


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    (IPNDMScheduler,)
     __SCREAMING_SNAKE_CASE     					=    (('''num_inference_steps''', 5_0),)


     def        __snake_case(  self							,					**A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     {"""num_train_timesteps""": 10_00}
       config.update(**A_    )
       return config


     def        __snake_case(  self							,					A_=0							,					**A_    ):
       _UpperCAmelCase		:							List[Any]							=     dict(self.forward_default_kwargs    )
       _UpperCAmelCase		:							str							=     kwargs.pop("""num_inference_steps"""							,					A_    )
       _UpperCAmelCase		:							Union[str, Any]							=     self.dummy_sample
       _UpperCAmelCase		:							List[Any]							=     0.1 * sample
       _UpperCAmelCase		:							int							=     [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase		:							str							=     self.get_scheduler_config(**A_    )
         _UpperCAmelCase		:							List[str]							=     scheduler_class(**A_    )
         scheduler.set_timesteps(A_    )
         # copy over dummy past residuals
         _UpperCAmelCase		:							Dict							=     dummy_past_residuals[:]

         if time_step is None:
           _UpperCAmelCase		:							Union[str, Any]							=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

         _UpperCAmelCase		:							Optional[Any]							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample
         _UpperCAmelCase		:							str							=     new_scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample

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

         _UpperCAmelCase		:							Optional[int]							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample
         _UpperCAmelCase		:							Any							=     new_scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample

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


     def        __snake_case(  self    ):
       pass


     def        __snake_case(  self							,					A_=0							,					**A_    ):
       _UpperCAmelCase		:							int							=     dict(self.forward_default_kwargs    )
       _UpperCAmelCase		:							str							=     kwargs.pop("""num_inference_steps"""							,					A_    )
       _UpperCAmelCase		:							Any							=     self.dummy_sample
       _UpperCAmelCase		:							List[str]							=     0.1 * sample
       _UpperCAmelCase		:							Optional[Any]							=     [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

       for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase		:							Optional[int]							=     self.get_scheduler_config()
         _UpperCAmelCase		:							List[Any]							=     scheduler_class(**A_    )
         scheduler.set_timesteps(A_    )

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

         if time_step is None:
           _UpperCAmelCase		:							str							=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

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

         _UpperCAmelCase		:							Optional[Any]							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample
         _UpperCAmelCase		:							Dict							=     new_scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample

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

         _UpperCAmelCase		:							Tuple							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample
         _UpperCAmelCase		:							Optional[int]							=     new_scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample

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


     def        __snake_case(  self							,					**A_    ):
       _UpperCAmelCase		:							Tuple							=     self.scheduler_classes[0]
       _UpperCAmelCase		:							Tuple							=     self.get_scheduler_config(**A_    )
       _UpperCAmelCase		:							str							=     scheduler_class(**A_    )

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

       for i, t in enumerate(scheduler.timesteps    ):
         _UpperCAmelCase		:							Union[str, Any]							=     model(A_							,					A_    )
         _UpperCAmelCase		:							Tuple							=     scheduler.step(A_							,					A_							,					A_    ).prev_sample

       for i, t in enumerate(scheduler.timesteps    ):
         _UpperCAmelCase		:							Union[str, Any]							=     model(A_							,					A_    )
         _UpperCAmelCase		:							Optional[int]							=     scheduler.step(A_							,					A_							,					A_    ).prev_sample

       return sample


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Dict							=     dict(self.forward_default_kwargs    )

       _UpperCAmelCase		:							Dict							=     kwargs.pop("""num_inference_steps"""							,					A_    )

       for scheduler_class in self.scheduler_classes:
         _UpperCAmelCase		:							Optional[int]							=     self.get_scheduler_config()
         _UpperCAmelCase		:							List[str]							=     scheduler_class(**A_    )

         _UpperCAmelCase		:							List[str]							=     self.dummy_sample
         _UpperCAmelCase		:							str							=     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"""    ):
           _UpperCAmelCase		:							List[Any]							=     num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         _UpperCAmelCase		:							Dict							=     [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         _UpperCAmelCase		:							List[str]							=     dummy_past_residuals[:]

         _UpperCAmelCase		:							List[Any]							=     scheduler.timesteps[5]
         _UpperCAmelCase		:							int							=     scheduler.timesteps[6]

         _UpperCAmelCase		:							List[Any]							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample
         _UpperCAmelCase		:							Tuple							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample

