

"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)

__UpperCamelCase    : int				 =						{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"distilbert"
					lowercase__												=			{
					    "hidden_size": "dim",
					    "num_attention_heads": "n_heads",
					    "num_hidden_layers": "n_layers",
					}







					def __init__(			self     :							str  ,lowercase_     :							Any=3_0_5_2_2  ,lowercase_     :							int=5_1_2  ,lowercase_     :							Dict=False  ,lowercase_     :							List[Any]=6  ,lowercase_     :							str=1_2  ,lowercase_     :							Dict=7_6_8  ,lowercase_     :							Optional[Any]=4 * 7_6_8  ,lowercase_     :							Tuple=0.1  ,lowercase_     :							int=0.1  ,lowercase_     :							Any="gelu"  ,lowercase_     :							List[str]=0.02  ,lowercase_     :							Optional[int]=0.1  ,lowercase_     :							List[str]=0.2  ,lowercase_     :							int=0  ,**lowercase_     :							str  ,):
						lowerCAmelCase__      :		int    							=      vocab_size
						lowerCAmelCase__      :		Dict    							=      max_position_embeddings
						lowerCAmelCase__      :		List[Any]    							=      sinusoidal_pos_embds
						lowerCAmelCase__      :		str    							=      n_layers
						lowerCAmelCase__      :		Any    							=      n_heads
						lowerCAmelCase__      :		Tuple    							=      dim
						lowerCAmelCase__      :		Tuple    							=      hidden_dim
						lowerCAmelCase__      :		Dict    							=      dropout
						lowerCAmelCase__      :		Dict    							=      attention_dropout
						lowerCAmelCase__      :		int    							=      activation
						lowerCAmelCase__      :		List[Any]    							=      initializer_range
						lowerCAmelCase__      :		Tuple    							=      qa_dropout
						lowerCAmelCase__      :		Optional[Any]    							=      seq_classif_dropout
						super().__init__(**lowercase_  ,pad_token_id=lowercase_						)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						if self.task == "multiple-choice":
							lowerCAmelCase__      :		List[Any]    							=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
							lowerCAmelCase__      :		List[Any]    							=      {0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						    ]						)







"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42
					lowercase__												=			42







					def __init__(			self     :							int  ,lowercase_     :							UNetaDModel  ,lowercase_     :							ScoreSdeVeScheduler						):
						super().__init__()
						self.register_modules(unet=lowercase_  ,scheduler=lowercase_						)







					@torch.no_grad()
					def __call__(			self     :							List[str]  ,lowercase_     :							int = 1  ,lowercase_     :							int = 2_0_0_0  ,lowercase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowercase_     :							Optional[str] = "pil"  ,lowercase_     :							bool = True  ,**lowercase_     :							Dict  ,):
						lowerCAmelCase__      :		str    							=      self.unet.config.sample_size
						lowerCAmelCase__      :		int    							=      (batch_size, 3, img_size, img_size)

						lowerCAmelCase__      :		List[Any]    							=      self.unet

						lowerCAmelCase__      :		Tuple    							=      randn_tensor(lowercase_  ,generator=lowercase_						) * self.scheduler.init_noise_sigma
						lowerCAmelCase__      :		List[Any]    							=      sample.to(self.device						)

						self.scheduler.set_timesteps(lowercase_						)
						self.scheduler.set_sigmas(lowercase_						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
							lowerCAmelCase__      :		Tuple    							=      self.scheduler.sigmas[i] * torch.ones(shape[0]  ,device=self.device						)

							# correction step
							for _ in range(self.scheduler.config.correct_steps						):
								lowerCAmelCase__      :		str    							=      self.unet(lowercase_  ,lowercase_						).sample
								lowerCAmelCase__      :		List[str]    							=      self.scheduler.step_correct(lowercase_  ,lowercase_  ,generator=lowercase_						).prev_sample

							# prediction step
							lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,lowercase_						).sample
							lowerCAmelCase__      :		Optional[int]    							=      self.scheduler.step_pred(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)

							lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      output.prev_sample, output.prev_sample_mean

						lowerCAmelCase__      :		List[str]    							=      sample_mean.clamp(0  ,1						)
						lowerCAmelCase__      :		Union[str, Any]    							=      sample.cpu().permute(0  ,2  ,3  ,1						).numpy()
						if output_type == "pil":
							lowerCAmelCase__      :		Union[str, Any]    							=      self.numpy_to_pil(lowercase_						)

						if not return_dict:
							return (sample,)

						return ImagePipelineOutput(images=lowercase_						)




