


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 		lowercase							(     _a	):
     _a											=		(PNDMScheduler,)
     _a											=		(("""num_inference_steps""", 5_0),)

     def       a__			(						self	,  **_a       )		->							Optional[int]:
           _A							:  Dict    =		{
               """num_train_timesteps""": 1000,
               """beta_start""": 0.0001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**snake_case_       )
           return config

     def       a__			(						self	,  _a=0	,  **_a       )		->							int:
           _A							:  int    =		dict(self.forward_default_kwargs       )
           _A							:  Dict    =		kwargs.pop("""num_inference_steps"""	,  snake_case_       )
           _A							:  Optional[Any]    =		self.dummy_sample
           _A							:  str    =		0.1 * sample
           _A							:  Union[str, Any]    =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 _A							:  Optional[int]    =		self.get_scheduler_config(**snake_case_       )
                 _A							:  List[Any]    =		scheduler_class(**snake_case_       )
                 scheduler.set_timesteps(snake_case_       )
                 # copy over dummy past residuals
                 _A							:  Dict    =		dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(snake_case_       )
                       _A							:  List[Any]    =		scheduler_class.from_pretrained(snake_case_       )
                       new_scheduler.set_timesteps(snake_case_       )
                       # copy over dummy past residuals
                       _A							:  str    =		dummy_past_residuals[:]

                 _A							:  Dict    =		scheduler.step_prk(snake_case_	,  snake_case_	,  snake_case_	,  **snake_case_       ).prev_sample
                 _A							:  Optional[Any]    =		new_scheduler.step_prk(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"

                 _A							:  Tuple    =		scheduler.step_plms(snake_case_	,  snake_case_	,  snake_case_	,  **snake_case_       ).prev_sample
                 _A							:  Tuple    =		new_scheduler.step_plms(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       )		->							Optional[Any]:
           pass

     def       a__			(						self	,  _a=0	,  **_a       )		->							List[str]:
           _A							:  List[Any]    =		dict(self.forward_default_kwargs       )
           _A							:  List[str]    =		kwargs.pop("""num_inference_steps"""	,  snake_case_       )
           _A							:  Optional[int]    =		self.dummy_sample
           _A							:  Optional[int]    =		0.1 * sample
           _A							:  str    =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 _A							:  Any    =		self.get_scheduler_config()
                 _A							:  Optional[int]    =		scheduler_class(**snake_case_       )
                 scheduler.set_timesteps(snake_case_       )

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(snake_case_       )
                       _A							:  Optional[int]    =		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)
                       _A							:  Optional[Any]    =		dummy_past_residuals[:]

                 _A							:  str    =		scheduler.step_prk(snake_case_	,  snake_case_	,  snake_case_	,  **snake_case_       ).prev_sample
                 _A							:  List[str]    =		new_scheduler.step_prk(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"

                 _A							:  Optional[Any]    =		scheduler.step_plms(snake_case_	,  snake_case_	,  snake_case_	,  **snake_case_       ).prev_sample
                 _A							:  int    =		new_scheduler.step_plms(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	,  **_a       )		->							Union[str, Any]:
           _A							:  str    =		self.scheduler_classes[0]
           _A							:  Optional[int]    =		self.get_scheduler_config(**snake_case_       )
           _A							:  int    =		scheduler_class(**snake_case_       )

           _A							:  Optional[Any]    =		10
           _A							:  Optional[Any]    =		self.dummy_model()
           _A							:  Dict    =		self.dummy_sample_deter
           scheduler.set_timesteps(snake_case_       )

           for i, t in enumerate(scheduler.prk_timesteps       ):
                 _A							:  Dict    =		model(snake_case_	,  snake_case_       )
                 _A							:  str    =		scheduler.step_prk(snake_case_	,  snake_case_	,  snake_case_       ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps       ):
                 _A							:  int    =		model(snake_case_	,  snake_case_       )
                 _A							:  Dict    =		scheduler.step_plms(snake_case_	,  snake_case_	,  snake_case_       ).prev_sample

           return sample

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Optional[Any]    =		dict(self.forward_default_kwargs       )

           _A							:  Any    =		kwargs.pop("""num_inference_steps"""	,  snake_case_       )

           for scheduler_class in self.scheduler_classes:
                 _A							:  Tuple    =		self.get_scheduler_config()
                 _A							:  Optional[int]    =		scheduler_class(**snake_case_       )

                 _A							:  int    =		self.dummy_sample
                 _A							:  int    =		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"""       ):
                       _A							:  Any    =		num_inference_steps

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

                 _A							:  Union[str, Any]    =		scheduler.step_prk(snake_case_	,  0	,  snake_case_	,  **snake_case_       ).prev_sample
                 _A							:  Union[str, Any]    =		scheduler.step_prk(snake_case_	,  1	,  snake_case_	,  **snake_case_       ).prev_sample

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

                 _A							:  List[str]    =		scheduler.step_plms(snake_case_	,  0	,  snake_case_	,  **snake_case_       ).prev_sample
                 _A							:  Optional[int]    =		scheduler.step_plms(snake_case_	,  1	,  snake_case_	,  **snake_case_       ).prev_sample

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

     def       a__			(						self       )		->							Dict:
           for timesteps in [100, 1000]:
                 self.check_over_configs(num_train_timesteps=snake_case_       )

     def       a__			(						self       )		->							Any:
           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=snake_case_       )

