

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Any     , __lowerCamelCase					:			TransformeraDModel     , __lowerCamelCase					:			AutoencoderKL     , __lowerCamelCase					:			KarrasDiffusionSchedulers     , __lowerCamelCase					:			Optional[Dict[int, str]] = None     , ):
       super().__init__()
       self.register_modules(transformer=__lowerCamelCase     , vae=__lowerCamelCase     , scheduler=__lowerCamelCase			)

       # create a imagenet -> id dictionary for easier use
       SCREAMING_SNAKE_CASE										=   {}
       if idalabel is not None:
           for key, value in idalabel.items():
               for label in value.split(","			):
                   SCREAMING_SNAKE_CASE										=   int(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   dict(sorted(self.labels.items()			)			)





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Union[str, List[str]]			):

       if not isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   list(__lowerCamelCase			)

       for l in label:
           if l not in self.labels:
               raise ValueError(
                   f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."			)

       return [self.labels[l] for l in label]





   @torch.no_grad()
   def __call__(       self					:			str     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			float = 4.0     , __lowerCamelCase					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None     , __lowerCamelCase					:			int = 50     , __lowerCamelCase					:			Optional[str] = "pil"     , __lowerCamelCase					:			bool = True     , ):
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.transformer.config.sample_size
       SCREAMING_SNAKE_CASE										=   self.transformer.config.in_channels

       SCREAMING_SNAKE_CASE										=   randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)     , generator=__lowerCamelCase     , device=self.device     , dtype=self.transformer.dtype     , )
       SCREAMING_SNAKE_CASE										=   torch.cat([latents] * 2			) if guidance_scale > 1 else latents

       SCREAMING_SNAKE_CASE										=   torch.tensor(__lowerCamelCase     , device=self.device			).reshape(-1			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([1000] * batch_size     , device=self.device			)
       SCREAMING_SNAKE_CASE										=   torch.cat([class_labels, class_null]     , 0			) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(__lowerCamelCase			)

       for t in self.progress_bar(self.scheduler.timesteps			):
           if guidance_scale > 1:
               SCREAMING_SNAKE_CASE										=   latent_model_input[: len(__lowerCamelCase			) // 2]
               SCREAMING_SNAKE_CASE										=   torch.cat([half, half]     , dim=0			)
           SCREAMING_SNAKE_CASE										=   self.scheduler.scale_model_input(__lowerCamelCase     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   t
           if not torch.is_tensor(__lowerCamelCase			):
               # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
               # This would be a good case for the `match` statement (Python 3.10+)
               SCREAMING_SNAKE_CASE										=   latent_model_input.device.type == "mps"
               if isinstance(__lowerCamelCase     , __lowerCamelCase			):
                   SCREAMING_SNAKE_CASE										=   torch.floataa if is_mps else torch.floataa
               else:
                   SCREAMING_SNAKE_CASE										=   torch.intaa if is_mps else torch.intaa
               SCREAMING_SNAKE_CASE										=   torch.tensor([timesteps]     , dtype=__lowerCamelCase     , device=latent_model_input.device			)
           elif len(timesteps.shape			) == 0:
               SCREAMING_SNAKE_CASE										=   timesteps[None].to(latent_model_input.device			)
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           SCREAMING_SNAKE_CASE										=   timesteps.expand(latent_model_input.shape[0]			)
           # predict noise model_output
           SCREAMING_SNAKE_CASE										=   self.transformer(
               __lowerCamelCase     , timestep=__lowerCamelCase     , class_labels=__lowerCamelCase			).sample

           # perform guidance
           if guidance_scale > 1:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   torch.split(__lowerCamelCase     , len(__lowerCamelCase			) // 2     , dim=0			)

               SCREAMING_SNAKE_CASE										=   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
               SCREAMING_SNAKE_CASE										=   torch.cat([half_eps, half_eps]     , dim=0			)

               SCREAMING_SNAKE_CASE										=   torch.cat([eps, rest]     , dim=1			)

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   torch.split(__lowerCamelCase     , __lowerCamelCase     , dim=1			)
           else:
               SCREAMING_SNAKE_CASE										=   noise_pred

