import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						(PNDMScheduler,)
  lowerCamelCase__     =						(('''num_inference_steps''', 5_0),)







  def    __A     (							self :       Dict  ,     **__magic_name__ :       str   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  {
            "num_train_timesteps": 1_000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__magic_name__   )
        return config







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Optional[Any]=0  ,     **__magic_name__ :       Optional[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  dict(self.forward_default_kwargs   )
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("num_inference_steps"  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.dummy_sample
        SCREAMING_SNAKE_CASE_          =  0.1 * sample
        SCREAMING_SNAKE_CASE_          =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config(**__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )
              scheduler.set_timesteps(__magic_name__   )
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_          =  dummy_past_residuals[:]

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

              SCREAMING_SNAKE_CASE_          =  scheduler.step_prk(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample
              SCREAMING_SNAKE_CASE_          =  new_scheduler.step_prk(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample

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

              SCREAMING_SNAKE_CASE_          =  scheduler.step_plms(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample
              SCREAMING_SNAKE_CASE_          =  new_scheduler.step_plms(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample

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







  def    __A     (							self :       Union[str, Any]   )  ->	Optional[int]:
        pass







  def    __A     (							self :       List[Any]  ,     __magic_name__ :       Any=0  ,     **__magic_name__ :       int   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  dict(self.forward_default_kwargs   )
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("num_inference_steps"  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.dummy_sample
        SCREAMING_SNAKE_CASE_          =  0.1 * sample
        SCREAMING_SNAKE_CASE_          =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )
              scheduler.set_timesteps(__magic_name__   )

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_          =  dummy_past_residuals[:]

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

                    # copy over dummy past residual (must be after setting timesteps)
                    SCREAMING_SNAKE_CASE_          =  dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_          =  scheduler.step_prk(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample
              SCREAMING_SNAKE_CASE_          =  new_scheduler.step_prk(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample

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

              SCREAMING_SNAKE_CASE_          =  scheduler.step_plms(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample
              SCREAMING_SNAKE_CASE_          =  new_scheduler.step_plms(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     **__magic_name__   ).prev_sample

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







  def    __A     (							self :       Optional[int]  ,     **__magic_name__ :       Tuple   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  10
        SCREAMING_SNAKE_CASE_          =  self.dummy_model()
        SCREAMING_SNAKE_CASE_          =  self.dummy_sample_deter
        scheduler.set_timesteps(__magic_name__   )

        for i, t in enumerate(scheduler.prk_timesteps   ):
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     __magic_name__   )
              SCREAMING_SNAKE_CASE_          =  scheduler.step_prk(__magic_name__  ,     __magic_name__  ,     __magic_name__   ).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps   ):
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     __magic_name__   )
              SCREAMING_SNAKE_CASE_          =  scheduler.step_plms(__magic_name__  ,     __magic_name__  ,     __magic_name__   ).prev_sample

        return sample







  def    __A     (							self :       Union[str, Any]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  dict(self.forward_default_kwargs   )

        SCREAMING_SNAKE_CASE_          =  kwargs.pop("num_inference_steps"  ,     __magic_name__   )

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

              SCREAMING_SNAKE_CASE_          =  self.dummy_sample
              SCREAMING_SNAKE_CASE_          =  0.1 * sample

              if num_inference_steps is not None and hasattr(__magic_name__  ,     "set_timesteps"   ):
                    scheduler.set_timesteps(__magic_name__   )
              elif num_inference_steps is not None and not hasattr(__magic_name__  ,     "set_timesteps"   ):
                    SCREAMING_SNAKE_CASE_          =  num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_          =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_          =  dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_          =  scheduler.step_prk(__magic_name__  ,     0  ,     __magic_name__  ,     **__magic_name__   ).prev_sample
              SCREAMING_SNAKE_CASE_          =  scheduler.step_prk(__magic_name__  ,     1  ,     __magic_name__  ,     **__magic_name__   ).prev_sample

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

              SCREAMING_SNAKE_CASE_          =  scheduler.step_plms(__magic_name__  ,     0  ,     __magic_name__  ,     **__magic_name__   ).prev_sample
              SCREAMING_SNAKE_CASE_          =  scheduler.step_plms(__magic_name__  ,     1  ,     __magic_name__  ,     **__magic_name__   ).prev_sample

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







  def    __A     (							self :       Optional[Any]   )  ->	Any:
        for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=__magic_name__   )







  def    __A     (							self :       Dict   )  ->	Any:
        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config(steps_offset=1   )
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )
        scheduler.set_timesteps(10   )
        assert torch.equal(
            scheduler.timesteps  ,     torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]   )  ,     )







  def    __A     (							self :       Tuple   )  ->	Any:
        for beta_start, beta_end in zip([0.0001, 0.001]  ,     [0.002, 0.02]   ):
              self.check_over_configs(beta_start=__magic_name__  ,     beta_end=__magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	List[str]:
        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__magic_name__   )







  def    __A     (							self :       int   )  ->	List[Any]:
        for t in [1, 5, 10]:
              self.check_over_forward(time_step=__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        for t, num_inference_steps in zip([1, 5, 10]  ,     [10, 50, 100]   ):
              self.check_over_forward(num_inference_steps=__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	Optional[Any]:
        # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
        SCREAMING_SNAKE_CASE_          =  27