           _A							:  Optional[Any]    =		self.scheduler_classes[0]
           _A							:  Tuple    =		self.get_scheduler_config(steps_offset=1       )
           _A							:  List[Any]    =		scheduler_class(**snake_case_       )
           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       )		->							Optional[Any]:
           for beta_start, beta_end in zip([0.0001, 0.001]	,  [0.002, 0.02]       ):
                 self.check_over_configs(beta_start=snake_case_	,  beta_end=snake_case_       )

     def       a__			(						self       )		->							str:
           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=snake_case_       )

     def       a__			(						self       )		->							Any:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=snake_case_       )

     def       a__			(						self       )		->							Union[str, Any]:
           for t in [1, 5, 10]:
                 self.check_over_forward(time_step=snake_case_       )

     def       a__			(						self       )		->							Union[str, Any]:
           for t, num_inference_steps in zip([1, 5, 10]	,  [10, 50, 100]       ):
                 self.check_over_forward(num_inference_steps=snake_case_       )

     def       a__			(						self       )		->							Optional[int]:
           # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
           _A							:  List[str]    =		27

           for scheduler_class in self.scheduler_classes:
                 _A							:  Any    =		self.dummy_sample
                 _A							:  List[str]    =		0.1 * sample

                 _A							:  Any    =		self.get_scheduler_config()
                 _A							:  Tuple    =		scheduler_class(**snake_case_       )

                 scheduler.set_timesteps(snake_case_       )

                 # 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]       ):
                       _A							:  str    =		scheduler.step_prk(snake_case_	,  snake_case_	,  snake_case_       ).prev_sample

     def       a__			(						self       )		->							List[str]:
           with self.assertRaises(snake_case_       ):
                 _A							:  List[Any]    =		self.scheduler_classes[0]
                 _A							:  Dict    =		self.get_scheduler_config()
                 _A							:  Optional[int]    =		scheduler_class(**snake_case_       )

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

     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		self.full_loop()
           _A							:  Any    =		torch.sum(torch.abs(snake_case_       )       )
           _A							:  Union[str, Any]    =		torch.mean(torch.abs(snake_case_       )       )

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

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  int    =		self.full_loop(prediction_type="""v_prediction"""       )
           _A							:  Tuple    =		torch.sum(torch.abs(snake_case_       )       )
           _A							:  Any    =		torch.mean(torch.abs(snake_case_       )       )

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

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

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



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

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







from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		list(snake_case_  )
      _A							:  List[Any]    =		list(snake_case_  )
      _A							:  Tuple    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count += 1
                  _A							:  Optional[Any]    =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]
      while True:
            _A							:  int    =		["""$"""] * len(snake_case_  )
            _A							:  Any    =		[]
            for i in range(len(snake_case_  )  ):
                  for j in range(i + 1,len(snake_case_  )  ):
                        _A							:  Tuple    =		compare_string(binary[i],binary[j]  )
                        if k is False:
                              _A							:  str    =		"""*"""
                              _A							:  str    =		"""*"""
                              temp.append("""X"""  )
            for i in range(len(snake_case_  )  ):
                  if checka[i] == "$":
                        pi.append(binary[i]  )
            if len(snake_case_  ) == 0:
                  return pi
            _A							:  Dict    =		list(set(snake_case_  )  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		[]
      for minterm in minterms:
            _A							:  Tuple    =		""""""
            for _ in range(snake_case_  ):
                  _A							:  Optional[Any]    =		str(minterm % 2  ) + string
                  minterm //= 2
            temp.append(snake_case_  )
      return temp







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		list(snake_case_  )
      _A							:  Tuple    =		list(snake_case_  )
      _A							:  Dict    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[]
      _A							:  str    =		[0] * len(snake_case_  )
      for i in range(len(chart[0]  )  ):
            _A							:  Union[str, Any]    =		0
            _A							:  Optional[Any]    =		-1
            for j in range(len(snake_case_  )  ):
                  if chart[j][i] == 1:
                        count += 1
                        _A							:  Dict    =		j
            if count == 1:
                  _A							:  int    =		1
      for i in range(len(snake_case_  )  ):
            if select[i] == 1:
                  for j in range(len(chart[0]  )  ):
                        if chart[i][j] == 1:
                              for k in range(len(snake_case_  )  ):
                                    _A							:  int    =		0
                  temp.append(prime_implicants[i]  )
      while True:
            _A							:  Optional[Any]    =		0
            _A							:  Tuple    =		-1
            _A							:  List[Any]    =		0
            for i in range(len(snake_case_  )  ):
                  _A							:  List[str]    =		chart[i].count(1  )
                  if count_n > max_n:
                        _A							:  Optional[int]    =		count_n
                        _A							:  Tuple    =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]  )

            for i in range(len(chart[0]  )  ):
                  if chart[rem][i] == 1:
                        for j in range(len(snake_case_  )  ):
                              _A							:  Optional[int]    =		0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[[0 for x in range(len(snake_case_  )  )] for x in range(len(snake_case_  )  )]
      for i in range(len(snake_case_  )  ):
            _A							:  List[Any]    =		prime_implicants[i].count("""_"""  )
            for j in range(len(snake_case_  )  ):
                  if is_for_table(prime_implicants[i],binary[j],snake_case_  ):
                        _A							:  Union[str, Any]    =		1

      return chart







def        lowerCAmelCase_			(  ):
      _A							:  Dict    =		int(input("""Enter the no. of variables\n"""  )  )
      _A							:  Dict    =		[
          float(snake_case_  )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""  ).split()
      ]
      _A							:  int    =		decimal_to_binary(snake_case_,snake_case_  )

      _A							:  Optional[Any]    =		check(snake_case_  )
      print("""Prime Implicants are:"""  )
      print(snake_case_  )
      _A							:  int    =		prime_implicant_chart(snake_case_,snake_case_  )

      _A							:  int    =		selection(snake_case_,snake_case_  )
      print("""Essential Prime Implicants are:"""  )
      print(snake_case_  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

