
"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					"gptsan-japanese"
				SCREAMING_SNAKE_CASE_:							List[str]							=					[
				    "past_key_values",
				]
				SCREAMING_SNAKE_CASE_:							Dict							=					{
				    "hidden_size": "d_model",
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}


				def __init__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			int=36_000				,     UpperCAmelCase_	:			Dict=1_280				,     UpperCAmelCase_	:			Tuple=1_024				,     UpperCAmelCase_	:			Union[str, Any]=8_192				,     UpperCAmelCase_	:			Dict=4_096				,     UpperCAmelCase_	:			str=128				,     UpperCAmelCase_	:			Dict=10				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			Dict=16				,     UpperCAmelCase_	:			Dict=16				,     UpperCAmelCase_	:			Any=128				,     UpperCAmelCase_	:			str=0.0				,     UpperCAmelCase_	:			Union[str, Any]=1E-5				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Optional[Any]=0.0				,     UpperCAmelCase_	:			Optional[int]="float32"				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			Optional[Any]=0.002				,     UpperCAmelCase_	:			Tuple=False				,     UpperCAmelCase_	:			Any=True				,     UpperCAmelCase_	:			List[Any]=35_998				,     UpperCAmelCase_	:			Any=35_995				,     UpperCAmelCase_	:			List[Any]=35_999				,     **UpperCAmelCase_	:			Dict				,     )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			d_model
											_lowerCAmelCase  	=			d_ff
											_lowerCAmelCase  	=			d_ext
											_lowerCAmelCase  	=			d_spout
											_lowerCAmelCase  	=			num_switch_layers
											_lowerCAmelCase  	=			num_ext_layers
											_lowerCAmelCase  	=			num_switch_layers + num_ext_layers
											_lowerCAmelCase  	=			num_heads
											_lowerCAmelCase  	=			num_experts
											_lowerCAmelCase  	=			expert_capacity
											_lowerCAmelCase  	=			dropout_rate
											_lowerCAmelCase  	=			layer_norm_epsilon
											_lowerCAmelCase  	=			router_bias
											_lowerCAmelCase  	=			router_jitter_noise
											_lowerCAmelCase  	=			router_dtype
											_lowerCAmelCase  	=			router_ignore_padding_tokens
											_lowerCAmelCase  	=			output_hidden_states
											_lowerCAmelCase  	=			output_attentions
											_lowerCAmelCase  	=			initializer_factor
											_lowerCAmelCase  	=			output_router_logits
											_lowerCAmelCase  	=			use_cache

											super().__init__(
											    separator_token_id=UpperCAmelCase_				,     pad_token_id=UpperCAmelCase_				,     eos_token_id=UpperCAmelCase_				,     **UpperCAmelCase_				,     )








"""simple docstring"""



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'''



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


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]  )		->  Dict:







											"""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]				,     UpperCAmelCase_  ):
																		raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
											_lowerCAmelCase  	=			copy.deepcopy(self  )
											_lowerCAmelCase  	=			self.label_schema.copy()
											_lowerCAmelCase  	=			features[self.label_column]
											_lowerCAmelCase  	=			label_schema
											return task_template







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict[str, str]:







											"""simple docstring"""




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






