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

from ..utils.generic import ModelOutput




class   __lowercase						(						__snake_case       ):






	def __init__(self				: List[Any]      ,	snake_case				: Optional[int]      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	Tuple           =   dataset
							_lowercase       :	List[str]           =   process
							_lowercase       :	Any           =   params






	def __len__(self				: Optional[Any]    ) ->			Any:
							return len(self.dataset    )






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			int:
							_lowercase       :	Optional[Any]           =   self.dataset[i]
							_lowercase       :	Union[str, Any]           =   self.process(snake_case      ,	**self.params    )
							return processed




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: List[Any]      ,	snake_case				: Tuple      ,	snake_case				: Tuple      ,	snake_case				: List[str]=None    ) ->			Optional[int]:
							_lowercase       :	List[str]           =   loader
							_lowercase       :	Optional[Any]           =   infer
							_lowercase       :	List[Any]           =   params
							if loader_batch_size == 1:
													# Let's spare some time by deactivating altogether
													_lowercase       :	str           =   None
							_lowercase       :	Optional[int]           =   loader_batch_size

							# Internal bookkeeping
							_lowercase       :	Dict           =   None
							_lowercase       :	Union[str, Any]           =   None






	def __len__(self				: Any    ) ->			Any:
							return len(self.loader    )






	def __iter__(self				: int    ) ->			Optional[Any]:
							_lowercase       :	List[str]           =   iter(self.loader    )
							return self






	def       _a(self				: int    ) ->			List[str]:
							if isinstance(self._loader_batch_data      ,	torch.Tensor    ):
													# Batch data is simple tensor, just fetch the slice
													_lowercase       :	Any           =   self._loader_batch_data[self._loader_batch_index]
							else:
													# Batch data is assumed to be BaseModelOutput (or dict)
													_lowercase       :	List[str]           =   {}
													for k, element in self._loader_batch_data.items():
																			if isinstance(snake_case      ,	snake_case    ):
																									# Convert ModelOutput to tuple first
																									_lowercase       :	Dict           =   element.to_tuple()
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	Optional[Any]           =   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(snake_case      ,	snake_case    ):
																									# Those are stored as lists of tensors so need specific unbatching.
																									if isinstance(element[0]      ,	torch.Tensor    ):
																															_lowercase       :	Optional[int]           =   tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
																									elif isinstance(element[0]      ,	np.ndarray    ):
																															_lowercase       :	List[str]           =   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       :	Optional[Any]           =   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       :	int           =   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       :	Tuple           =   np.expand_dims(element[self._loader_batch_index]      ,	0    )
																			else:
																									# This is typically a list, so no need to `unsqueeze`.
																									_lowercase       :	Union[str, Any]           =   element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
													_lowercase       :	int           =   self._loader_batch_data.__class__(snake_case    )
							self._loader_batch_index += 1
							return result






	def       _a(self				: Any    ) ->			str:
							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       :	str           =   next(self.iterator    )
							_lowercase       :	Any           =   self.infer(snake_case      ,	**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(snake_case      ,	torch.Tensor    ):
																			_lowercase       :	int           =   processed
													else:
																			_lowercase       :	List[str]           =   list(processed.keys()    )[0]
																			_lowercase       :	List[str]           =   processed[key]
													if isinstance(snake_case      ,	snake_case    ):
																			_lowercase       :	int           =   len(snake_case    )
													else:
																			_lowercase       :	Optional[int]           =   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       :	Union[str, Any]           =   observed_batch_size
													# Setting internal index to unwrap the batch
													_lowercase       :	Any           =   processed
													_lowercase       :	Dict           =   0
													return self.loader_batch_item()
							else:
													# We're not unrolling batches
													return processed




class   __lowercase						(						__snake_case       ):






	def __init__(self				: Union[str, Any]      ,	snake_case				: str      ,	snake_case				: Dict      ,	snake_case				: Optional[Any]      ,	snake_case				: List[str]=None    ) ->			List[str]:
							super().__init__(snake_case      ,	snake_case      ,	snake_case    )






