






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


A_     :					Dict							=     logging.get_logger(__name__)

A_     :					str							=     {
    "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 						a_ (      _a					):







      '''simple docstring'''

      lowerCamelCase__			:							Tuple					=      'flaubert'
      lowerCamelCase__			:							Union[str, 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,	lowerCamelCase_=False,	lowerCamelCase_=0.0,	lowerCamelCase_=3_0_1_4_5,	lowerCamelCase_=2_0_4_8,	lowerCamelCase_=1_2,	lowerCamelCase_=1_6,	lowerCamelCase_=0.1,	lowerCamelCase_=0.1,	lowerCamelCase_=True,	lowerCamelCase_=False,	lowerCamelCase_=False,	lowerCamelCase_=False,	lowerCamelCase_=1,	lowerCamelCase_=True,	lowerCamelCase_=5_1_2,	lowerCamelCase_=2_0_4_8**-0.5,	lowerCamelCase_=1e-12,	lowerCamelCase_=0.02,	lowerCamelCase_=0,	lowerCamelCase_=1,	lowerCamelCase_=2,	lowerCamelCase_=3,	lowerCamelCase_=5,	lowerCamelCase_=True,	lowerCamelCase_="first",	lowerCamelCase_=True,	lowerCamelCase_=None,	lowerCamelCase_=True,	lowerCamelCase_=0.1,	lowerCamelCase_=5,	lowerCamelCase_=5,	lowerCamelCase_=0,	lowerCamelCase_=0,	lowerCamelCase_=2,	lowerCamelCase_=0,	**lowerCamelCase_,	):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		pre_norm
       lowerCamelCase__	:     str								=		layerdrop
       lowerCamelCase__	:     Optional[Any]								=		vocab_size
       lowerCamelCase__	:     Optional[int]								=		emb_dim
       lowerCamelCase__	:     Optional[int]								=		n_layers
       lowerCamelCase__	:     str								=		n_heads
       lowerCamelCase__	:     Any								=		dropout
       lowerCamelCase__	:     Dict								=		attention_dropout
       lowerCamelCase__	:     Tuple								=		gelu_activation
       lowerCamelCase__	:     Any								=		sinusoidal_embeddings
       lowerCamelCase__	:     Tuple								=		causal
       lowerCamelCase__	:     List[Any]								=		asm
       lowerCamelCase__	:     Dict								=		n_langs
       lowerCamelCase__	:     Any								=		use_lang_emb
       lowerCamelCase__	:     Dict								=		layer_norm_eps
       lowerCamelCase__	:     Any								=		bos_index
       lowerCamelCase__	:     Dict								=		eos_index
       lowerCamelCase__	:     str								=		pad_index
       lowerCamelCase__	:     Union[str, Any]								=		unk_index
       lowerCamelCase__	:     Dict								=		mask_index
       lowerCamelCase__	:     str								=		is_encoder
       lowerCamelCase__	:     Any								=		max_position_embeddings
       lowerCamelCase__	:     Any								=		embed_init_std
       lowerCamelCase__	:     Tuple								=		init_std
       lowerCamelCase__	:     Optional[int]								=		summary_type
       lowerCamelCase__	:     List[str]								=		summary_use_proj
       lowerCamelCase__	:     Any								=		summary_activation
       lowerCamelCase__	:     Optional[Any]								=		summary_proj_to_labels
       lowerCamelCase__	:     Union[str, Any]								=		summary_first_dropout
       lowerCamelCase__	:     Tuple								=		start_n_top
       lowerCamelCase__	:     Tuple								=		end_n_top
       lowerCamelCase__	:     Dict								=		mask_token_id
       lowerCamelCase__	:     Optional[Any]								=		lang_id

       if "n_words" in kwargs:
        lowerCamelCase__	:     Tuple								=		kwargs["""n_words"""]

       super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE,	bos_token_id=_SCREAMING_SNAKE_CASE,	**_SCREAMING_SNAKE_CASE   )



class 						a_ (      _a					):







      '''simple docstring'''

      @property
      def      a__				(self   ):

       '''simple docstring'''



       if self.task == "multiple-choice":
        lowerCamelCase__	:     List[Any]								=		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase__	:     Optional[Any]								=		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]   )



'''simple docstring'''


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class     __snake_case			(    _a	):
		a_          =					["safety_checker", "feature_extractor"]




		def __init__(  self					:str			,    _SCREAMING_SNAKE_CASE					:AutoencoderKL			,    _SCREAMING_SNAKE_CASE					:CLIPTextModel			,    _SCREAMING_SNAKE_CASE					:CLIPTokenizer			,    _SCREAMING_SNAKE_CASE					:UNetaDConditionModel			,    _SCREAMING_SNAKE_CASE					:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,    _SCREAMING_SNAKE_CASE					:StableDiffusionSafetyChecker			,    _SCREAMING_SNAKE_CASE					:CLIPImageProcessor			,    _SCREAMING_SNAKE_CASE					:bool = True			,    )					->			int:






								'''simple docstring'''






								super().__init__()
								if hasattr(scheduler.config			,    """steps_offset""") and scheduler.config.steps_offset != 1:
														UpperCamelCase_     :	Optional[Any]					     =     (
														    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
														    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
														    """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
														    """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
														    """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
														    """ file"""
														)
														deprecate("""steps_offset!=1"""			,    """1.0.0"""			,    _SCREAMING_SNAKE_CASE			,    standard_warn=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Union[str, Any]					     =     dict(scheduler.config)
														UpperCamelCase_     :	Optional[Any]					     =     1
														UpperCamelCase_     :	int					     =     FrozenDict(_SCREAMING_SNAKE_CASE)

								self.register_modules(
								    vae=_SCREAMING_SNAKE_CASE			,    text_encoder=_SCREAMING_SNAKE_CASE			,    tokenizer=_SCREAMING_SNAKE_CASE			,    unet=_SCREAMING_SNAKE_CASE			,    scheduler=_SCREAMING_SNAKE_CASE			,    safety_checker=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE			,    )
								self.register_to_config(requires_safety_checker=_SCREAMING_SNAKE_CASE)




		@property
		def 	_snake_case	(  self					:Tuple)					->			Dict[str, Any]:






								'''simple docstring'''






								return {k: getattr(self			,    _SCREAMING_SNAKE_CASE) for k in self.config.keys() if not k.startswith("""_""")}




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, int]] = "auto")					->			Union[str, Any]:






								'''simple docstring'''






								if slice_size == "auto":
														# half the attention head size is usually a good trade-off between
														# speed and memory
														UpperCamelCase_     :	int					     =     self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Union[str, Any])					->			Dict:






								'''simple docstring'''






								self.enable_attention_slicing(_SCREAMING_SNAKE_CASE)




		@torch.no_grad()
		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:float = 0.8			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 5_0			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    )					->			List[str]:






								'''simple docstring'''






								return StableDiffusionInpaintPipelineLegacy(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    image=_SCREAMING_SNAKE_CASE			,    mask_image=_SCREAMING_SNAKE_CASE			,    strength=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    )




		@torch.no_grad()
		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:float = 0.8			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 5_0			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    **_SCREAMING_SNAKE_CASE					:int			,    )					->			Any:






								'''simple docstring'''






								return StableDiffusionImgaImgPipeline(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    image=_SCREAMING_SNAKE_CASE			,    strength=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    callback_steps=_SCREAMING_SNAKE_CASE			,    )





		@torch.no_grad()
		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:int = 5_1_2			,    _SCREAMING_SNAKE_CASE					:int = 5_1_2			,    _SCREAMING_SNAKE_CASE					:int = 5_0			,    _SCREAMING_SNAKE_CASE					:float = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:float = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[torch.FloatTensor] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    )					->			List[Any]:






								'''simple docstring'''






								return StableDiffusionPipeline(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    height=_SCREAMING_SNAKE_CASE			,    width=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    latents=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    callback_steps=_SCREAMING_SNAKE_CASE			,    )




