
from typing import Mapping

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


__A				=logging.get_logger(__name__)

__A				={
    '''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     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'umt5'
	lowerCAmelCase__         = ['past_key_values']



	def __init__(    self						,  lowercase=250112						,  lowercase=512						,  lowercase=64						,  lowercase=1024						,  lowercase=8						,  lowercase=None						,  lowercase=6						,  lowercase=32						,  lowercase=128						,  lowercase=0.1						,  lowercase=1e-6						,  lowercase=1.0						,  lowercase="gated-gelu"						,  lowercase=True						,  lowercase=True						,  lowercase="T5Tokenizer"						,  lowercase=True						,  lowercase=0						,  lowercase=1						,  lowercase=0						,  **lowercase						,  )				->       List[Any]:
					super().__init__(
					    is_encoder_decoder=lowercase						,  tokenizer_class=lowercase						,  tie_word_embeddings=lowercase						,  pad_token_id=lowercase						,  eos_token_id=lowercase						,  decoder_start_token_id=lowercase						,  **lowercase						,  )
					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(lowercase ) > 1 and act_info[0] != "gated" or len(lowercase ) > 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[Any]:
					return self.d_model



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self.num_heads



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					return self.num_layers




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def     SCREAMING_SNAKE_CASE_(    self )				->       Mapping[str, Mapping[int, str]]:
					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_(lowercase						,  direction="inputs" )

					return common_inputs



	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					return 13



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       float:
					return 5e-4




from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def 			lowerCamelCase_ (				):
				lowerCamelCase_	,    lowerCamelCase_						=			9, 1_4  # noqa: F841
				lowerCamelCase_						=			[
				    [0, 1, 4],
				    [0, 7, 8],
				    [1, 2, 8],
				    [7, 8, 7],
				    [7, 6, 1],
				    [2, 8, 2],
				    [8, 6, 6],
				    [2, 3, 7],
				    [2, 5, 4],
				    [6, 5, 2],
				    [3, 5, 1_4],
				    [3, 4, 9],
				    [5, 4, 1_0],
				    [1, 7, 1_1],
				]

				lowerCamelCase_						=			defaultdict(lowerCamelCase__    )
				for nodea, nodea, cost in edges:
								adjancency[nodea].append([nodea, cost]    )
								adjancency[nodea].append([nodea, cost]    )

				lowerCamelCase_						=			mst(lowerCamelCase__    )

				lowerCamelCase_						=			[
				    [7, 6, 1],
				    [2, 8, 2],
				    [6, 5, 2],
				    [0, 1, 4],
				    [2, 5, 4],
				    [2, 3, 7],
				    [0, 7, 8],
				    [3, 4, 9],
				]

				for answer in expected:
								lowerCamelCase_						=			tuple(answer[:2]    )
								lowerCamelCase_						=			tuple(edge[::-1]    )
								assert edge in result or reverse in result


