



'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


lowercase											=      logging.get_logger(__name__)

lowercase											=      {
    '''google/electra-small-generator''': '''https://huggingface.co/google/electra-small-generator/resolve/main/config.json''',
    '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/config.json''',
    '''google/electra-large-generator''': '''https://huggingface.co/google/electra-large-generator/resolve/main/config.json''',
    '''google/electra-small-discriminator''': (
        '''https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-base-discriminator''': (
        '''https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-large-discriminator''': (
        '''https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'''
    ),
}







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			str	=   "electra"




 def __init__( self				,	lowerCAmelCase_=3_0_5_2_2				,	lowerCAmelCase_=1_2_8				,	lowerCAmelCase_=2_5_6				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=4				,	lowerCAmelCase_=1_0_2_4				,	lowerCAmelCase_="gelu"				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=5_1_2				,	lowerCAmelCase_=2				,	lowerCAmelCase_=0.0_2				,	lowerCAmelCase_=1e-12				,	lowerCAmelCase_="first"				,	lowerCAmelCase_=True				,	lowerCAmelCase_="gelu"				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0				,	lowerCAmelCase_="absolute"				,	lowerCAmelCase_=True				,	lowerCAmelCase_=None				,	**lowerCAmelCase_				,	)			->   Any:



      """simple docstring"""







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

      a_									=vocab_size
      a_									=embedding_size
      a_									=hidden_size
      a_									=num_hidden_layers
      a_									=num_attention_heads
      a_									=intermediate_size
      a_									=hidden_act
      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_									=summary_type
      a_									=summary_use_proj
      a_									=summary_activation
      a_									=summary_last_dropout
      a_									=position_embedding_type
      a_									=use_cache
      a_									=classifier_dropout







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 @property
 def    lowercase_	( self)			->   Tuple:



      """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),
          ])




from typing import List, Optional, Tuple, Union

import torch

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







class 	A__				(	__magic_name__      ):






				def __init__(		self   : Any       ,	a   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a       ,	scheduler=a  )






				@torch.no_grad()
				def __call__(		self   : Optional[Any]       ,	a   : int = 1       ,	a   : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	a   : int = 1_000       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	):






							'''simple docstring'''







							if isinstance(self.unet.config.sample_size       ,	a  ):
										lowerCAmelCase__ : Union[str, Any]   =						(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										lowerCAmelCase__ : str   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										lowerCAmelCase__ : Optional[int]   =						randn_tensor(a       ,	generator=a  )
										lowerCAmelCase__ : Optional[int]   =						image.to(self.device  )
							else:
										lowerCAmelCase__ : Dict   =						randn_tensor(a       ,	generator=a       ,	device=self.device  )

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

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

										# 2. compute previous image: x_t -> x_t-1
										lowerCAmelCase__ : Optional[Any]   =						self.scheduler.step(a       ,	a       ,	a       ,	generator=a  ).prev_sample

							lowerCAmelCase__ : str   =						(image / 2 + 0.5).clamp(0       ,	1  )
							lowerCAmelCase__ : Optional[int]   =						image.cpu().permute(0       ,	2       ,	3       ,	1  ).numpy()
							if output_type == "pil":
										lowerCAmelCase__ : int   =						self.numpy_to_pil(a  )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a  )