



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


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







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Optional[Any]       =						["safety_checker", "feature_extractor"]


   def __init__(    self:     Tuple  ,lowerCamelCase_:     AutoencoderKL  ,lowerCamelCase_:     CLIPTextModel  ,lowerCamelCase_:     CLIPTokenizer  ,lowerCamelCase_:     UNetaDConditionModel  ,lowerCamelCase_:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,lowerCamelCase_:     StableDiffusionSafetyChecker  ,lowerCamelCase_:     CLIPImageProcessor  ,lowerCamelCase_:     bool = True  ,)	->      int:
       super().__init__()
       if hasattr(scheduler.config  ,"""steps_offset"""							) and scheduler.config.steps_offset != 1:
           UpperCAmelCase_     :       Optional[int]  		=					(
               F'''The configuration file of this scheduler: {scheduler} is outdated.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"""  ,lowerCamelCase_  ,standard_warn=lowerCamelCase_							)
           UpperCAmelCase_     :       int  		=					dict(scheduler.config							)
           UpperCAmelCase_     :       Any  		=					1
           UpperCAmelCase_     :       str  		=					FrozenDict(lowerCamelCase_							)

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


   @property
   def       A__    (    self:     int							)	->      Dict[str, Any]:
       return {k: getattr(self  ,lowerCamelCase_							) for k in self.config.keys() if not k.startswith("""_"""							)}


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Optional[Union[str, int]] = "auto"							)	->      Dict:
       if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           UpperCAmelCase_     :       Dict  		=					self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowerCamelCase_							)


   def       A__    (    self:     List[str]							)	->      Tuple:
       self.enable_attention_slicing(lowerCamelCase_							)


   @torch.no_grad()
   def       A__    (    self:     str  ,lowerCamelCase_:     Union[str, List[str]]  ,lowerCamelCase_:     Union[torch.FloatTensor, PIL.Image.Image]  ,lowerCamelCase_:     Union[torch.FloatTensor, PIL.Image.Image]  ,lowerCamelCase_:     float = 0.8  ,lowerCamelCase_:     Optional[int] = 50  ,lowerCamelCase_:     Optional[float] = 7.5  ,lowerCamelCase_:     Optional[Union[str, List[str]]] = None  ,lowerCamelCase_:     Optional[int] = 1  ,lowerCamelCase_:     Optional[float] = 0.0  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,lowerCamelCase_:     Optional[str] = "pil"  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,lowerCamelCase_:     int = 1  ,)	->      Optional[int]:
       # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
       return StableDiffusionInpaintPipelineLegacy(**self.components							)(
           prompt=lowerCamelCase_  ,image=lowerCamelCase_  ,mask_image=lowerCamelCase_  ,strength=lowerCamelCase_  ,num_inference_steps=lowerCamelCase_  ,guidance_scale=lowerCamelCase_  ,negative_prompt=lowerCamelCase_  ,num_images_per_prompt=lowerCamelCase_  ,eta=lowerCamelCase_  ,generator=lowerCamelCase_  ,output_type=lowerCamelCase_  ,return_dict=lowerCamelCase_  ,callback=lowerCamelCase_  ,)


   @torch.no_grad()
   def       A__    (    self:     Any  ,lowerCamelCase_:     Union[str, List[str]]  ,lowerCamelCase_:     Union[torch.FloatTensor, PIL.Image.Image]  ,lowerCamelCase_:     float = 0.8  ,lowerCamelCase_:     Optional[int] = 50  ,lowerCamelCase_:     Optional[float] = 7.5  ,lowerCamelCase_:     Optional[Union[str, List[str]]] = None  ,lowerCamelCase_:     Optional[int] = 1  ,lowerCamelCase_:     Optional[float] = 0.0  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,lowerCamelCase_:     Optional[str] = "pil"  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,lowerCamelCase_:     int = 1  ,**lowerCamelCase_:     List[str]  ,)	->      List[str]:
       # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
       return StableDiffusionImgaImgPipeline(**self.components							)(
           prompt=lowerCamelCase_  ,image=lowerCamelCase_  ,strength=lowerCamelCase_  ,num_inference_steps=lowerCamelCase_  ,guidance_scale=lowerCamelCase_  ,negative_prompt=lowerCamelCase_  ,num_images_per_prompt=lowerCamelCase_  ,eta=lowerCamelCase_  ,generator=lowerCamelCase_  ,output_type=lowerCamelCase_  ,return_dict=lowerCamelCase_  ,callback=lowerCamelCase_  ,callback_steps=lowerCamelCase_  ,)






   @torch.no_grad()
   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Union[str, List[str]]  ,lowerCamelCase_:     int = 512  ,lowerCamelCase_:     int = 512  ,lowerCamelCase_:     int = 50  ,lowerCamelCase_:     float = 7.5  ,lowerCamelCase_:     Optional[Union[str, List[str]]] = None  ,lowerCamelCase_:     Optional[int] = 1  ,lowerCamelCase_:     float = 0.0  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,lowerCamelCase_:     Optional[torch.FloatTensor] = None  ,lowerCamelCase_:     Optional[str] = "pil"  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,lowerCamelCase_:     int = 1  ,)	->      Tuple:
       # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
       return StableDiffusionPipeline(**self.components							)(
           prompt=lowerCamelCase_  ,height=lowerCamelCase_  ,width=lowerCamelCase_  ,num_inference_steps=lowerCamelCase_  ,guidance_scale=lowerCamelCase_  ,negative_prompt=lowerCamelCase_  ,num_images_per_prompt=lowerCamelCase_  ,eta=lowerCamelCase_  ,generator=lowerCamelCase_  ,latents=lowerCamelCase_  ,output_type=lowerCamelCase_  ,return_dict=lowerCamelCase_  ,callback=lowerCamelCase_  ,callback_steps=lowerCamelCase_  ,)





from collections import OrderedDict
from typing import Mapping

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


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						super().__init__(pad_token_id=lowerCamelCase_						,			**lowerCamelCase_						)

						a   	=  vocab_size
						a   	=  hidden_size
						a   	=  num_hidden_layers
						a   	=  num_attention_heads
						a   	=  hidden_act
						a   	=  intermediate_size
						a   	=  hidden_dropout_prob
						a   	=  attention_probs_dropout_prob
						a   	=  max_position_embeddings
						a   	=  type_vocab_size
						a   	=  initializer_range
						a   	=  layer_norm_eps
						a   	=  embedding_size
						a   	=  trigram_input
						a   	=  use_bottleneck
						a   	=  intra_bottleneck_size
						a   	=  use_bottleneck_attention
						a   	=  key_query_shared_bottleneck
						a   	=  num_feedforward_networks
						a   	=  normalization_type
						a   	=  classifier_activation

						if self.use_bottleneck:
											a   	=  intra_bottleneck_size
						else:
											a   	=  hidden_size

						a   	=  classifier_dropout




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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