
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_snake_case      :							Optional[int]     	=    logging.get_logger(__name__)  # pylint: disable=invalid-name





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Any		,    lowerCAmelCase__							:AutoencoderKL		,    lowerCAmelCase__							:CLIPTextModel		,    lowerCAmelCase__							:CLIPTokenizer		,    lowerCAmelCase__							:UNetaDConditionModel		,    lowerCAmelCase__							:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,    lowerCAmelCase__							:StableDiffusionSafetyChecker		,    lowerCAmelCase__							:CLIPImageProcessor		,    )  ->Tuple:
										super().__init__()
										self.register_modules(
										    vae=lowerCAmelCase__		,    text_encoder=lowerCAmelCase__		,    tokenizer=lowerCAmelCase__		,    unet=lowerCAmelCase__		,    scheduler=lowerCAmelCase__		,    safety_checker=lowerCAmelCase__		,    feature_extractor=lowerCAmelCase__		,    )





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





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->str:
										self.enable_attention_slicing(lowerCAmelCase__     )





						@torch.no_grad()
						def __call__(			self							:Dict		,    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		,    lowerCAmelCase__							:Optional[torch.FloatTensor] = None		,    **lowerCAmelCase__							:int		,    )  ->List[Any]:

										if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      1
										elif isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      len(lowerCAmelCase__     )
										else:
														raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__     )}'''     )

										if height % 8 != 0 or width % 8 != 0:
														raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''     )

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) or callback_steps <= 0)
										):
														raise ValueError(
														    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
														    F''' {type(lowerCAmelCase__     )}.'''     )

										# get prompt text embeddings
										lowercase         =      self.tokenizer(
										    lowerCAmelCase__		,    padding="max_length"		,    max_length=self.tokenizer.model_max_length		,    return_tensors="pt"		,    )
										lowercase         =      text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														lowercase         =      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]     )
														logger.warning(
														    "The following part of your input was truncated because CLIP can only handle sequences up to"
														    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )
														lowercase         =      text_input_ids[:, : self.tokenizer.model_max_length]

										if text_embeddings is None:
														lowercase         =      self.text_encoder(text_input_ids.to(self.device     )     )[0]

										# duplicate text embeddings for each generation per prompt, using mps friendly method
										lowercase     ,	lowercase     ,	lowercase         =      text_embeddings.shape
										lowercase         =      text_embeddings.repeat(1		,    lowerCAmelCase__		,    1     )
										lowercase         =      text_embeddings.view(bs_embed * num_images_per_prompt		,    lowerCAmelCase__		,    -1     )

										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										lowercase         =      guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
														lowercase         =      42
														if negative_prompt is None:
																		lowercase         =      [""]
														elif type(lowerCAmelCase__     ) is not type(lowerCAmelCase__     ):
																		raise TypeError(
																		    F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__     )} !='''
																		    F''' {type(lowerCAmelCase__     )}.'''     )
														elif isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ):
																		lowercase         =      [negative_prompt]
														elif batch_size != len(lowerCAmelCase__     ):
																		raise ValueError(
																		    F'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__     )}, but `prompt`:'''
																		    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																		    " the batch size of `prompt`."     )
														else:
																		lowercase         =      negative_prompt

														lowercase         =      text_input_ids.shape[-1]
														lowercase         =      self.tokenizer(
														    lowerCAmelCase__		,    padding="max_length"		,    max_length=lowerCAmelCase__		,    truncation=lowerCAmelCase__		,    return_tensors="pt"		,    )
														lowercase         =      self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														lowercase         =      uncond_embeddings.shape[1]
														lowercase         =      uncond_embeddings.repeat(lowerCAmelCase__		,    lowerCAmelCase__		,    1     )
														lowercase         =      uncond_embeddings.view(batch_size * num_images_per_prompt		,    lowerCAmelCase__		,    -1     )

														# For classifier free guidance, we need to do two forward passes.
														# Here we concatenate the unconditional and text embeddings into a single batch
														# to avoid doing two forward passes
														lowercase         =      torch.cat([uncond_embeddings, text_embeddings]     )

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										lowercase         =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										lowercase         =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
										lowercase         =      text_embeddings.dtype
										if latents is None:
														if self.device.type == "mps":
																		# randn does not exist on mps
																		lowercase         =      torch.randn(
																		    lowerCAmelCase__		,    generator=lowerCAmelCase__		,    device="cpu"		,    dtype=lowerCAmelCase__     ).to(self.device     )
																		lowercase         =      torch.randn(lowerCAmelCase__		,    generator=lowerCAmelCase__		,    device="cpu"		,    dtype=lowerCAmelCase__     ).to(
																		    self.device     )
														else:
																		lowercase         =      torch.randn(
																		    lowerCAmelCase__		,    generator=lowerCAmelCase__		,    device=self.device		,    dtype=lowerCAmelCase__     )
																		lowercase         =      torch.randn(lowerCAmelCase__		,    generator=lowerCAmelCase__		,    device=self.device		,    dtype=lowerCAmelCase__     )
										else:
														if latents_reference.shape != latents_shape:
																		raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
														lowercase         =      latents_reference.to(self.device     )
														lowercase         =      latents.to(self.device     )

