
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase					:    Tuple           = logging.get_logger(__name__)

lowerCAmelCase					:    List[Any]           = {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""data2vec-text"""







				def __init__(       self						,				A_=30522						,				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_						,				)->		Tuple:







											'''simple docstring'''






											super().__init__(pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_						,				**A_     )

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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







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







											'''simple docstring'''






											if self.task == "multiple-choice":
																		UpperCamelCase   			=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		UpperCamelCase   			=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]     )






'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




