code: from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowercase__    :       Optional[Any]						=			logging.get_logger(__name__)
  
  lowercase__    :       Union[str, Any]						=			{
      '''microsoft/prophetnet-large-uncased''': (
          '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
      ),
  }
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  	UpperCAmelCase_						:      Optional[int]     =       """prophetnet"""
  	UpperCAmelCase_						:      Optional[int]     =       ["""past_key_values"""]
  	UpperCAmelCase_						:      Any     =       {
  	    """num_attention_heads""": """num_encoder_attention_heads""",
  	}
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = "gelu"	,    __SCREAMING_SNAKE_CASE = 30522	,    __SCREAMING_SNAKE_CASE = 1024	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 4096	,    __SCREAMING_SNAKE_CASE = 12	,    __SCREAMING_SNAKE_CASE = 16	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 0.1	,    __SCREAMING_SNAKE_CASE = 512	,    __SCREAMING_SNAKE_CASE = 0.0_2	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 2	,    __SCREAMING_SNAKE_CASE = 32	,    __SCREAMING_SNAKE_CASE = 128	,    __SCREAMING_SNAKE_CASE = False	,    __SCREAMING_SNAKE_CASE = 0.0	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 0	,    __SCREAMING_SNAKE_CASE = 1	,    __SCREAMING_SNAKE_CASE = 2	,    **__SCREAMING_SNAKE_CASE	,    )		->int:
  								lowerCAmelCase				=	vocab_size
  								lowerCAmelCase				=	hidden_size
  								lowerCAmelCase				=	encoder_ffn_dim
  								lowerCAmelCase				=	num_encoder_layers
  								lowerCAmelCase				=	num_encoder_attention_heads
  								lowerCAmelCase				=	decoder_ffn_dim
  								lowerCAmelCase				=	num_decoder_layers
  								lowerCAmelCase				=	num_decoder_attention_heads
  								lowerCAmelCase				=	max_position_embeddings
  								lowerCAmelCase				=	init_std  # Normal(0, this parameter)
  								lowerCAmelCase				=	activation_function
  
  								# parameters for prophetnet
  								lowerCAmelCase				=	ngram
  								lowerCAmelCase				=	num_buckets
  								lowerCAmelCase				=	relative_max_distance
  								lowerCAmelCase				=	disable_ngram_loss
  								lowerCAmelCase				=	eps
  
  								# 3 Types of Dropout
  								lowerCAmelCase				=	attention_dropout
  								lowerCAmelCase				=	activation_dropout
  								lowerCAmelCase				=	dropout
  
  								lowerCAmelCase				=	use_cache
  
  								super().__init__(
  								    pad_token_id=__SCREAMING_SNAKE_CASE	,    bos_token_id=__SCREAMING_SNAKE_CASE	,    eos_token_id=__SCREAMING_SNAKE_CASE	,    is_encoder_decoder=__SCREAMING_SNAKE_CASE	,    add_cross_attention=__SCREAMING_SNAKE_CASE	,    decoder_start_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  	@num_hidden_layers.setter
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								raise NotImplementedError(
  								    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
  								    ''' `num_decoder_layers`.'''       )
  
  
  
  
style_context: from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  
  from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available
  
  
  
  
  @dataclass
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Union[List[PIL.Image.Image], np.ndarray]
  
  
  if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
  	from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
  	from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
  
  
  
  