

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     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    """longt5"""
 UpperCAmelCase				     =    ["""past_key_values"""]
 UpperCAmelCase				     =    {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


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

   UpperCAmelCase          =	self.feed_forward_proj.split("-"     )
   UpperCAmelCase          =	act_info[-1]
   UpperCAmelCase          =	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":
     UpperCAmelCase          =	"gelu_new"

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






class     __magic_name__     (							_snake_case     ):


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

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

   return common_inputs


 @property
 def _UpperCamelCase						(    self		:			Union[str, Any]     )     ->   int:
   return 1_3






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__							=			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["XLMProphetNetTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMProphetNetDecoder",
          "XLMProphetNetEncoder",
          "XLMProphetNetForCausalLM",
          "XLMProphetNetForConditionalGeneration",
          "XLMProphetNetModel",
          "XLMProphetNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase__							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