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

         _UpperCAmelCase		:							Any							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample
         _UpperCAmelCase		:							str							=     scheduler.step(A_							,					A_							,					A_							,					**A_    ).prev_sample

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


     def        __snake_case(  self    ):
       for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=A_							,					time_step=A_    )


     def        __snake_case(  self    ):
       for t, num_inference_steps in zip([1, 5, 10]							,					[10, 50, 1_00]    ):
         self.check_over_forward(num_inference_steps=A_							,					time_step=A_    )




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     self.full_loop()
       _UpperCAmelCase		:							int							=     torch.mean(torch.abs(A_    )    )

       assert abs(result_mean.item() - 2_54_05_29    ) < 10









import os
import string
import sys


SCREAMING_SNAKE_CASE__					:     List[str]       =     1 << 8

SCREAMING_SNAKE_CASE__					:     str       =     {
    'tab': ord('\t'),
    'newline': ord('\r'),
    'esc': 27,
    'up': 65 + ARROW_KEY_FLAG,
    'down': 66 + ARROW_KEY_FLAG,
    'right': 67 + ARROW_KEY_FLAG,
    'left': 68 + ARROW_KEY_FLAG,
    'mod_int': 91,
    'undefined': sys.maxsize,
    'interrupt': 3,
    'insert': 50,
    'delete': 51,
    'pg_up': 53,
    'pg_down': 54,
}

SCREAMING_SNAKE_CASE__					:     List[str]       =     KEYMAP['up']
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     KEYMAP['left']

if sys.platform == "win32":
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     []
 SCREAMING_SNAKE_CASE__					:     Dict       =     {
     b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
     b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
     b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
     b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
     b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
 }

for i in range(10):
 SCREAMING_SNAKE_CASE__					:     List[str]       =     ord(str(i))




def    a__    (		):
  if os.name == "nt":
    import msvcrt

    _UpperCAmelCase		:							int							=     """mbcs"""
    # Flush the keyboard buffer
    while msvcrt.kbhit():
      msvcrt.getch()
    if len(snake_case__     ) == 0:
      # Read the keystroke
      _UpperCAmelCase		:							Any							=     msvcrt.getch()

      # If it is a prefix char, get second part
      if ch in (b"\x00", b"\xe0"):
        _UpperCAmelCase		:							str							=     ch + msvcrt.getch()
        # Translate actual Win chars to bullet char types
        try:
          _UpperCAmelCase		:							Optional[int]							=     chr(WIN_KEYMAP[cha]     )
          WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""]     )     )
          WIN_CH_BUFFER.append(snake_case__     )
          if ord(snake_case__     ) in (
              KEYMAP["insert"] - 1 << 9,
              KEYMAP["delete"] - 1 << 9,
              KEYMAP["pg_up"] - 1 << 9,
              KEYMAP["pg_down"] - 1 << 9,
          ):
            WIN_CH_BUFFER.append(chr(126     )     )
          _UpperCAmelCase		:							Any							=     chr(KEYMAP["""esc"""]     )
        except KeyError:
          _UpperCAmelCase		:							Optional[int]							=     cha[1]
      else:
        _UpperCAmelCase		:							Union[str, Any]							=     ch.decode(snake_case__     )
    else:
      _UpperCAmelCase		:							Optional[int]							=     WIN_CH_BUFFER.pop(0     )
  elif os.name == "posix":
    import termios
    import tty

    _UpperCAmelCase		:							List[str]							=     sys.stdin.fileno()
    _UpperCAmelCase		:							Optional[Any]							=     termios.tcgetattr(snake_case__     )
    try:
      tty.setraw(snake_case__     )
      _UpperCAmelCase		:							Tuple							=     sys.stdin.read(1     )
    finally:
      termios.tcsetattr(snake_case__						,   termios.TCSADRAIN						,   snake_case__     )
  return ch





def    a__    (		):
  _UpperCAmelCase		:							int							=     get_raw_chars()
  if ord(snake_case__     ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
    return char

  elif ord(snake_case__     ) == KEYMAP["esc"]:
    _UpperCAmelCase		:							int							=     get_raw_chars()
    if ord(snake_case__     ) == KEYMAP["mod_int"]:
      _UpperCAmelCase		:							Optional[Any]							=     get_raw_chars()
      if ord(snake_case__     ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(snake_case__     ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
        return chr(ord(snake_case__     ) + ARROW_KEY_FLAG     )
      else:
        return KEYMAP["undefined"]
    else:
      return get_raw_chars()

  else:
    if char in string.printable:
      return char
    else:
      return KEYMAP["undefined"]





