
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__a  			=					logging.get_logger(__name__)

__a  			=					{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}
class  lowercase__(				lowerCAmelCase_							):



	"""simple docstring"""




	a			:Any         =	'deberta-v2'
	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict=1_2_8_1_0_0       ,							SCREAMING_SNAKE_CASE_  :			Any=1_5_3_6       ,							SCREAMING_SNAKE_CASE_  :			Tuple=2_4       ,							SCREAMING_SNAKE_CASE_  :			int=2_4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=6_1_4_4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.02       ,							SCREAMING_SNAKE_CASE_  :			Dict=1e-7       ,							SCREAMING_SNAKE_CASE_  :			int=False       ,							SCREAMING_SNAKE_CASE_  :			Any=-1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0       ,							SCREAMING_SNAKE_CASE_  :			Tuple=True       ,							SCREAMING_SNAKE_CASE_  :			Any=None       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=0       ,							SCREAMING_SNAKE_CASE_  :			Tuple="gelu"       ,							**SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							)       ->     Optional[int]:
					super().__init__(**_snake_case       )

					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_act
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					relative_attention
					lowercase_       =					max_relative_positions
					lowercase_       =					pad_token_id
					lowercase_       =					position_biased_input

					# Backwards compatibility
					if type(_snake_case       ) == str:
									lowercase_       =					[x.strip() for x in pos_att_type.lower().split('''|'''       )]

					lowercase_       =					pos_att_type
					lowercase_       =					vocab_size
					lowercase_       =					layer_norm_eps

					lowercase_       =					kwargs.get('''pooler_hidden_size'''       ,							_snake_case       )
					lowercase_       =					pooler_dropout
					lowercase_       =					pooler_hidden_act

class  lowercase__(				lowerCAmelCase_							):



	"""simple docstring"""




	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
									lowercase_       =					{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
									lowercase_       =					{0: 'batch', 1: 'sequence'}
					if self._config.type_vocab_size > 0:
									return OrderedDict(
									    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]       )
					else:
									return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]       )
	@property
	def 						_lowercase  (       self  :			Dict       )       ->     int:
					return 1_2




	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			Optional["TensorType"] = None       ,							SCREAMING_SNAKE_CASE_  :			int = 3       ,							SCREAMING_SNAKE_CASE_  :			int = 4_0       ,							SCREAMING_SNAKE_CASE_  :			int = 4_0       ,							SCREAMING_SNAKE_CASE_  :			"PreTrainedTokenizerBase" = None       ,							)       ->     Mapping[str, Any]:
					lowercase_       =					super().generate_dummy_inputs(preprocessor=_snake_case       ,							framework=_snake_case       )
					if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
									del dummy_inputs["token_type_ids"]
					return dummy_inputs









from typing import TYPE_CHECKING

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


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 a_   :Optional[int]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






