


from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase      :						Union[str, Any]    =		logging.get_logger(__name__)

UpperCAmelCase      :						List[Any]    =		{
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}







class 					__lowercase					(       a_     ):



		"""simple docstring"""

		UpperCamelCase    :				Optional[Any] 					=		"plbart"
		UpperCamelCase    :				List[Any] 					=		["past_key_values"]
		UpperCamelCase    :				List[str] 					=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(   self		,			A=5_00_05		,			A=10_24		,			A=6		,			A=30_72		,			A=12		,			A=6		,			A=30_72		,			A=12		,			A=0.0		,			A=0.0		,			A=True		,			A=True		,			A="gelu"		,			A=7_68		,			A=0.1		,			A=0.1		,			A=0.0		,			A=0.02		,			A=0.0		,			A=True		,			A=1		,			A=0		,			A=2		,			A=2		,			**A		,			)     ->   Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase								=     vocab_size
					lowerCamelCase								=     max_position_embeddings
					lowerCamelCase								=     d_model
					lowerCamelCase								=     encoder_ffn_dim
					lowerCamelCase								=     encoder_layers
					lowerCamelCase								=     encoder_attention_heads
					lowerCamelCase								=     decoder_ffn_dim
					lowerCamelCase								=     decoder_layers
					lowerCamelCase								=     decoder_attention_heads
					lowerCamelCase								=     dropout
					lowerCamelCase								=     attention_dropout
					lowerCamelCase								=     activation_dropout
					lowerCamelCase								=     activation_function
					lowerCamelCase								=     init_std
					lowerCamelCase								=     encoder_layerdrop
					lowerCamelCase								=     decoder_layerdrop
					lowerCamelCase								=     classifier_dropout
					lowerCamelCase								=     use_cache
					lowerCamelCase								=     encoder_layers
					lowerCamelCase								=     scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=A		,			bos_token_id=A		,			eos_token_id=A		,			is_encoder_decoder=A		,			forced_eos_token_id=A		,			**A		,			)







class 					__lowercase					(       a_     ):



		"""simple docstring"""

		@property
		def 				__A      (   self  )     ->   Mapping[str, Mapping[int, str]]:







					'''simple docstring'''







					return OrderedDict(
					    [
					        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
					        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
					    ]  )







		@property
		def 				__A      (   self  )     ->   Mapping[str, Mapping[int, str]]:







					'''simple docstring'''







					if self.use_past:
								return OrderedDict(
								    [
								        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
								        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
								        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
								    ]  )
					else:
								return OrderedDict(
								    [
								        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
								        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
								    ]  )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      :						List[str]    =		{
    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						str    =		["BigBirdTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						Dict    =		[
		    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BigBirdForCausalLM",
		    "BigBirdForMaskedLM",
		    "BigBirdForMultipleChoice",
		    "BigBirdForPreTraining",
		    "BigBirdForQuestionAnswering",
		    "BigBirdForSequenceClassification",
		    "BigBirdForTokenClassification",
		    "BigBirdLayer",
		    "BigBirdModel",
		    "BigBirdPreTrainedModel",
		    "load_tf_weights_in_big_bird",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase      :						Dict    =		[
		    "FlaxBigBirdForCausalLM",
		    "FlaxBigBirdForMaskedLM",
		    "FlaxBigBirdForMultipleChoice",
		    "FlaxBigBirdForPreTraining",
		    "FlaxBigBirdForQuestionAnswering",
		    "FlaxBigBirdForSequenceClassification",
		    "FlaxBigBirdForTokenClassification",
		    "FlaxBigBirdModel",
		    "FlaxBigBirdPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_big_bird import BigBirdTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_big_bird_fast import BigBirdTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_big_bird import (
				    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdForCausalLM,
				    BigBirdForMaskedLM,
				    BigBirdForMultipleChoice,
				    BigBirdForPreTraining,
				    BigBirdForQuestionAnswering,
				    BigBirdForSequenceClassification,
				    BigBirdForTokenClassification,
				    BigBirdLayer,
				    BigBirdModel,
				    BigBirdPreTrainedModel,
				    load_tf_weights_in_big_bird,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_big_bird import (
				    FlaxBigBirdForCausalLM,
				    FlaxBigBirdForMaskedLM,
				    FlaxBigBirdForMultipleChoice,
				    FlaxBigBirdForPreTraining,
				    FlaxBigBirdForQuestionAnswering,
				    FlaxBigBirdForSequenceClassification,
				    FlaxBigBirdForTokenClassification,
				    FlaxBigBirdModel,
				    FlaxBigBirdPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

