


from typing import Mapping

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


lowerCAmelCase__						=			logging.get_logger(__name__)

lowerCAmelCase__						=			{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			"longt5"
     __SCREAMING_SNAKE_CASE        =			["past_key_values"]
     __SCREAMING_SNAKE_CASE        =			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(						self			,						__lowerCamelCase=3_2_1_2_8			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=6_4			,						__lowerCamelCase=2_0_4_8			,						__lowerCamelCase=6			,						__lowerCamelCase=None			,						__lowerCamelCase=8			,						__lowerCamelCase=1_2_7			,						__lowerCamelCase=1_6			,						__lowerCamelCase=3_2			,						__lowerCamelCase=1_2_8			,						__lowerCamelCase=0.1			,						__lowerCamelCase=1e-6			,						__lowerCamelCase=1.0			,						__lowerCamelCase="relu"			,						__lowerCamelCase=True			,						__lowerCamelCase="local"			,						__lowerCamelCase=True			,						__lowerCamelCase=0			,						__lowerCamelCase=1			,						**__lowerCamelCase			,						)      ->    List[str]:
            _A				:	str       =			vocab_size
            _A				:	List[Any]       =			d_model
            _A				:	Optional[Any]       =			d_kv
            _A				:	int       =			d_ff
            _A				:	int       =			num_layers
            # default = symmetry
            _A				:	Tuple       =			num_decoder_layers if num_decoder_layers is not None else self.num_layers
            _A				:	Union[str, Any]       =			num_heads
            _A				:	Optional[Any]       =			local_radius
            _A				:	Union[str, Any]       =			global_block_size
            _A				:	Any       =			relative_attention_num_buckets
            _A				:	str       =			relative_attention_max_distance
            _A				:	Union[str, Any]       =			dropout_rate
            _A				:	Union[str, Any]       =			layer_norm_epsilon
            _A				:	Dict       =			initializer_factor
            _A				:	int       =			feed_forward_proj
            _A				:	List[Any]       =			encoder_attention_type
            _A				:	int       =			use_cache

            _A				:	Optional[Any]       =			self.feed_forward_proj.split("-")
            _A				:	Union[str, Any]       =			act_info[-1]
            _A				:	Any       =			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'")

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                   _A				:	List[Any]       =			"gelu_new"

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







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''


     @property
     def 			_lowerCamelCase			(						self)      ->    Mapping[str, Mapping[int, str]]:
            _A				:	int       =			{
                "input_ids": {0: "batch", 1: "encoder_sequence"},
                "attention_mask": {0: "batch", 1: "encoder_sequence"},
            }
            if self.use_past:
                   _A				:	Optional[int]       =			"past_encoder_sequence + sequence"
                   _A				:	Optional[Any]       =			{0: "batch"}
                   _A				:	Dict       =			{0: "batch", 1: "past_decoder_sequence + sequence"}
            else:
                   _A				:	Optional[Any]       =			{0: "batch", 1: "decoder_sequence"}
                   _A				:	Dict       =			{0: "batch", 1: "decoder_sequence"}

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

            return common_inputs


     @property
     def 			_lowerCamelCase			(						self)      ->    int:
            return 1_3




from __future__ import annotations

from typing import Any







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    None:  # noqa: A002
            _A				:	List[str]       =			item
            _A				:	List[Any]       =			next







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self)      ->    None:
            _A				:	Node | None       =			None
            _A				:	List[Any]       =			0


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    None:
            _A				:	Union[str, Any]       =			Node(__lowerCamelCase			,						self.head)
            self.size += 1


     def 			_lowerCamelCase			(						self)      ->    Any:
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   _A				:	str       =			self.head.item
                   _A				:	List[Any]       =			self.head.next
                   self.size -= 1
                   return item


     def 			_lowerCamelCase			(						self)      ->    bool:
            return self.head is None


     def __str__(						self)      ->    str:
            if self.is_empty():
                   return ""
            else:
                   _A				:	List[str]       =			self.head
                   _A				:	Optional[int]       =			""
                   _A				:	list[str]       =			[]
                   while iterate:
                          item_list.append(str(iterate.item))
                          _A				:	Optional[Any]       =			iterate.next

                   _A				:	List[str]       =			" --> ".join(__lowerCamelCase)

                   return item_str


     def __len__(						self)      ->    int:
            return self.size
