



from typing import Mapping

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


_lowercase: Optional[Any]      			=		logging.get_logger(__name__)

_lowercase: Tuple      			=		{
    "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 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"umt5"
	__A							=							["past_key_values"]




	def __init__(self		,       lowerCamelCase_=250112		,       lowerCamelCase_=512		,       lowerCamelCase_=64		,       lowerCamelCase_=1024		,       lowerCamelCase_=8		,       lowerCamelCase_=None		,       lowerCamelCase_=6		,       lowerCamelCase_=32		,       lowerCamelCase_=128		,       lowerCamelCase_=0.1		,       lowerCamelCase_=1E-6		,       lowerCamelCase_=1.0		,       lowerCamelCase_="gated-gelu"		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_="T5Tokenizer"		,       lowerCamelCase_=True		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.d_model




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.num_heads




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.num_layers




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						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_(lowerCamelCase_		,       direction="inputs"		)

						return common_inputs




	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 13




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return 5E-4





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase: Union[str, Any]      			=		{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: Dict      			=		["BridgeTowerImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: int      			=		[
				    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BridgeTowerForContrastiveLearning",
				    "BridgeTowerForImageAndTextRetrieval",
				    "BridgeTowerForMaskedLM",
				    "BridgeTowerModel",
				    "BridgeTowerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_bridgetower import (
				    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BridgeTowerConfig,
				    BridgeTowerTextConfig,
				    BridgeTowerVisionConfig,
				)
				from .processing_bridgetower import BridgeTowerProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_bridgetower import BridgeTowerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bridgetower import (
								    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BridgeTowerForContrastiveLearning,
								    BridgeTowerForImageAndTextRetrieval,
								    BridgeTowerForMaskedLM,
								    BridgeTowerModel,
								    BridgeTowerPreTrainedModel,
								)


else:
				import sys

				_lowercase: Optional[Any]      			=		_LazyModule(__name__, globals()["__file__"], _import_structure)
