



'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_     :       Tuple								=						logging.get_logger(__name__)

lowerCAmelCase_     :       int								=						{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}

class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   List[str]	     =				'roberta-prelayernorm'







						def __init__(  self   :      Any				,    lowerCAmelCase__   :      Optional[int]=5_02_65				,    lowerCAmelCase__   :      Any=7_68				,    lowerCAmelCase__   :      Union[str, Any]=12				,    lowerCAmelCase__   :      Union[str, Any]=12				,    lowerCAmelCase__   :      Optional[int]=30_72				,    lowerCAmelCase__   :      List[str]="gelu"				,    lowerCAmelCase__   :      List[str]=0.1				,    lowerCAmelCase__   :      Optional[Any]=0.1				,    lowerCAmelCase__   :      str=5_12				,    lowerCAmelCase__   :      Optional[Any]=2				,    lowerCAmelCase__   :      Tuple=0.02				,    lowerCAmelCase__   :      str=1e-12				,    lowerCAmelCase__   :      List[str]=1				,    lowerCAmelCase__   :      Dict=0				,    lowerCAmelCase__   :      Dict=2				,    lowerCAmelCase__   :      Optional[Any]="absolute"				,    lowerCAmelCase__   :      Any=True				,    lowerCAmelCase__   :      Any=None				,    **lowerCAmelCase__   :      List[str]				,    ):

													"""simple docstring"""
													super().__init__(pad_token_id=__lowerCAmelCase				,    bos_token_id=__lowerCAmelCase				,    eos_token_id=__lowerCAmelCase				,    **__lowerCAmelCase				)

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


class 	lowerCamelCase_							(					snake_case_			):







						@property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""







													if self.task == "multiple-choice":
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
													else:
																				SCREAMING_SNAKE_CASE					:      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




