



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__   =       {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

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

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__   =       ["MBartTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__   =       [
							    "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MBartForCausalLM",
							    "MBartForConditionalGeneration",
							    "MBartForQuestionAnswering",
							    "MBartForSequenceClassification",
							    "MBartModel",
							    "MBartPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__   =       [
							    "TFMBartForConditionalGeneration",
							    "TFMBartModel",
							    "TFMBartPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__   =       [
							    "FlaxMBartForConditionalGeneration",
							    "FlaxMBartForQuestionAnswering",
							    "FlaxMBartForSequenceClassification",
							    "FlaxMBartModel",
							    "FlaxMBartPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

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







'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			classifier_dropout




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]       )



