout_rate
					snake_case__      :Optional[int]         =   layer_norm_epsilon
					snake_case__      :Optional[int]         =   initializer_factor
					snake_case__      :Dict         =   feed_forward_proj
					snake_case__      :List[str]         =   use_cache

					snake_case__      :Any         =   self.feed_forward_proj.split("-"       )
					snake_case__      :Any         =   act_info[-1]
					snake_case__      :Optional[int]         =   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'"       )

					if feed_forward_proj == "gated-gelu":
									snake_case__      :Any         =   "gelu_new"






	@property
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					return self.d_model






	@property
	def    lowerCAmelCase_							(		self       )						->    int:
					return self.num_heads






	@property
	def    lowerCAmelCase_							(		self       )						->    str:
					return self.num_layers




class 					_snake_case						(			_A ):






	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def    lowerCAmelCase_							(		self       )						->    Mapping[str, Mapping[int, str]]:
					snake_case__      :List[str]         =   {
					    "input_ids": {0: "batch", 1: "encoder_sequence"},
					    "attention_mask": {0: "batch", 1: "encoder_sequence"},
					}
					if self.use_past:
									snake_case__      :Tuple         =   "past_encoder_sequence + sequence"
									snake_case__      :str         =   {0: "batch"}
									snake_case__      :Optional[Any]         =   {0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
									snake_case__      :List[str]         =   {0: "batch", 1: "decoder_sequence"}
									snake_case__      :Optional[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    lowerCAmelCase_							(		self       )						->    int:
					return 13






	@property
	def    lowerCAmelCase_							(		self       )						->    float:
					return 5E-4







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 					_snake_case						(			_A       ,    _A       ,    _A ):






	@register_to_config
	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = False					,)						->    int:
					super().__init__()

					snake_case__      :Union[str, Any]         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )

					snake_case__      :int         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Any         =   False

					snake_case__      :List[Any]         =   nn.Dropout(p=UpperCamelCase       )

					snake_case__      :Tuple         =   TaConfig(
					    vocab_size=UpperCamelCase					,d_model=UpperCamelCase					,num_heads=UpperCamelCase					,d_kv=UpperCamelCase					,d_ff=UpperCamelCase					,dropout_rate=UpperCamelCase					,feed_forward_proj=UpperCamelCase					,is_decoder=UpperCamelCase					,is_encoder_decoder=UpperCamelCase					,)

					snake_case__      :List[str]         =   nn.ModuleList()
					for lyr_num in range(UpperCamelCase       ):
									snake_case__      :List[Any]         =   TaBlock(UpperCamelCase       )
									self.encoders.append(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   TaLayerNorm(UpperCamelCase       )
					snake_case__      :Any         =   nn.Dropout(p=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :str         =   self.token_embedder(UpperCamelCase       )

					snake_case__      :int         =   encoder_input_tokens.shape[1]
					snake_case__      :List[Any]         =   torch.arange(UpperCamelCase					,device=encoder_input_tokens.device       )
					x += self.position_encoding(UpperCamelCase       )

					snake_case__      :Optional[int]         =   self.dropout_pre(UpperCamelCase       )

					# inverted the attention mask
					snake_case__      :Optional[Any]         =   encoder_input_tokens.size()
					snake_case__      :Dict         =   self.get_extended_attention_mask(UpperCamelCase					,UpperCamelCase       )

					for lyr in self.encoders:
									snake_case__      :str         =   lyr(UpperCamelCase					,UpperCamelCase       )[0]
					snake_case__      :List[Any]         =   self.layer_norm(UpperCamelCase       )

					return self.dropout_post(UpperCamelCase       ), encoder_inputs_mask