	def __iter__(self				: List[Any]    ) ->			str:
							_lowercase       :	int           =   iter(self.loader    )
							_lowercase       :	Optional[int]           =   None
							return self






	def       _a(self				: Optional[Any]    ) ->			Optional[int]:
							if self.subiterator is None:
													_lowercase       :	List[str]           =   self.infer(next(self.iterator    )      ,	**self.params    )
							try:
													# Try to return next item
													_lowercase       :	str           =   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       :	Any           =   self.infer(next(self.iterator    )      ,	**self.params    )
													_lowercase       :	Optional[int]           =   next(self.subiterator    )
							return processed




class   __lowercase						(						__snake_case       ):






	def __iter__(self				: List[str]    ) ->			List[str]:
							_lowercase       :	Any           =   iter(self.loader    )
							return self






	def       _a(self				: List[Any]    ) ->			Union[str, Any]:
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							_lowercase       :	Dict           =   False
							_lowercase       :	Optional[int]           =   []
							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       :	Tuple           =   self.loader_batch_item()
																			_lowercase       :	List[Any]           =   item.pop("is_last"    )
																			accumulator.append(snake_case    )
																			if is_last:
																									return accumulator

							while not is_last:
													_lowercase       :	List[Any]           =   self.infer(next(self.iterator    )      ,	**self.params    )
													if self.loader_batch_size is not None:
																			if isinstance(snake_case      ,	torch.Tensor    ):
																									_lowercase       :	Optional[Any]           =   processed
																			else:
																									_lowercase       :	Tuple           =   list(processed.keys()    )[0]
																									_lowercase       :	int           =   processed[key]
																			if isinstance(snake_case      ,	snake_case    ):
																									_lowercase       :	str           =   len(snake_case    )
																			else:
																									_lowercase       :	List[str]           =   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       :	Tuple           =   observed_batch_size
																			_lowercase       :	Any           =   processed
																			_lowercase       :	Dict           =   0
																			while self._loader_batch_index < self.loader_batch_size:
																									_lowercase       :	str           =   self.loader_batch_item()
																									_lowercase       :	int           =   item.pop("is_last"    )
																									accumulator.append(snake_case    )
																									if is_last:
																															return accumulator
													else:
																			_lowercase       :	str           =   processed
																			_lowercase       :	int           =   item.pop("is_last"    )
																			accumulator.append(snake_case    )
							return accumulator




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str    ) ->			List[Any]:
							_lowercase       :	Optional[Any]           =   dataset
							_lowercase       :	Any           =   key






	def __len__(self				: Any    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: int      ,	snake_case				: Any    ) ->			Any:
							return self.dataset[i][self.key]




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	snake_case				: Dataset      ,	snake_case				: str      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	int           =   dataset
							_lowercase       :	Optional[Any]           =   keya
							_lowercase       :	Tuple           =   keya






	def __len__(self				: List[str]    ) ->			Union[str, Any]:
							return len(self.dataset    )






	def __getitem__(self				: Optional[Any]      ,	snake_case				: Dict    ) ->			int:
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class   __lowercase						(						__snake_case       ):
	_A       =   (DEISMultistepScheduler,)
	_A       =   (("num_inference_steps", 25),)






	def       _a(self				: Optional[int]      ,	**snake_case				: str    ) ->			Optional[Any]:
							_lowercase       :	str           =   {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**snake_case    )
							return config






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]=0      ,	**snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	Dict           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[Any]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Optional[int]           =   self.dummy_sample
							_lowercase       :	Tuple           =   0.1 * sample
							_lowercase       :	Optional[Any]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )
													# copy over dummy past residuals
													_lowercase       :	Any           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	Any           =   scheduler_class.from_pretrained(snake_case    )
																			new_scheduler.set_timesteps(snake_case    )
																			# copy over dummy past residuals
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase      ,    _lowercase       :	Optional[Any]           =   sample, sample
													for t in range(snake_case      ,	time_step + scheduler.config.solver_order + 1    ):
																			_lowercase       :	List[Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
																			_lowercase       :	Union[str, Any]           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

																			assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: str    ) ->			List[str]:
							pass






	def       _a(self				: Optional[Any]      ,	snake_case				: List[Any]=0      ,	**snake_case				: str    ) ->			Union[str, Any]:
							_lowercase       :	List[str]           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Dict           =   self.dummy_sample
							_lowercase       :	Union[str, Any]           =   0.1 * sample
							_lowercase       :	List[str]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )

