

'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     = {
    "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       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					Optional[Any]     =					'''roberta'''




      def __init__(       self			:		Dict  ,       snake_case			:		List[Any]=50265  ,       snake_case			:		int=768  ,       snake_case			:		List[Any]=12  ,       snake_case			:		str=12  ,       snake_case			:		List[Any]=3072  ,       snake_case			:		Optional[int]="gelu"  ,       snake_case			:		Union[str, Any]=0.1  ,       snake_case			:		int=0.1  ,       snake_case			:		Dict=512  ,       snake_case			:		Dict=2  ,       snake_case			:		Any=0.02  ,       snake_case			:		Dict=1e-12  ,       snake_case			:		List[str]=1  ,       snake_case			:		str=0  ,       snake_case			:		Union[str, Any]=2  ,       snake_case			:		Dict="absolute"  ,       snake_case			:		Union[str, Any]=True  ,       snake_case			:		str=None  ,       **snake_case			:		int  ,       ):



          """simple docstring"""





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

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



class       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE       ):






      '''simple docstring'''






      @property
      def 						__UpperCAmelCase      (       self			:		Any ):



          """simple docstring"""





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





'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    ImageTextPipelineOutput,
						    UniDiffuserPipeline,
						)
else:
						from .modeling_text_decoder import UniDiffuserTextDecoder
						from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
						from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