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_          =  self.dummy_sample
              SCREAMING_SNAKE_CASE_          =  0.1 * sample

              SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

              scheduler.set_timesteps(__magic_name__   )

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]   ):
                    SCREAMING_SNAKE_CASE_          =  scheduler.step_prk(__magic_name__  ,     __magic_name__  ,     __magic_name__   ).prev_sample







  def    __A     (							self :       int   )  ->	Optional[Any]:
        with self.assertRaises(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

              scheduler.step_plms(self.dummy_sample  ,     1  ,     self.dummy_sample   ).prev_sample







  def    __A     (							self :       int   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.full_loop()
        SCREAMING_SNAKE_CASE_          =  torch.sum(torch.abs(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  torch.mean(torch.abs(__magic_name__   )   )

        assert abs(result_sum.item() - 198.1318   ) < 1e-2
        assert abs(result_mean.item() - 0.2580   ) < 1e-3







  def    __A     (							self :       Dict   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.full_loop(prediction_type="v_prediction"   )
        SCREAMING_SNAKE_CASE_          =  torch.sum(torch.abs(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  torch.mean(torch.abs(__magic_name__   )   )

        assert abs(result_sum.item() - 67.3986   ) < 1e-2
        assert abs(result_mean.item() - 0.0878   ) < 1e-3







  def    __A     (							self :       Any   )  ->	Union[str, Any]:
        # We specify different beta, so that the first alpha is 0.99
        SCREAMING_SNAKE_CASE_          =  self.full_loop(set_alpha_to_one=__magic_name__  ,     beta_start=0.01   )
        SCREAMING_SNAKE_CASE_          =  torch.sum(torch.abs(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  torch.mean(torch.abs(__magic_name__   )   )

        assert abs(result_sum.item() - 230.0399   ) < 1e-2
        assert abs(result_mean.item() - 0.2995   ) < 1e-3







  def    __A     (							self :       Optional[int]   )  ->	List[str]:
        # We specify different beta, so that the first alpha is 0.99
        SCREAMING_SNAKE_CASE_          =  self.full_loop(set_alpha_to_one=__magic_name__  ,     beta_start=0.01   )
        SCREAMING_SNAKE_CASE_          =  torch.sum(torch.abs(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  torch.mean(torch.abs(__magic_name__   )   )

        assert abs(result_sum.item() - 186.9482   ) < 1e-2
        assert abs(result_mean.item() - 0.2434   ) < 1e-3



# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

A     :  Dict					     =       TypeVar("T")


class   lowerCamelCase					(Generic[T]					):
  """simple docstring"""







  def __init__(							self :       Optional[int]  ,     __magic_name__ :       bool = True   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  {}  # dictionary of lists
        SCREAMING_SNAKE_CASE_          =  directed







  def    __A     (							self :       Dict  ,     __magic_name__ :       T  ,     __magic_name__ :       T   )  ->	GraphAdjacencyList[T]:

        if not self.directed:  # For undirected graphs
              # if both source vertex and destination vertex are both present in the
              # adjacency list, add destination vertex to source vertex list of adjacent
              # vertices and add source vertex to destination vertex list of adjacent
              # vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    self.adj_list[destination_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination vertex
              # to source vertex list of adjacent vertices, then create a new vertex with
              # destination vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
              # if only destination vertex is present in adjacency list, add source vertex
              # to destination vertex list of adjacent vertices, then create a new vertex
              # with source vertex as key and assign a list containing the source vertex
              # as it's first adjacent vertex.
              elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and assign a list
              # containing the destination vertex as it's first adjacent vertex also
              # create a new vertex with destination vertex as key and assign a list
              # containing the source vertex as it's first adjacent vertex.
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  [source_vertex]
        else:  # For directed graphs
              # if both source vertex and destination vertex are present in adjacency
              # list, add destination vertex to source vertex list of adjacent vertices.
              if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
              # if only source vertex is present in adjacency list, add destination
              # vertex to source vertex list of adjacent vertices and create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  []
              # if only destination vertex is present in adjacency list, create a new
              # vertex with source vertex as key and assign a list containing destination
              # vertex as first adjacent vertex
              elif destination_vertex in self.adj_list:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
              # if both source vertex and destination vertex are not present in adjacency
              # list, create a new vertex with source vertex as key and a list containing
              # destination vertex as it's first adjacent vertex. Then create a new vertex
              # with destination vertex as key, which has no adjacent vertex
              else:
                    SCREAMING_SNAKE_CASE_          =  [destination_vertex]
                    SCREAMING_SNAKE_CASE_          =  []

        return self







  def __repr__(							self :       int   )  ->	str:
        return pformat(self.adj_list   )


