

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_     =      {
    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_     =      ["BigBirdTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_     =      ["BigBirdTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_     =      [
						    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BigBirdForCausalLM",
						    "BigBirdForMaskedLM",
						    "BigBirdForMultipleChoice",
						    "BigBirdForPreTraining",
						    "BigBirdForQuestionAnswering",
						    "BigBirdForSequenceClassification",
						    "BigBirdForTokenClassification",
						    "BigBirdLayer",
						    "BigBirdModel",
						    "BigBirdPreTrainedModel",
						    "load_tf_weights_in_big_bird",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_     =      [
						    "FlaxBigBirdForCausalLM",
						    "FlaxBigBirdForMaskedLM",
						    "FlaxBigBirdForMultipleChoice",
						    "FlaxBigBirdForPreTraining",
						    "FlaxBigBirdForQuestionAnswering",
						    "FlaxBigBirdForSequenceClassification",
						    "FlaxBigBirdForTokenClassification",
						    "FlaxBigBirdModel",
						    "FlaxBigBirdPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_big_bird import BigBirdTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_big_bird_fast import BigBirdTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_big_bird import (
												    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BigBirdForCausalLM,
												    BigBirdForMaskedLM,
												    BigBirdForMultipleChoice,
												    BigBirdForPreTraining,
												    BigBirdForQuestionAnswering,
												    BigBirdForSequenceClassification,
												    BigBirdForTokenClassification,
												    BigBirdLayer,
												    BigBirdModel,
												    BigBirdPreTrainedModel,
												    load_tf_weights_in_big_bird,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_big_bird import (
												    FlaxBigBirdForCausalLM,
												    FlaxBigBirdForMaskedLM,
												    FlaxBigBirdForMultipleChoice,
												    FlaxBigBirdForPreTraining,
												    FlaxBigBirdForQuestionAnswering,
												    FlaxBigBirdForSequenceClassification,
												    FlaxBigBirdForTokenClassification,
												    FlaxBigBirdModel,
												    FlaxBigBirdPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase_     =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







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  )