
'''simple docstring'''

from typing import Mapping

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


UpperCAmelCase__ :			Tuple											=		logging.get_logger(__name__)

UpperCAmelCase__ :			List[str]											=		{
    "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  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Union[str, Any]   =  'umt5'
	snake_case__   :Any   =  ['past_key_values']





	def __init__(       self      :							List[Any]   ,				__magic_name__      :							Tuple=250112   ,				__magic_name__      :							str=512   ,				__magic_name__      :							int=64   ,				__magic_name__      :							str=1024   ,				__magic_name__      :							Tuple=8   ,				__magic_name__      :							Optional[int]=None   ,				__magic_name__      :							Optional[Any]=6   ,				__magic_name__      :							Dict=32   ,				__magic_name__      :							Optional[Any]=128   ,				__magic_name__      :							Union[str, Any]=0.1   ,				__magic_name__      :							int=1E-6   ,				__magic_name__      :							Optional[int]=1.0   ,				__magic_name__      :							Dict="gated-gelu"   ,				__magic_name__      :							List[str]=True   ,				__magic_name__      :							Tuple=True   ,				__magic_name__      :							Optional[int]="T5Tokenizer"   ,				__magic_name__      :							str=True   ,				__magic_name__      :							int=0   ,				__magic_name__      :							Union[str, Any]=1   ,				__magic_name__      :							str=0   ,				**__magic_name__      :							Any   ,				):


				"""simple docstring"""

				super().__init__(
				    is_encoder_decoder=__magic_name__   ,				tokenizer_class=__magic_name__   ,				tie_word_embeddings=__magic_name__   ,				pad_token_id=__magic_name__   ,				eos_token_id=__magic_name__   ,				decoder_start_token_id=__magic_name__   ,				**__magic_name__   ,				)
				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				d_model
				lowerCAmelCase__										=				d_kv
				lowerCAmelCase__										=				d_ff
				lowerCAmelCase__										=				num_layers
				lowerCAmelCase__										=				(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				lowerCAmelCase__										=				num_heads
				lowerCAmelCase__										=				relative_attention_num_buckets
				lowerCAmelCase__										=				relative_attention_max_distance
				lowerCAmelCase__										=				dropout_rate
				lowerCAmelCase__										=				layer_norm_epsilon
				lowerCAmelCase__										=				initializer_factor
				lowerCAmelCase__										=				feed_forward_proj
				lowerCAmelCase__										=				use_cache

				lowerCAmelCase__										=				self.feed_forward_proj.split("-"				)
				lowerCAmelCase__										=				act_info[-1]
				lowerCAmelCase__										=				act_info[0] == "gated"

				if len(__magic_name__				) > 1 and act_info[0] != "gated" or len(__magic_name__				) > 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":
							lowerCAmelCase__										=				"gelu_new"





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				return self.d_model





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				return self.num_heads







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self.num_layers






class  A  (						SCREAMING_SNAKE_CASE__     ):





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							lowerCAmelCase__										=				"past_encoder_sequence + sequence"
							lowerCAmelCase__										=				{0: "batch"}
							lowerCAmelCase__										=				{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							lowerCAmelCase__										=				{0: "batch", 1: "decoder_sequence"}
							lowerCAmelCase__										=				{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(__magic_name__   ,				direction="inputs"				)

				return common_inputs





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return 13







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return 5E-4







import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCamelCase_						:     List[str]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	*snake_case				: Optional[Any]      ,	**snake_case				: Optional[Any]    ) ->			None:
							warnings.warn(
							    "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use FlavaImageProcessor instead."      ,	snake_case      ,	)
							super().__init__(*snake_case      ,	**snake_case    )




