



from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase         =	logging.get_logger(__name__)

_lowerCamelCase         =	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      """mega"""





     def __init__(							self			:				Dict  ,	UpperCamelCase			:				str=3_0_5_2_2  ,	UpperCamelCase			:				Tuple=1_2_8  ,	UpperCamelCase			:				List[Any]=4  ,	UpperCamelCase			:				Optional[int]=2_5_6  ,	UpperCamelCase			:				List[Any]=1_6  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Tuple=6_4  ,	UpperCamelCase			:				Dict=False  ,	UpperCamelCase			:				List[Any]=-1  ,	UpperCamelCase			:				Any=None  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				Optional[Any]="scalenorm"  ,	UpperCamelCase			:				str=True  ,	UpperCamelCase			:				List[str]="silu"  ,	UpperCamelCase			:				int="softmax"  ,	UpperCamelCase			:				Dict=0.1  ,	UpperCamelCase			:				Tuple=0.1  ,	UpperCamelCase			:				List[str]=0.1  ,	UpperCamelCase			:				Optional[int]=False  ,	UpperCamelCase			:				Tuple=True  ,	UpperCamelCase			:				Optional[int]=2_5_6  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				Optional[int]=0.1  ,	UpperCamelCase			:				Optional[Any]=2_0_4_8  ,	UpperCamelCase			:				Optional[Any]=False  ,	UpperCamelCase			:				Optional[Any]=2  ,	UpperCamelCase			:				Optional[Any]=0.0_2  ,	UpperCamelCase			:				List[str]=0.2  ,	UpperCamelCase			:				int=0.0_2  ,	UpperCamelCase			:				Tuple=1.0  ,	UpperCamelCase			:				List[str]=1  ,	UpperCamelCase			:				Optional[Any]=0  ,	UpperCamelCase			:				Optional[Any]=2  ,	UpperCamelCase			:				Union[str, Any]="rotary"  ,	UpperCamelCase			:				Optional[int]=None  ,	UpperCamelCase			:				int=True  ,	UpperCamelCase			:				Union[str, Any]=True  ,	**UpperCamelCase			:				Tuple  ,	)->Dict:
      super().__init__(pad_token_id=UpperCamelCase  ,	bos_token_id=UpperCamelCase  ,	eos_token_id=UpperCamelCase  ,	**UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						vocab_size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						hidden_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						num_hidden_layers
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						activation
      __SCREAMING_SNAKE_CASE							:     Tuple							=						attention_activation
      __SCREAMING_SNAKE_CASE							:     Any							=						intermediate_size
      __SCREAMING_SNAKE_CASE							:     int							=						ema_projection_size
      __SCREAMING_SNAKE_CASE							:     int							=						bidirectional
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						shared_representation_size
      __SCREAMING_SNAKE_CASE							:     List[str]							=						use_chunking
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						chunk_size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						truncation
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						normalize_before_mega
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						normalization_type
      __SCREAMING_SNAKE_CASE							:     Tuple							=						norm_affine
      __SCREAMING_SNAKE_CASE							:     str							=						dropout_prob
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     List[str]							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						use_feature_dropout
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						use_normalized_ffn
      __SCREAMING_SNAKE_CASE							:     str							=						nffn_hidden_size
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						normalize_before_ffn
      __SCREAMING_SNAKE_CASE							:     Any							=						nffn_activation_dropout_prob
      __SCREAMING_SNAKE_CASE							:     List[str]							=						max_positions
      __SCREAMING_SNAKE_CASE							:     List[str]							=						add_token_type_embeddings
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						type_vocab_size
      __SCREAMING_SNAKE_CASE							:     Dict							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     Tuple							=						ema_delta_alpha_range
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						ema_beta_range
      __SCREAMING_SNAKE_CASE							:     Tuple							=						ema_gamma_omega_range
      __SCREAMING_SNAKE_CASE							:     Any							=						relative_positional_bias
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						use_cache
      __SCREAMING_SNAKE_CASE							:     int							=						classifier_dropout
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						add_lm_hidden_dense_layer
      __SCREAMING_SNAKE_CASE							:     Tuple							=						1  # not used but required by Hugging Face






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     @property
     def 						__snake_case     (							self			:				Tuple					)->Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						{0: "batch", 1: "choice", 2: "sequence"}
      else:
       __SCREAMING_SNAKE_CASE							:     str							=						{0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", 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  )