
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
					from ... import PreTrainedTokenizerBase, TensorType

lowerCamelCase__          =		logging.get_logger(__name__)







class      A__							(     __magic_name__	):
				lowercase			    =  'vision-encoder-decoder'
				lowercase			    =  True






				def __init__(						self					:      List[str]    ,     **a					:      str   ):






							'''simple docstring'''







							super().__init__(**a   )
							if "encoder" not in kwargs or "decoder" not in kwargs:
										raise ValueError(
										    f'''A configuraton of type {self.model_type} cannot be instantiated because '''
										    f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''   )

							lowerCAmelCase__      :      Tuple         =		kwargs.pop('encoder'   )
							lowerCAmelCase__      :      Dict         =		encoder_config.pop('model_type'   )
							lowerCAmelCase__      :      Any         =		kwargs.pop('decoder'   )
							lowerCAmelCase__      :      Any         =		decoder_config.pop('model_type'   )

							lowerCAmelCase__      :      Union[str, Any]         =		AutoConfig.for_model(a    ,     **a   )
							lowerCAmelCase__      :      Optional[int]         =		AutoConfig.for_model(a    ,     **a   )
							lowerCAmelCase__      :      List[Any]         =		True






				@classmethod
				def 							_lowerCamelCase		(						cls					:      int    ,     a					:      PretrainedConfig    ,     a					:      PretrainedConfig    ,     **a					:      List[Any]   ):






							'''simple docstring'''







							logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'   )
							lowerCAmelCase__      :      Any         =		True
							lowerCAmelCase__      :      List[str]         =		True

							return cls(encoder=encoder_config.to_dict()    ,     decoder=decoder_config.to_dict()    ,     **a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		copy.deepcopy(self.__dict__   )
							lowerCAmelCase__      :      str         =		self.encoder.to_dict()
							lowerCAmelCase__      :      Optional[int]         =		self.decoder.to_dict()
							lowerCAmelCase__      :      Optional[Any]         =		self.__class__.model_type
							return output







class      A__							(     __magic_name__	):
				lowercase			    =  version.parse('1.11'	)






				@property
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]   )






				@property
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return 1E-4






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}   )







class      A__							(     __magic_name__	):






				@property
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		OrderedDict()
							lowerCAmelCase__      :      Optional[int]         =		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							lowerCAmelCase__      :      Optional[Any]         =		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							lowerCAmelCase__      :      Dict         =		{0: 'batch', 1: 'encoder_sequence'}

							return common_inputs






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      "PreTrainedTokenizerBase"    ,     a					:      int = -1    ,     a					:      int = -1    ,     a					:      bool = False    ,     a					:      Optional["TensorType"] = None    ,     ):






							'''simple docstring'''







							import torch

							lowerCAmelCase__      :      List[str]         =		OrderedDict()

							lowerCAmelCase__      :      Any         =		super().generate_dummy_inputs(
							    a    ,     batch_size=a    ,     seq_length=a    ,     is_pair=a    ,     framework=a   )

							lowerCAmelCase__      ,				lowerCAmelCase__      :      List[Any]         =		dummy_input['input_ids'].shape
							lowerCAmelCase__      :      Union[str, Any]         =		(batch, encoder_sequence, self._config.encoder_hidden_size)
							lowerCAmelCase__      :      List[str]         =		dummy_input.pop('input_ids'   )
							lowerCAmelCase__      :      Dict         =		dummy_input.pop('attention_mask'   )
							lowerCAmelCase__      :      List[str]         =		torch.zeros(a   )

							return common_inputs







class      A__							(     __magic_name__	):






				@property
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      PretrainedConfig   ):






							'''simple docstring'''







							return VisionEncoderDecoderEncoderOnnxConfig(a   )






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      PretrainedConfig    ,     a					:      PretrainedConfig    ,     a					:      str = "default"   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		encoder_config.hidden_size
							return VisionEncoderDecoderDecoderOnnxConfig(a    ,     a   )

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__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







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






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







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

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

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

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

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

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

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

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )