
lowercase						:      List[Any]									= frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

lowercase						:      Any									= frozenset(['prompt', 'negative_prompt'])

lowercase						:      Dict									= frozenset([])

lowercase						:      Optional[int]									= frozenset(['image'])

lowercase						:      Any									= frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

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

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

lowercase						:      Union[str, Any]									= frozenset(['prompt', 'image', 'negative_prompt'])

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

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

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

lowercase						:      Any									= frozenset(['image', 'mask_image'])

lowercase						:      Tuple									= frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

lowercase						:      List[str]									= frozenset(['example_image', 'image', 'mask_image'])

lowercase						:      Tuple									= frozenset(['class_labels'])

lowercase						:      Union[str, Any]									= frozenset(['class_labels'])

lowercase						:      Dict									= frozenset(['batch_size'])

lowercase						:      Optional[int]									= frozenset([])

lowercase						:      Tuple									= frozenset(['batch_size'])

lowercase						:      Dict									= frozenset([])

lowercase						:      Optional[Any]									= frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

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

lowercase						:      Dict									= frozenset(['input_tokens'])

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


lowercase						:      Dict									= logging.get_logger(__name__)

lowercase						:      Optional[Any]									= {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'speech_to_text_2'
	_A			=  ['past_key_values']
	_A			=  {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(      self	:Dict  ,     a	:Tuple=1_0_0_0_0  ,     a	:Optional[int]=6  ,     a	:List[str]=2_0_4_8  ,     a	:Tuple=4  ,     a	:List[Any]=0.0  ,     a	:str=True  ,     a	:Any="relu"  ,     a	:Any=2_5_6  ,     a	:Optional[int]=0.1  ,     a	:Any=0.0  ,     a	:int=0.0  ,     a	:int=0.02  ,     a	:List[Any]=2  ,     a	:Tuple=True  ,     a	:str=1  ,     a	:Optional[int]=0  ,     a	:List[Any]=2  ,     a	:Any=1_0_2_4  ,     **a	:str  ,     )	->				int:
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     int         =      d_model
								__UpperCamelCase			:     Optional[int]         =      decoder_ffn_dim
								__UpperCamelCase			:     Any         =      decoder_layers
								__UpperCamelCase			:     Any         =      decoder_attention_heads
								__UpperCamelCase			:     Tuple         =      dropout
								__UpperCamelCase			:     Any         =      attention_dropout
								__UpperCamelCase			:     Any         =      activation_dropout
								__UpperCamelCase			:     Dict         =      activation_function
								__UpperCamelCase			:     int         =      init_std
								__UpperCamelCase			:     List[str]         =      decoder_layerdrop
								__UpperCamelCase			:     Optional[Any]         =      use_cache
								__UpperCamelCase			:     int         =      decoder_layers
								__UpperCamelCase			:     Union[str, Any]         =      scale_embedding  # scale factor will be sqrt(d_model) if True
								__UpperCamelCase			:     Dict         =      max_target_positions

								super().__init__(
								    pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     decoder_start_token_id=a  ,     **a  ,     )