'''simple docstring'''






_UpperCAmelCase    :     str         =       frozenset(
    [
        '''prompt''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

_UpperCAmelCase    :     str         =       frozenset(['''prompt''', '''negative_prompt'''])

_UpperCAmelCase    :     List[str]         =       frozenset([])

_UpperCAmelCase    :     Any         =       frozenset(['''image'''])

_UpperCAmelCase    :     List[str]         =       frozenset(
    [
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

_UpperCAmelCase    :     int         =       frozenset(['''image'''])

_UpperCAmelCase    :     Union[str, Any]         =       frozenset(
    [
        '''prompt''',
        '''image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

_UpperCAmelCase    :     Tuple         =       frozenset(['''prompt''', '''image''', '''negative_prompt'''])

_UpperCAmelCase    :     Optional[int]         =       frozenset(
    [
        # Text guided image variation with an image mask
        '''prompt''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
    ]
)

_UpperCAmelCase    :     Dict         =       frozenset(['''prompt''', '''image''', '''mask_image''', '''negative_prompt'''])

_UpperCAmelCase    :     Union[str, Any]         =       frozenset(
    [
        # image variation with an image mask
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

_UpperCAmelCase    :     str         =       frozenset(['''image''', '''mask_image'''])

_UpperCAmelCase    :     Optional[Any]         =       frozenset(
    [
        '''example_image''',
        '''image''',
        '''mask_image''',
        '''height''',
        '''width''',
        '''guidance_scale''',
    ]
)

_UpperCAmelCase    :     Tuple         =       frozenset(['''example_image''', '''image''', '''mask_image'''])

_UpperCAmelCase    :     int         =       frozenset(['''class_labels'''])

_UpperCAmelCase    :     Dict         =       frozenset(['''class_labels'''])

_UpperCAmelCase    :     Optional[int]         =       frozenset(['''batch_size'''])

_UpperCAmelCase    :     str         =       frozenset([])

_UpperCAmelCase    :     Optional[Any]         =       frozenset(['''batch_size'''])

_UpperCAmelCase    :     int         =       frozenset([])

_UpperCAmelCase    :     Union[str, Any]         =       frozenset(
    [
        '''prompt''',
        '''audio_length_in_s''',
        '''guidance_scale''',
        '''negative_prompt''',
        '''prompt_embeds''',
        '''negative_prompt_embeds''',
        '''cross_attention_kwargs''',
    ]
)

_UpperCAmelCase    :     Any         =       frozenset(['''prompt''', '''negative_prompt'''])
_UpperCAmelCase    :     Union[str, Any]         =       frozenset(['''input_tokens'''])

_UpperCAmelCase    :     str         =       frozenset(['''input_tokens'''])




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             _A    		=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]     )



