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

from ..utils.generic import ModelOutput
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[int]:
					lowercase_       =					dataset
					lowercase_       =					process
					lowercase_       =					params
	def __len__(       self  :			Optional[Any]       )       ->     Tuple:
					return len(self.dataset       )




	def __getitem__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Dict:
					lowercase_       =					self.dataset[i]
					lowercase_       =					self.process(SCREAMING_SNAKE_CASE_       ,							**self.params       )
					return processed

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			List[str]=None       )       ->     int:
					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  :			Optional[int]       )       ->     int:
					return len(self.loader       )
	def __iter__(       self  :			List[str]       )       ->     int:
					lowercase_       =					iter(self.loader       )
					return self
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Dict:
					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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
																	# 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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
																	# 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__(SCREAMING_SNAKE_CASE_       )
					self._loader_batch_index += 1
					return result




	def 						_lowercase  (       self  :			int       )       ->     Dict:
					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(SCREAMING_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(SCREAMING_SNAKE_CASE_       ,							torch.Tensor       ):
													lowercase_       =					processed
									else:
													lowercase_       =					list(processed.keys()       )[0]
													lowercase_       =					processed[key]
									if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
													lowercase_       =					len(SCREAMING_SNAKE_CASE_       )
									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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any=None       )       ->     Union[str, Any]:
					super().__init__(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def __iter__(       self  :			Tuple       )       ->     Optional[Any]:
					lowercase_       =					iter(self.loader       )
					lowercase_       =					None
					return self




	def 						_lowercase  (       self  :			int       )       ->     str:
					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  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __iter__(       self  :			Tuple       )       ->     Any:
					lowercase_       =					iter(self.loader       )
					return self




	def 						_lowercase  (       self  :			List[Any]       )       ->     str:
					# 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_       =					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(SCREAMING_SNAKE_CASE_       )
													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(SCREAMING_SNAKE_CASE_       ,							torch.Tensor       ):
																	lowercase_       =					processed
													else:
																	lowercase_       =					list(processed.keys()       )[0]
																	lowercase_       =					processed[key]
													if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
																	lowercase_       =					len(SCREAMING_SNAKE_CASE_       )
													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(SCREAMING_SNAKE_CASE_       )
																	if is_last:
																					return accumulator
									else:
													lowercase_       =					processed
													lowercase_       =					item.pop('''is_last'''       )
													accumulator.append(SCREAMING_SNAKE_CASE_       )
					return accumulator

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Dataset       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     int:
					lowercase_       =					dataset
					lowercase_       =					key
	def __len__(       self  :			Optional[Any]       )       ->     Optional[int]:
					return len(self.dataset       )




	def __getitem__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     Tuple:
					return self.dataset[i][self.key]

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Dataset       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[int]:
					lowercase_       =					dataset
					lowercase_       =					keya
					lowercase_       =					keya
	def __len__(       self  :			Tuple       )       ->     List[str]:
					return len(self.dataset       )




	def __getitem__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     Union[str, Any]:
					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  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(DPMSolverSinglestepScheduler,)
    lowerCamelCase			=							(('num_inference_steps', 25),)


    def 						snake_case__     (      self :       Tuple,**lowercase_ :       Dict		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'prediction_type': 'epsilon',
            'thresholding': False,
            'sample_max_value': 1.0,
            'algorithm_type': 'dpmsolver++',
            'solver_type': 'midpoint',
            'lambda_min_clipped': -float('inf'		),
            'variance_type': None,
        }

        config.update(**lowercase_		)
        return config


    def 						snake_case__     (      self :       str,lowercase_ :       Optional[Any]=0,**lowercase_ :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)
            # copy over dummy past residuals
            A__          =   dummy_past_residuals[: scheduler.config.solver_order]

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

            A__      ,						A__          =   sample, sample
            for t in range(lowercase_,time_step + scheduler.config.solver_order + 1		):
                A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
                A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

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


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





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, Any]=0,**lowercase_ :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config()
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)

            # copy over dummy past residuals (must be after setting timesteps)
            A__          =   dummy_past_residuals[: scheduler.config.solver_order]

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

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

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

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


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Optional[int]=None,**lowercase_ :       int		)->    int:





        '''simple docstring'''







        if scheduler is None:
            A__          =   self.scheduler_classes[0]
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)

        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(**lowercase_		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        return sample


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   5_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_574		) < 1E-3


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





        '''simple docstring'''







        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


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





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3

        A__          =   DEISMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
        A__          =   UniPCMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        self.check_over_configs(thresholding=lowercase_		)
        for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
                for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            thresholding=lowercase_,prediction_type=lowercase_,sample_max_value=lowercase_,algorithm_type='dpmsolver++',solver_order=lowercase_,solver_type=lowercase_,)


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





        '''simple docstring'''







        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowercase_		)


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





        '''simple docstring'''







        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
                for order in [1, 2, 3]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        A__          =   self.full_loop(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"


    def 						snake_case__     (      self :       Optional[int]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(lower_order_final=lowercase_		)
        self.check_over_configs(lower_order_final=lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(variance_type=lowercase_		)
        self.check_over_configs(variance_type='learned_range'		)


    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=lowercase_,time_step=0		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop()
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.full_loop(use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_248		) < 1E-3


    def 						snake_case__     (      self :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction'		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.1_453		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    int:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction',use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.0_649		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(thresholding=lowercase_,dynamic_thresholding_ratio=0		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        assert sample.dtype == torch.floataa

