



from typing import Mapping

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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''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_ (lowercase__  ):




       """simple docstring"""






       _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     ,):

             """simple docstring"""





             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     ,)
             UpperCAmelCase_  :	Optional[int]				      =		vocab_size
             UpperCAmelCase_  :	Any				      =		d_model
             UpperCAmelCase_  :	Any				      =		d_kv
             UpperCAmelCase_  :	int				      =		d_ff
             UpperCAmelCase_  :	Tuple				      =		num_layers
             UpperCAmelCase_  :	int				      =		(
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             UpperCAmelCase_  :	Optional[int]				      =		num_heads
             UpperCAmelCase_  :	str				      =		relative_attention_num_buckets
             UpperCAmelCase_  :	Any				      =		relative_attention_max_distance
             UpperCAmelCase_  :	Optional[Any]				      =		dropout_rate
             UpperCAmelCase_  :	Union[str, Any]				      =		layer_norm_epsilon
             UpperCAmelCase_  :	Optional[Any]				      =		initializer_factor
             UpperCAmelCase_  :	int				      =		feed_forward_proj
             UpperCAmelCase_  :	str				      =		use_cache

             UpperCAmelCase_  :	List[str]				      =		self.feed_forward_proj.split("-")
             UpperCAmelCase_  :	Any				      =		act_info[-1]
             UpperCAmelCase_  :	Optional[int]				      =		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":
                   UpperCAmelCase_  :	Tuple				      =		"gelu_new"



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.d_model



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.num_heads



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.num_layers


class   snake_case_ (lowercase__  ):




       """simple docstring"""






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

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		{
                 "input_ids": {0: "batch", 1: "encoder_sequence"},
                 "attention_mask": {0: "batch", 1: "encoder_sequence"},
             }
             if self.use_past:
                   UpperCAmelCase_  :	Union[str, Any]				      =		"past_encoder_sequence + sequence"
                   UpperCAmelCase_  :	Optional[int]				      =		{0: "batch"}
                   UpperCAmelCase_  :	Union[str, Any]				      =		{0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                   UpperCAmelCase_  :	Optional[int]				      =		{0: "batch", 1: "decoder_sequence"}
                   UpperCAmelCase_  :	Dict				      =		{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  A_   (     self):

             """simple docstring"""





             return 13



       @property
       def  A_   (     self):

             """simple docstring"""





             return 5E-4






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	list[list[Edge]]				      =		[[] for _ in range(lowercase)]
             UpperCAmelCase_  :	str				      =		size



       def __getitem__(     self     ,lowercase):

             """simple docstring"""





             return iter(self._graph[vertex])



       @property
       def  A_   (     self):

             """simple docstring"""





             return self._size



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             if weight not in (0, 1):
                   raise ValueError("Edge weight must be either 0 or 1.")

             if to_vertex < 0 or to_vertex >= self.size:
                   raise ValueError("Vertex indexes must be in [0; size).")

             self._graph[from_vertex].append(Edge(lowercase     ,lowercase))



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		deque([start_vertex])
             UpperCAmelCase_  :	list[int | None]				      =		[None] * self.size
             UpperCAmelCase_  :	Dict				      =		0

             while queue:
                   UpperCAmelCase_  :	List[Any]				      =		queue.popleft()
                   UpperCAmelCase_  :	Any				      =		distances[current_vertex]
                   if current_distance is None:
                         continue

                   for edge in self[current_vertex]:
                         UpperCAmelCase_  :	Tuple				      =		current_distance + edge.weight
                         UpperCAmelCase_  :	Optional[Any]				      =		distances[edge.destination_vertex]
                         if (
                             isinstance(lowercase     ,lowercase)
                             and new_distance >= dest_vertex_distance
                         ):
                               continue
                         UpperCAmelCase_  :	Tuple				      =		new_distance
                         if edge.weight == 0:
                               queue.appendleft(edge.destination_vertex)
                         else:
                               queue.append(edge.destination_vertex)

             if distances[finish_vertex] is None:
                   raise ValueError("No path from start_vertex to finish_vertex.")

             return distances[finish_vertex]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
