






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


_A			:    List[str]					  =logging.get_logger(__name__)

_A			:    int					  ={
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """roberta"""




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]=50_265    ,							UpperCamelCase__:       Any=768    ,							UpperCamelCase__:       Optional[int]=12    ,							UpperCamelCase__:       Any=12    ,							UpperCamelCase__:       Optional[Any]=3_072    ,							UpperCamelCase__:       Tuple="gelu"    ,							UpperCamelCase__:       int=0.1    ,							UpperCamelCase__:       str=0.1    ,							UpperCamelCase__:       Optional[int]=512    ,							UpperCamelCase__:       List[Any]=2    ,							UpperCamelCase__:       Tuple=0.02    ,							UpperCamelCase__:       Optional[Any]=1e-12    ,							UpperCamelCase__:       Union[str, Any]=1    ,							UpperCamelCase__:       str=0    ,							UpperCamelCase__:       List[str]=2    ,							UpperCamelCase__:       Optional[Any]="absolute"    ,							UpperCamelCase__:       Optional[Any]=True    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Union[str, Any]    ,							):
														super().__init__(pad_token_id=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= vocab_size
														lowerCamelCase__      :			List[str] 						= hidden_size
														lowerCamelCase__      :			Any 						= num_hidden_layers
														lowerCamelCase__      :			Union[str, Any] 						= num_attention_heads
														lowerCamelCase__      :			Any 						= hidden_act
														lowerCamelCase__      :			Optional[int] 						= intermediate_size
														lowerCamelCase__      :			Optional[int] 						= hidden_dropout_prob
														lowerCamelCase__      :			Dict 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Optional[int] 						= max_position_embeddings
														lowerCamelCase__      :			Tuple 						= type_vocab_size
														lowerCamelCase__      :			Union[str, Any] 						= initializer_range
														lowerCamelCase__      :			List[str] 						= layer_norm_eps
														lowerCamelCase__      :			str 						= position_embedding_type
														lowerCamelCase__      :			Union[str, Any] 						= use_cache
														lowerCamelCase__      :			Any 						= classifier_dropout



class _lowercase			(       _lowercase	):




							@property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														if self.task == "multiple-choice":
																					lowerCamelCase__      :			int 						= {0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					lowerCamelCase__      :			Union[str, Any] 						= {0: """batch""", 1: """sequence"""}
														return OrderedDict(
														    [
														        ("""input_ids""", dynamic_axis),
														        ("""attention_mask""", dynamic_axis),
														    ]  )









'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

