

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler



class 						lowercase_					(						unittest.TestCase  ):
      # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
      UpperCamelCase_    :	Union[str, Any]		   =       (ScoreSdeVeScheduler,)
      UpperCamelCase_    :	Dict		   =       ()



      @property
      def 		UpperCamelCase_	(    self   :       int	)					->  List[Any]:
        _snake_case		=							4
        _snake_case		=							3
        _snake_case		=							8
        _snake_case		=							8

        _snake_case		=							torch.rand((batch_size, num_channels, height, width)	)

        return sample



      @property
      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  Union[str, Any]:
        _snake_case		=							4
        _snake_case		=							3
        _snake_case		=							8
        _snake_case		=							8

        _snake_case		=							batch_size * num_channels * height * width
        _snake_case		=							torch.arange(A__	)
        _snake_case		=							sample.reshape(A__    ,      A__    ,      A__    ,      A__	)
        _snake_case		=							sample / num_elems
        _snake_case		=							sample.permute(3    ,      0    ,      1    ,      2	)

        return sample



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







        def model(A__   :       List[Any]    ,      A__   :       int    ,      *A__   :       Union[str, Any]	):
          return sample * t / (t + 1)

        return model



      def 		UpperCamelCase_	(    self   :       Any    ,      **A__   :       Union[str, Any]	)					->  int:
        _snake_case		=							{
            "num_train_timesteps": 2000,
            "snr": 0.15,
            "sigma_min": 0.01,
            "sigma_max": 1348,
            "sampling_eps": 1e-5,
        }

        config.update(**A__	)
        return config



      def 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      A__   :       Optional[Any]=0    ,      **A__   :       Optional[int]	)					->  str:
        _snake_case		=							dict(self.forward_default_kwargs	)

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

          _snake_case		=							self.get_scheduler_config(**A__	)
          _snake_case		=							scheduler_class(**A__	)

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(A__	)
            _snake_case		=							scheduler_class.from_pretrained(A__	)

          _snake_case		=							scheduler.step_pred(
              A__    ,      A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample
          _snake_case		=							new_scheduler.step_pred(
              A__    ,      A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample

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

          _snake_case		=							scheduler.step_correct(A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample
          _snake_case		=							new_scheduler.step_correct(
              A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample

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



      def 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      A__   :       Optional[int]=0    ,      **A__   :       int	)					->  Tuple:
        _snake_case		=							dict(self.forward_default_kwargs	)
        kwargs.update(A__	)

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

          _snake_case		=							self.get_scheduler_config()
          _snake_case		=							scheduler_class(**A__	)

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(A__	)
            _snake_case		=							scheduler_class.from_pretrained(A__	)

          _snake_case		=							scheduler.step_pred(
              A__    ,      A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample
          _snake_case		=							new_scheduler.step_pred(
              A__    ,      A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample

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

          _snake_case		=							scheduler.step_correct(A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample
          _snake_case		=							new_scheduler.step_correct(
              A__    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample

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



      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  str:







        for timesteps in [10, 100, 1000]:
          self.check_over_configs(num_train_timesteps=A__	)



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







        for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,      [1, 100, 1000]	):
          self.check_over_configs(sigma_min=A__    ,      sigma_max=A__	)



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







        for t in [0.1, 0.5, 0.75]:
          self.check_over_forward(time_step=A__	)



      def 		UpperCamelCase_	(    self   :       str	)					->  Tuple:
        _snake_case		=							dict(self.forward_default_kwargs	)

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

        _snake_case		=							3

        _snake_case		=							self.dummy_model()
        _snake_case		=							self.dummy_sample_deter

        scheduler.set_sigmas(A__	)
        scheduler.set_timesteps(A__	)
        _snake_case		=							torch.manual_seed(0	)

        for i, t in enumerate(scheduler.timesteps	):
          _snake_case		=							scheduler.sigmas[i]

          for _ in range(scheduler.config.correct_steps	):
            with torch.no_grad():
              _snake_case		=							model(A__    ,      A__	)
            _snake_case		=							scheduler.step_correct(A__    ,      A__    ,      generator=A__    ,      **A__	).prev_sample

          with torch.no_grad():
            _snake_case		=							model(A__    ,      A__	)