           # compute previous image: x_t -> x_t-1
           SCREAMING_SNAKE_CASE										=   self.scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			).prev_sample

       if guidance_scale > 1:
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   latent_model_input.chunk(2     , dim=0			)
       else:
           SCREAMING_SNAKE_CASE										=   latent_model_input

       SCREAMING_SNAKE_CASE										=   1 / self.vae.config.scaling_factor * latents
       SCREAMING_SNAKE_CASE										=   self.vae.decode(__lowerCamelCase			).sample

       SCREAMING_SNAKE_CASE										=   (samples / 2 + 0.5).clamp(0     , 1			)

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

       if output_type == "pil":
           SCREAMING_SNAKE_CASE										=   self.numpy_to_pil(__lowerCamelCase			)

       if not return_dict:
           return (samples,)

       return ImagePipelineOutput(images=__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


_lowercase		:  Tuple			       =logging.get_logger(__name__)

_lowercase		:  Any			       ={
    """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_  (  snake_case__							):
					_a      :						int						    =					'deberta-v2'



					def __init__(		self     :     Any       ,      lowerCamelCase     :     Dict=12_81_00       ,      lowerCamelCase     :     List[Any]=15_36       ,      lowerCamelCase     :     Union[str, Any]=24       ,      lowerCamelCase     :     Union[str, Any]=24       ,      lowerCamelCase     :     Optional[int]=61_44       ,      lowerCamelCase     :     Any="gelu"       ,      lowerCamelCase     :     int=0.1       ,      lowerCamelCase     :     str=0.1       ,      lowerCamelCase     :     Optional[int]=5_12       ,      lowerCamelCase     :     str=0       ,      lowerCamelCase     :     Union[str, Any]=0.02       ,      lowerCamelCase     :     List[str]=1E-7       ,      lowerCamelCase     :     Dict=False       ,      lowerCamelCase     :     List[Any]=-1       ,      lowerCamelCase     :     Union[str, Any]=0       ,      lowerCamelCase     :     Optional[int]=True       ,      lowerCamelCase     :     Optional[int]=None       ,      lowerCamelCase     :     Tuple=0       ,      lowerCamelCase     :     Tuple="gelu"       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							super().__init__(**lowerCamelCase						)

							lowerCamelCase_	:     Any 						=      hidden_size
							lowerCamelCase_	:     Dict 						=      num_hidden_layers
							lowerCamelCase_	:     Optional[int] 						=      num_attention_heads
							lowerCamelCase_	:     Optional[int] 						=      intermediate_size
							lowerCamelCase_	:     str 						=      hidden_act
							lowerCamelCase_	:     int 						=      hidden_dropout_prob
							lowerCamelCase_	:     Tuple 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     Union[str, Any] 						=      max_position_embeddings
							lowerCamelCase_	:     Union[str, Any] 						=      type_vocab_size
							lowerCamelCase_	:     List[Any] 						=      initializer_range
							lowerCamelCase_	:     Optional[Any] 						=      relative_attention
							lowerCamelCase_	:     List[Any] 						=      max_relative_positions
							lowerCamelCase_	:     Optional[int] 						=      pad_token_id
							lowerCamelCase_	:     int 						=      position_biased_input

							# Backwards compatibility
							if type(lowerCamelCase						) == str:
									lowerCamelCase_	:     Dict 						=      [x.strip() for x in pos_att_type.lower().split('|'						)]

							lowerCamelCase_	:     Optional[Any] 						=      pos_att_type
							lowerCamelCase_	:     List[Any] 						=      vocab_size
							lowerCamelCase_	:     Any 						=      layer_norm_eps

							lowerCamelCase_	:     int 						=      kwargs.get('pooler_hidden_size'       ,      lowerCamelCase						)
							lowerCamelCase_	:     Dict 						=      pooler_dropout
							lowerCamelCase_	:     Tuple 						=      pooler_hidden_act






class 						UpperCamelCase_  (  snake_case__							):



					@property
					def   __a     (		self     :     Optional[int]						):
							if self.task == "multiple-choice":
									lowerCamelCase_	:     Optional[Any] 						=      {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
									lowerCamelCase_	:     Any 						=      {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   __a     (		self     :     List[Any]						):
							return 12





					def   __a     (		self     :     Tuple       ,      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       ,      ):
							lowerCamelCase_	:     Union[str, Any] 						=      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
