



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Optional[int]	    =     "big_bird"


	def __init__(self : Optional[Any],					__UpperCAmelCase : Optional[int]=50358,					__UpperCAmelCase : Optional[int]=768,					__UpperCAmelCase : Dict=12,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : List[Any]=3072,					__UpperCAmelCase : Union[str, Any]="gelu_new",					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Union[str, Any]=4096,					__UpperCAmelCase : Optional[Any]=2,					__UpperCAmelCase : Dict=0.02,					__UpperCAmelCase : str=1e-12,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : List[str]=0,					__UpperCAmelCase : List[str]=1,					__UpperCAmelCase : str=2,					__UpperCAmelCase : Any=66,					__UpperCAmelCase : Optional[int]="block_sparse",					__UpperCAmelCase : str=True,					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : Optional[Any]=64,					__UpperCAmelCase : Tuple=3,					__UpperCAmelCase : Optional[Any]=None,					**__UpperCAmelCase : str,					)     ->			List[Any]:

					"""simple docstring"""

					super().__init__(
					    pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					sep_token_id=__UpperCAmelCase,					**__UpperCAmelCase,					)

					SCREAMING_SNAKE_CASE  :						str      					= vocab_size
					SCREAMING_SNAKE_CASE  :						List[Any]      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						str      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						int      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						Dict      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Tuple      					= type_vocab_size
					SCREAMING_SNAKE_CASE  :						Any      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						List[Any]      					= use_cache

					SCREAMING_SNAKE_CASE  :						Dict      					= rescale_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= attention_type
					SCREAMING_SNAKE_CASE  :						int      					= use_bias
					SCREAMING_SNAKE_CASE  :						List[Any]      					= block_size
					SCREAMING_SNAKE_CASE  :						str      					= num_random_blocks
					SCREAMING_SNAKE_CASE  :						List[Any]      					= classifier_dropout



class 	a__     (				_lowercase	):


	@property
	def 				lowercase__  (self : Tuple      )     ->			Mapping[str, Mapping[int, str]]:

					"""simple docstring"""

					if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
									SCREAMING_SNAKE_CASE  :						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 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

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






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

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

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