													# copy over dummy past residuals (must be after setting timesteps)
													_lowercase       :	Union[str, Any]           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	str           =   scheduler_class.from_pretrained(snake_case    )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(snake_case    )

																			# copy over dummy past residual (must be after setting timesteps)
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase       :	Any           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Dict           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: Dict      ,	snake_case				: str=None      ,	**snake_case				: Dict    ) ->			Optional[Any]:
							if scheduler is None:
													_lowercase       :	Optional[Any]           =   self.scheduler_classes[0]
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	Optional[int]           =   scheduler_class(**snake_case    )

							_lowercase       :	Tuple           =   self.scheduler_classes[0]
							_lowercase       :	str           =   self.get_scheduler_config(**snake_case    )
							_lowercase       :	Tuple           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	List[str]           =   self.dummy_model()
							_lowercase       :	List[Any]           =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	str           =   model(snake_case      ,	snake_case    )
													_lowercase       :	int           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							return sample






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   dict(self.forward_default_kwargs    )

							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	Optional[Any]           =   scheduler_class(**snake_case    )

													_lowercase       :	Tuple           =   self.dummy_sample
													_lowercase       :	List[Any]           =   0.1 * sample

													if num_inference_steps is not None and hasattr(snake_case      ,	"set_timesteps"    ):
																			scheduler.set_timesteps(snake_case    )
													elif num_inference_steps is not None and not hasattr(snake_case      ,	"set_timesteps"    ):
																			_lowercase       :	str           =   num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowercase       :	int           =   [residual + 0.2, residual + 0.15, residual + 0.10]
													_lowercase       :	Dict           =   dummy_past_residuals[: scheduler.config.solver_order]

													_lowercase       :	Tuple           =   scheduler.timesteps[5]
													_lowercase       :	Dict           =   scheduler.timesteps[6]

													_lowercase       :	Tuple           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Union[str, Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													self.assertEqual(output_a.shape      ,	sample.shape    )
													self.assertEqual(output_a.shape      ,	output_a.shape    )






	def       _a(self				: Optional[int]    ) ->			List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_lowercase       :	Any           =   DEISMultistepScheduler(**self.get_scheduler_config()    )
							_lowercase       :	List[Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

							_lowercase       :	str           =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
							_lowercase       :	Optional[int]           =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	List[Any]           =   UniPCMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	int           =   DEISMultistepScheduler.from_config(scheduler.config    )

							_lowercase       :	Union[str, Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Dict           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case    )






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							self.check_over_configs(thresholding=snake_case    )
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=snake_case      ,	prediction_type=snake_case      ,	sample_max_value=snake_case      ,	algorithm_type="deis"      ,	solver_order=snake_case      ,	solver_type=snake_case      ,	)






	def       _a(self				: int    ) ->			Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															_lowercase       :	Any           =   self.full_loop(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															assert not torch.isnan(snake_case    ).any(), "Samples have nan numbers"






	def       _a(self				: int    ) ->			List[str]:
							self.check_over_configs(lower_order_final=snake_case    )
							self.check_over_configs(lower_order_final=snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=snake_case      ,	time_step=0    )






	def       _a(self				: Tuple    ) ->			Optional[Any]:
							_lowercase       :	Optional[Any]           =   self.full_loop()
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase       :	int           =   self.full_loop(prediction_type="v_prediction"    )
							_lowercase       :	str           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	int           =   self.scheduler_classes[0]
							_lowercase       :	Tuple           =   self.get_scheduler_config(thresholding=snake_case      ,	dynamic_thresholding_ratio=0    )
							_lowercase       :	int           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	Union[str, Any]           =   self.dummy_model()
							_lowercase       :	Dict           =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	Tuple           =   model(snake_case      ,	snake_case    )
													_lowercase       :	List[str]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							assert sample.dtype == torch.floataa




