

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),
             ])




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

