



"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 			__snake_case			(      _lowercase):




  def __init__(					self	:		int		,   __lowerCAmelCase	:		Tuple		,   __lowerCAmelCase	:		str						):






         """simple docstring"""






         super().__init__()

         # make sure scheduler can always be converted to DDIM
         _lowerCamelCase		:							Union[str, Any]            =					DDIMScheduler.from_config(scheduler.config						)

         self.register_modules(unet=__lowerCAmelCase		,   scheduler=__lowerCAmelCase						)




  @torch.no_grad()
  def __call__(					self	:		Dict		,   __lowerCAmelCase	:		int = 1		,   __lowerCAmelCase	:		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,   __lowerCAmelCase	:		float = 0.0		,   __lowerCAmelCase	:		int = 5_0		,   __lowerCAmelCase	:		Optional[bool] = None		,   __lowerCAmelCase	:		Optional[str] = "pil"		,   __lowerCAmelCase	:		bool = True		,   ):






         """simple docstring"""






         if isinstance(self.unet.config.sample_size		,   __lowerCAmelCase						):
                _lowerCamelCase		:							Tuple            =					(
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size,
                    self.unet.config.sample_size,
                )
         else:
                _lowerCamelCase		:							List[Any]            =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if isinstance(__lowerCAmelCase		,   __lowerCAmelCase						) and len(__lowerCAmelCase						) != batch_size:
                raise ValueError(
                    f'''You have passed a list of generators of length {len(__lowerCAmelCase						)}, but requested an effective batch'''
                    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

         _lowerCamelCase		:							Tuple            =					randn_tensor(__lowerCAmelCase		,   generator=__lowerCAmelCase		,   device=self.device		,   dtype=self.unet.dtype						)

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

         for t in self.progress_bar(self.scheduler.timesteps						):
                # 1. predict noise model_output
                _lowerCamelCase		:							Tuple            =					self.unet(__lowerCAmelCase		,   __lowerCAmelCase						).sample

                # 2. predict previous mean of image x_t-1 and add variance depending on eta
                # eta corresponds to η in paper and should be between [0, 1]
                # do x_t -> x_t-1
                _lowerCamelCase		:							Union[str, Any]            =					self.scheduler.step(
                    __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   eta=__lowerCAmelCase		,   use_clipped_model_output=__lowerCAmelCase		,   generator=__lowerCAmelCase						).prev_sample

         _lowerCamelCase		:							Dict            =					(image / 2 + 0.5).clamp(0		,   1						)
         _lowerCamelCase		:							str            =					image.cpu().permute(0		,   2		,   3		,   1						).numpy()
         if output_type == "pil":
                _lowerCamelCase		:							Tuple            =					self.numpy_to_pil(__lowerCAmelCase						)

         if not return_dict:
                return (image,)

         return ImagePipelineOutput(images=__lowerCAmelCase						)









"""simple docstring"""





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


__A											=						logging.get_logger(__name__)

__A											=						{
    """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    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   """deberta-v2"""






       def __init__(       self       ,					__UpperCAmelCase=1_2_8_1_0_0       ,					__UpperCAmelCase=1_5_3_6       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=6_1_4_4       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=1E-7       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=-1       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase="gelu"       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )

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

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

         lowerCAmelCase__     :str											=				pos_att_type
         lowerCAmelCase__     :Dict											=				vocab_size
         lowerCAmelCase__     :Tuple											=				layer_norm_eps

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('pooler_hidden_size'       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				pooler_dropout
         lowerCAmelCase__     :int											=				pooler_hidden_act






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self.task == "multiple-choice":
           lowerCAmelCase__     :str											=				{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           lowerCAmelCase__     :Tuple											=				{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 	snake_case		(       self ):



         '''simple docstring'''

         return 1_2






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				super().generate_dummy_inputs(preprocessor=__UpperCAmelCase       ,					framework=__UpperCAmelCase )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
         return dummy_inputs
