


import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						str				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Any		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		dataset
							lowercase__					      =		process
							lowercase__					      =		params

					def __len__(self   :						List[Any]		)						->       str:




							"""simple docstring"""


							return len(self.dataset		)


					def __getitem__(self   :						Union[str, Any]				, _UpperCAmelCase   :						List[str]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		self.dataset[i]
							lowercase__					      =		self.process(_UpperCAmelCase				, **self.params		)
							return processed






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						Dict				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Tuple=None		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		loader
							lowercase__					      =		infer
							lowercase__					      =		params
							if loader_batch_size == 1:
									# Let's spare some time by deactivating altogether
									lowercase__					      =		None
							lowercase__					      =		loader_batch_size

							# Internal bookkeeping
							lowercase__					      =		None
							lowercase__					      =		None

					def __len__(self   :						List[Any]		)						->       Optional[int]:




							"""simple docstring"""


							return len(self.loader		)

					def __iter__(self   :						Union[str, Any]		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		iter(self.loader		)
							return self

					def   lowerCamelCase__					(self   :						List[Any]		)						->       int:




							"""simple docstring"""


							if isinstance(self._loader_batch_data				, torch.Tensor		):
									# Batch data is simple tensor, just fetch the slice
									lowercase__					      =		self._loader_batch_data[self._loader_batch_index]
							else:
									# Batch data is assumed to be BaseModelOutput (or dict)
									lowercase__					      =		{}
									for k, element in self._loader_batch_data.items():
											if isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
													# Convert ModelOutput to tuple first
													lowercase__					      =		element.to_tuple()
													if isinstance(element[0]				, torch.Tensor		):
															lowercase__					      =		tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
													elif isinstance(element[0]				, np.ndarray		):
															lowercase__					      =		tuple(np.expand_dims(el[self._loader_batch_index]				, 0		) for el in element		)
													continue
											if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
													# Those are stored as lists of tensors so need specific unbatching.
													if isinstance(element[0]				, torch.Tensor		):
															lowercase__					      =		tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
													elif isinstance(element[0]				, np.ndarray		):
															lowercase__					      =		tuple(np.expand_dims(el[self._loader_batch_index]				, 0		) for el in element		)
													continue
											if element is None:
													# This can happen for optional data that get passed around
													lowercase__					      =		None
											elif isinstance(element[self._loader_batch_index]				, torch.Tensor		):
													# Take correct batch data, but make it looked like batch_size=1
													# For compatibility with other methods within transformers

													lowercase__					      =		element[self._loader_batch_index].unsqueeze(0		)
											elif isinstance(element[self._loader_batch_index]				, np.ndarray		):
													# Take correct batch data, but make it looked like batch_size=1
													# For compatibility with other methods within transformers
													lowercase__					      =		np.expand_dims(element[self._loader_batch_index]				, 0		)
											else:
													# This is typically a list, so no need to `unsqueeze`.
													lowercase__					      =		element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
									lowercase__					      =		self._loader_batch_data.__class__(_UpperCAmelCase		)
							self._loader_batch_index += 1
							return result


					def   lowerCamelCase__					(self   :						Optional[int]		)						->       List[Any]:




							"""simple docstring"""


							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
									# We are currently unrolling a batch so we just need to return
									# the current item within a batch
									return self.loader_batch_item()

							# We're out of items within a batch
							lowercase__					      =		next(self.iterator		)
							lowercase__					      =		self.infer(_UpperCAmelCase				, **self.params		)
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
									# Try to infer the size of the batch
									if isinstance(_UpperCAmelCase				, torch.Tensor		):
											lowercase__					      =		processed
									else:
											lowercase__					      =		list(processed.keys()		)[0]
											lowercase__					      =		processed[key]
									if isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
											lowercase__					      =		len(_UpperCAmelCase		)
									else:
											lowercase__					      =		first_tensor.shape[0]
									if 0 < observed_batch_size < self.loader_batch_size:
											# could be last batch so we can't unroll as many
											# elements.
											lowercase__					      =		observed_batch_size
									# Setting internal index to unwrap the batch
									lowercase__					      =		processed
									lowercase__					      =		0
									return self.loader_batch_item()
							else:
									# We're not unrolling batches
									return processed






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						int				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Any=None		)						->       Tuple:




							"""simple docstring"""


							super().__init__(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

					def __iter__(self   :						Optional[int]		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		iter(self.loader		)
							lowercase__					      =		None
							return self


					def   lowerCamelCase__					(self   :						Optional[int]		)						->       Optional[int]:




							"""simple docstring"""


							if self.subiterator is None:
									lowercase__					      =		self.infer(next(self.iterator		)				, **self.params		)
							try:
									# Try to return next item
									lowercase__					      =		next(self.subiterator		)
							except StopIteration:
									# When a preprocess iterator ends, we can start lookig at the next item
									# ChunkIterator will keep feeding until ALL elements of iterator
									# all have created their subiterator and have been iterating against.
									#
									# Another way to look at it, is we're basically flattening lists of lists
									# into a single list, but with generators
									lowercase__					      =		self.infer(next(self.iterator		)				, **self.params		)
									lowercase__					      =		next(self.subiterator		)
							return processed






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __iter__(self   :						List[Any]		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		iter(self.loader		)
							return self


					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		False
							lowercase__					      =		[]
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
									while self._loader_batch_index < self.loader_batch_size:
											lowercase__					      =		self.loader_batch_item()
											lowercase__					      =		item.pop("""is_last"""		)
											accumulator.append(_UpperCAmelCase		)
											if is_last:
													return accumulator

							while not is_last:
									lowercase__					      =		self.infer(next(self.iterator		)				, **self.params		)
									if self.loader_batch_size is not None:
											if isinstance(_UpperCAmelCase				, torch.Tensor		):
													lowercase__					      =		processed
											else:
													lowercase__					      =		list(processed.keys()		)[0]
													lowercase__					      =		processed[key]
											if isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
													lowercase__					      =		len(_UpperCAmelCase		)
											else:
													lowercase__					      =		first_tensor.shape[0]
											if 0 < observed_batch_size < self.loader_batch_size:
													# could be last batch so we can't unroll as many
													# elements.
													lowercase__					      =		observed_batch_size
											lowercase__					      =		processed
											lowercase__					      =		0
											while self._loader_batch_index < self.loader_batch_size:
													lowercase__					      =		self.loader_batch_item()
													lowercase__					      =		item.pop("""is_last"""		)
													accumulator.append(_UpperCAmelCase		)
													if is_last:
															return accumulator
									else:
											lowercase__					      =		processed
											lowercase__					      =		item.pop("""is_last"""		)
											accumulator.append(_UpperCAmelCase		)
							return accumulator






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						str				, _UpperCAmelCase   :						Dataset				, _UpperCAmelCase   :						str		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		dataset
							lowercase__					      =		key

					def __len__(self   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


							return len(self.dataset		)


					def __getitem__(self   :						Tuple				, _UpperCAmelCase   :						List[str]		)						->       int:




							"""simple docstring"""


							return self.dataset[i][self.key]






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						List[str]				, _UpperCAmelCase   :						Dataset				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						str		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		dataset
							lowercase__					      =		keya
							lowercase__					      =		keya

					def __len__(self   :						Dict		)						->       List[Any]:




							"""simple docstring"""


							return len(self.dataset		)


					def __getitem__(self   :						int				, _UpperCAmelCase   :						Optional[Any]		)						->       str:




							"""simple docstring"""


							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





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   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			42
					A__      =			42

					def __init__(self   :						Union[str, Any]				, _UpperCAmelCase   :						UNetaDModel				, _UpperCAmelCase   :						ScoreSdeVeScheduler		)						->       List[str]:




							"""simple docstring"""


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


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




							"""simple docstring"""


							lowercase__					      =		self.unet.config.sample_size
							lowercase__					      =		(batch_size, 3, img_size, img_size)

							lowercase__					      =		self.unet

							lowercase__					      =		randn_tensor(_UpperCAmelCase				, generator=_UpperCAmelCase		) * self.scheduler.init_noise_sigma
							lowercase__					      =		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		)		):
									lowercase__					      =		self.scheduler.sigmas[i] * torch.ones(shape[0]				, device=self.device		)

									# correction step
									for _ in range(self.scheduler.config.correct_steps		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		).sample
											lowercase__					      =		self.scheduler.step_correct(_UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		).prev_sample

									# prediction step
									lowercase__					      =		model(_UpperCAmelCase				, _UpperCAmelCase		).sample
									lowercase__					      =		self.scheduler.step_pred(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, generator=_UpperCAmelCase		)

									lowercase__      ,     lowercase__					      =		output.prev_sample, output.prev_sample_mean

							lowercase__					      =		sample_mean.clamp(0				, 1		)
							lowercase__					      =		sample.cpu().permute(0				, 2				, 3				, 1		).numpy()
							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase		)