          _snake_case		=							scheduler.step_pred(A__    ,      A__    ,      A__    ,      generator=A__    ,      **A__	)
          _snake_case		=							output.prev_sample, output.prev_sample_mean

        _snake_case		=							torch.sum(torch.abs(A__	)	)
        _snake_case		=							torch.mean(torch.abs(A__	)	)

        assert np.isclose(result_sum.item()    ,      14372758528.0	)
        assert np.isclose(result_mean.item()    ,      18714530.0	)







      def 		UpperCamelCase_	(    self   :       Optional[Any]	)					->  List[str]:
        _snake_case		=							dict(self.forward_default_kwargs	)

        _snake_case		=							kwargs.pop('''num_inference_steps'''    ,      A__	)

        for scheduler_class in self.scheduler_classes:
          _snake_case		=							self.get_scheduler_config()
          _snake_case		=							scheduler_class(**A__	)

          _snake_case		=							self.dummy_sample
          _snake_case		=							0.1 * sample

          if num_inference_steps is not None and hasattr(A__    ,      '''set_timesteps'''	):
            scheduler.set_timesteps(A__	)
          elif num_inference_steps is not None and not hasattr(A__    ,      '''set_timesteps'''	):
            _snake_case		=							num_inference_steps

          _snake_case		=							scheduler.step_pred(A__    ,      0    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample
          _snake_case		=							scheduler.step_pred(A__    ,      1    ,      A__    ,      generator=torch.manual_seed(0	)    ,      **A__	).prev_sample

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



from __future__ import annotations

from typing import Generic, TypeVar

__A    	=			TypeVar('''T''')



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       str    ,      A__   :       T	)					->  Union[str, Any]:
        _snake_case		=							data  # Assign data
        _snake_case		=							None  # Initialize next as null
        _snake_case		=							None  # Initialize prev as null



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       List[str]	)					->  None:
        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       T	)					->  None:
        if self.head is None:
          _snake_case		=							Node(A__	)
        else:
          _snake_case		=							Node(A__	)
          _snake_case		=							new_node
          _snake_case		=							self.head
          _snake_case		=							None
          _snake_case		=							new_node



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  T | None:
        if self.head is None:
          return None
        else:
          assert self.head is not None
          _snake_case		=							self.head.data
          _snake_case		=							self.head.next
          if self.head is not None:
            _snake_case		=							None
          return temp



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  T | None:
        return self.head.data if self.head is not None else None



      def __len__(    self   :       Optional[int]	)					->  int:
        _snake_case		=							self.head
        _snake_case		=							0
        while temp is not None:
          count += 1
          _snake_case		=							temp.next
        return count



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  bool:
        return self.head is None







      def 		UpperCamelCase_	(    self   :       Dict	)					->  None:
        print('''stack elements are:'''	)
        _snake_case		=							self.head
        while temp is not None:
          print(temp.data    ,      end='''->'''	)
          _snake_case		=							temp.next


# Code execution starts here
if __name__ == "__main__":
 # Start with the empty stack
 __A    	=			Stack()

 # Insert 4 at the beginning. So stack becomes 4->None
 print('''Stack operations using Doubly LinkedList''')
 stack.push(4)

 # Insert 5 at the beginning. So stack becomes 4->5->None
 stack.push(5)

 # Insert 6 at the beginning. So stack becomes 4->5->6->None
 stack.push(6)

 # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
 stack.push(7)

 # Print the stack
 stack.print_stack()

 # Print the top element
 print('''\nTop element is ''', stack.top())

 # Print the stack size
 print('''Size of the stack is ''', len(stack))

 # pop the top element
 stack.pop()

 # pop the top element
 stack.pop()

 # two elements have now been popped off
 stack.print_stack()

 # Print True if the stack is empty else False
 print('''\nstack is empty:''', stack.is_empty())

