code: 
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  snake_case_    						=				logging.get_logger(__name__)
  
  snake_case_    						=				{
      '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
      '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
      '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
      '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
  }
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: List[Any]														=  """flaubert"""
      __lowerCamelCase		: Optional[Any]														=  {
          """hidden_size""": """emb_dim""",
          """num_attention_heads""": """n_heads""",
          """num_hidden_layers""": """n_layers""",
          """n_words""": """vocab_size""",  # For backward compatibility
      }
  
  
  
  
  
      def __init__(   self		,     a=False		,     a=0.0		,     a=3_0145		,     a=2048		,     a=12		,     a=16		,     a=0.1		,     a=0.1		,     a=True		,     a=False		,     a=False		,     a=False		,     a=1		,     a=True		,     a=512		,     a=2048**-0.5		,     a=1e-12		,     a=0.02		,     a=0		,     a=1		,     a=2		,     a=3		,     a=5		,     a=True		,     a="first"		,     a=True		,     a=None		,     a=True		,     a=0.1		,     a=5		,     a=5		,     a=0		,     a=0		,     a=2		,     a=0		,     **a		,     ):
           lowercase__					:     Optional[Any]   = pre_norm
           lowercase__					:     Optional[Any]   = layerdrop
           lowercase__					:     Optional[Any]   = vocab_size
           lowercase__					:     Optional[Any]   = emb_dim
           lowercase__					:     Optional[Any]   = n_layers
           lowercase__					:     str   = n_heads
           lowercase__					:     int   = dropout
           lowercase__					:     Optional[int]   = attention_dropout
           lowercase__					:     Any   = gelu_activation
           lowercase__					:     Tuple   = sinusoidal_embeddings
           lowercase__					:     Union[str, Any]   = causal
           lowercase__					:     List[Any]   = asm
           lowercase__					:     Union[str, Any]   = n_langs
           lowercase__					:     Union[str, Any]   = use_lang_emb
           lowercase__					:     str   = layer_norm_eps
           lowercase__					:     List[str]   = bos_index
           lowercase__					:     Optional[int]   = eos_index
           lowercase__					:     str   = pad_index
           lowercase__					:     Optional[Any]   = unk_index
           lowercase__					:     int   = mask_index
           lowercase__					:     Dict   = is_encoder
           lowercase__					:     Dict   = max_position_embeddings
           lowercase__					:     int   = embed_init_std
           lowercase__					:     Any   = init_std
           lowercase__					:     str   = summary_type
           lowercase__					:     Tuple   = summary_use_proj
           lowercase__					:     Tuple   = summary_activation
           lowercase__					:     Optional[int]   = summary_proj_to_labels
           lowercase__					:     Optional[int]   = summary_first_dropout
           lowercase__					:     List[Any]   = start_n_top
           lowercase__					:     List[Any]   = end_n_top
           lowercase__					:     List[Any]   = mask_token_id
           lowercase__					:     List[str]   = lang_id
  
           if "n_words" in kwargs:
                lowercase__					:     Dict   = kwargs['n_words']
  
           super().__init__(pad_token_id=a		,     bos_token_id=a		,     **a)
  
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           if self.task == "multiple-choice":
                lowercase__					:     Tuple   = {0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                lowercase__					:     Optional[int]   = {0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
               ])
  
  
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    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: 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
  
  