										# This is the key part of the pipeline where we
										# try to ensure that the generated images w/ the same seed
										# but different sizes actually result in similar images
										lowercase         =      (latents_shape[3] - latents_shape_reference[3]) // 2
										lowercase         =      (latents_shape[2] - latents_shape_reference[2]) // 2
										lowercase         =      latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
										lowercase         =      latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
										lowercase         =      0 if dx < 0 else dx
										lowercase         =      0 if dy < 0 else dy
										lowercase         =      max(-dx		,    0     )
										lowercase         =      max(-dy		,    0     )
										# import pdb
										# pdb.set_trace()
										lowercase         =      latents_reference[:, :, dy : dy + h, dx : dx + w]

										# set timesteps
										self.scheduler.set_timesteps(lowerCAmelCase__     )

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										lowercase         =      self.scheduler.timesteps.to(self.device     )

										# scale the initial noise by the standard deviation required by the scheduler
										lowercase         =      latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										lowercase         =      "eta" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
										lowercase         =      {}
										if accepts_eta:
														lowercase         =      eta

										for i, t in enumerate(self.progress_bar(lowerCAmelCase__     )     ):
														# expand the latents if we are doing classifier free guidance
														lowercase         =      torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
														lowercase         =      self.scheduler.scale_model_input(lowerCAmelCase__		,    lowerCAmelCase__     )

														# predict the noise residual
														lowercase         =      self.unet(lowerCAmelCase__		,    lowerCAmelCase__		,    encoder_hidden_states=lowerCAmelCase__     ).sample

														# perform guidance
														if do_classifier_free_guidance:
																		lowercase     ,	lowercase         =      noise_pred.chunk(2     )
																		lowercase         =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														# compute the previous noisy sample x_t -> x_t-1
														lowercase         =      self.scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    **lowerCAmelCase__     ).prev_sample

														# call the callback, if provided
														if callback is not None and i % callback_steps == 0:
																		callback(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      1 / 0.1_82_15 * latents
										lowercase         =      self.vae.decode(lowerCAmelCase__     ).sample

										lowercase         =      (image / 2 + 0.5).clamp(0		,    1     )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										lowercase         =      image.cpu().permute(0		,    2		,    3		,    1     ).float().numpy()

										if self.safety_checker is not None:
														lowercase         =      self.feature_extractor(self.numpy_to_pil(lowerCAmelCase__     )		,    return_tensors="pt"     ).to(
														    self.device     )
														lowercase     ,	lowercase         =      self.safety_checker(
														    images=lowerCAmelCase__		,    clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype     )     )
										else:
														lowercase         =      None

										if output_type == "pil":
														lowercase         =      self.numpy_to_pil(lowerCAmelCase__     )

										if not return_dict:
														return (image, has_nsfw_concept)

										return StableDiffusionPipelineOutput(images=lowerCAmelCase__		,    nsfw_content_detected=lowerCAmelCase__     )


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_snake_case      :							Dict     	=    logging.get_logger(__name__)

_snake_case      :							List[str]     	=    {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					'''deberta-v2'''





						def __init__(			self							:int		,    lowerCAmelCase__							:Union[str, Any]=128100		,    lowerCAmelCase__							:Optional[Any]=1536		,    lowerCAmelCase__							:Tuple=24		,    lowerCAmelCase__							:List[Any]=24		,    lowerCAmelCase__							:Union[str, Any]=6144		,    lowerCAmelCase__							:List[str]="gelu"		,    lowerCAmelCase__							:Union[str, Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:List[str]=512		,    lowerCAmelCase__							:Optional[int]=0		,    lowerCAmelCase__							:Optional[int]=0.02		,    lowerCAmelCase__							:int=1E-7		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:List[Any]=-1		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:List[Any]=0		,    lowerCAmelCase__							:Union[str, Any]="gelu"		,    **lowerCAmelCase__							:str		,    )  ->Optional[Any]:
										super().__init__(**lowerCAmelCase__     )

										lowercase         =      hidden_size
										lowercase         =      num_hidden_layers
										lowercase         =      num_attention_heads
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      hidden_dropout_prob
										lowercase         =      attention_probs_dropout_prob
										lowercase         =      max_position_embeddings
										lowercase         =      type_vocab_size
										lowercase         =      initializer_range
										lowercase         =      relative_attention
										lowercase         =      max_relative_positions
										lowercase         =      pad_token_id
										lowercase         =      position_biased_input

										# Backwards compatibility
										if type(lowerCAmelCase__     ) == str:
														lowercase         =      [x.strip() for x in pos_att_type.lower().split("|"     )]

										lowercase         =      pos_att_type
										lowercase         =      vocab_size
										lowercase         =      layer_norm_eps

										lowercase         =      kwargs.get("pooler_hidden_size"		,    lowerCAmelCase__     )
										lowercase         =      pooler_dropout
										lowercase         =      pooler_hidden_act





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
														lowercase         =      {0: "batch", 1: "choice", 2: "sequence"}
										else:
														lowercase         =      {0: "batch", 1: "sequence"}
										if self._config.type_vocab_size > 0:
														return OrderedDict(
														    [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]     )
										else:
														return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										return 12





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional["TensorType"] = None		,    lowerCAmelCase__							:int = 3		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:"PreTrainedTokenizerBase" = None		,    )  ->Mapping[str, Any]:
										lowercase         =      super().generate_dummy_inputs(preprocessor=lowerCAmelCase__		,    framework=lowerCAmelCase__     )
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
														del dummy_inputs["token_type_ids"]
										return dummy_inputs
