




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


a__		     =     {
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Optional[int]      	=		'ernie_m'
	__magic_name__   :       Dict[str, str]      	=		{"dropout": "classifier_dropout", "num_classes": "num_labels"}






	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						int = 250002					,     lowerCAmelCase		:						int = 768					,     lowerCAmelCase		:						int = 12					,     lowerCAmelCase		:						int = 12					,     lowerCAmelCase		:						int = 3072					,     lowerCAmelCase		:						str = "gelu"					,     lowerCAmelCase		:						float = 0.1					,     lowerCAmelCase		:						float = 0.1					,     lowerCAmelCase		:						int = 514					,     lowerCAmelCase		:						float = 0.02					,     lowerCAmelCase		:						int = 1					,     lowerCAmelCase		:						float = 1E-05					,     lowerCAmelCase		:						Any=None					,     lowerCAmelCase		:						Tuple=False					,     lowerCAmelCase		:						Dict=0.0					,     **lowerCAmelCase		:						Union[str, Any]					,     )	->       List[Any]:







						"""simple docstring"""







						super().__init__(pad_token_id=lowerCAmelCase					,     **lowerCAmelCase	)
						__UpperCamelCase					:	Dict             =      vocab_size
						__UpperCamelCase					:	str             =      hidden_size
						__UpperCamelCase					:	Any             =      num_hidden_layers
						__UpperCamelCase					:	str             =      num_attention_heads
						__UpperCamelCase					:	str             =      intermediate_size
						__UpperCamelCase					:	Optional[int]             =      hidden_act
						__UpperCamelCase					:	List[Any]             =      hidden_dropout_prob
						__UpperCamelCase					:	Tuple             =      attention_probs_dropout_prob
						__UpperCamelCase					:	Optional[int]             =      max_position_embeddings
						__UpperCamelCase					:	Tuple             =      initializer_range
						__UpperCamelCase					:	str             =      layer_norm_eps
						__UpperCamelCase					:	List[str]             =      classifier_dropout
						__UpperCamelCase					:	Any             =      is_decoder
						__UpperCamelCase					:	List[Any]             =      act_dropout












import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		field(default='image-classification'	,  metadata={'include_in_asdict_even_if_is_default': True}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'image': Image()}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'labels': ClassLabel}   )
	__magic_name__   :       str      	=		"image"
	__magic_name__   :       str      	=		"labels"






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Optional[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						if self.label_column not in features:
											raise ValueError(F'''Column {self.label_column} is not present in features.'''	)
						if not isinstance(features[self.label_column]					,     lowerCAmelCase	):
											raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''	)
						__UpperCamelCase					:	List[str]             =      copy.deepcopy(self	)
						__UpperCamelCase					:	List[Any]             =      self.label_schema.copy()
						__UpperCamelCase					:	List[str]             =      features[self.label_column]
						__UpperCamelCase					:	List[str]             =      label_schema
						return task_template






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Dict[str, str]:







						"""simple docstring"""







						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}






