


"""simple docstring"""


import warnings

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


A	:  Optional[int]            =     logging.get_logger(__name__)

A	:  List[str]            =     {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 ="""mvp"""
							__UpperCAmelCase		:       str							 =["""past_key_values"""]
							__UpperCAmelCase		:       Dict							 ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

							def __init__(	self       ,  __a=5_02_67       ,  __a=10_24       ,  __a=12       ,  __a=40_96       ,  __a=16       ,  __a=12       ,  __a=40_96       ,  __a=16       ,  __a=0.0       ,  __a=0.0       ,  __a="gelu"       ,  __a=10_24       ,  __a=0.1       ,  __a=0.0       ,  __a=0.0       ,  __a=0.0_2       ,  __a=0.0       ,  __a=False       ,  __a=True       ,  __a=1       ,  __a=0       ,  __a=2       ,  __a=True       ,  __a=2       ,  __a=2       ,  __a=False       ,  __a=1_00       ,  __a=8_00       ,  **__a       ,  ):
										__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
										__lowerCAmelCase							       =     use_prompt
										__lowerCAmelCase							       =     prompt_length
										__lowerCAmelCase							       =     prompt_mid_dim

										super().__init__(
										    pad_token_id=__a       ,  bos_token_id=__a       ,  eos_token_id=__a       ,  is_encoder_decoder=__a       ,  decoder_start_token_id=__a       ,  forced_eos_token_id=__a       ,  **__a       ,  )

										if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"       ,  __a      ):
													__lowerCAmelCase							       =     self.bos_token_id
													warnings.warn(
													    f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
													    "The config can simply be saved and uploaded again to be fixed."      )








"""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=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

							def      snake_case							(	self       ,  __a      ):
										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]       ,  __a      ):
													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      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




