


from typing import Mapping

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


A							:					Tuple     						=     logging.get_logger(__name__)

A							:					List[Any]     						=     {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""t5"""
					snake_case_		:				Tuple										=	["""past_key_values"""]
					snake_case_		:				List[str]										=	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str=3_2_1_2_8     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=6_4     ,    SCREAMING_SNAKE_CASE	:		Tuple=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=6     ,    SCREAMING_SNAKE_CASE	:		Dict=None     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=8     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2     ,    SCREAMING_SNAKE_CASE	:		str=1_2_8     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=1e-6     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=1.0     ,    SCREAMING_SNAKE_CASE	:		List[Any]="relu"     ,    SCREAMING_SNAKE_CASE	:		Tuple=True     ,    SCREAMING_SNAKE_CASE	:		str=True     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0     ,    SCREAMING_SNAKE_CASE	:		int=1     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		vocab_size
											__snake_case				=		d_model
											__snake_case				=		d_kv
											__snake_case				=		d_ff
											__snake_case				=		num_layers
											__snake_case				=		(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											__snake_case				=		num_heads
											__snake_case				=		relative_attention_num_buckets
											__snake_case				=		relative_attention_max_distance
											__snake_case				=		dropout_rate
											__snake_case				=		layer_norm_epsilon
											__snake_case				=		initializer_factor
											__snake_case				=		feed_forward_proj
											__snake_case				=		use_cache

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

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

											super().__init__(
											    pad_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    is_encoder_decoder=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )






class     UpperCamelCase(  _a   ):


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

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

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

											return common_inputs






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	int:




											'''simple docstring'''

											return 1_3










from __future__ import annotations

from collections.abc import Iterator






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	None:




											'''simple docstring'''

											__snake_case				=		value
											__snake_case				=		None
											__snake_case				=		None






class     UpperCamelCase:


					def __init__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Node						)			->	None:




											'''simple docstring'''

											__snake_case				=		tree


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Node | None						)			->	int:




											'''simple docstring'''

											if node is None:
																	return 0
											return node.value + (
											    self.depth_first_search(node.left						) + self.depth_first_search(node.right						)
											)






					def __iter__(						self	:		int						)			->	Iterator[int]:




											'''simple docstring'''

											yield self.depth_first_search(self.tree						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





