






"""simple docstring"""


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class __a ( unittest.TestCase					):
  # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
  __lowercase     :     int       						=       (ScoreSdeVeScheduler,)
  __lowercase     :     List[str]       						=       ()



  @property
  def     A_					(self	:		str )		->				Optional[int]:
   __A						:							int      =						4
   __A						:							Tuple      =						3
   __A						:							Tuple      =						8
   __A						:							str      =						8

   __A						:							List[str]      =						torch.rand((batch_size, num_channels, height, width) )

   return sample



  @property
  def     A_					(self	:		Optional[Any] )		->				Any:
   __A						:							List[str]      =						4
   __A						:							int      =						3
   __A						:							Any      =						8
   __A						:							int      =						8

   __A						:							Dict      =						batch_size * num_channels * height * width
   __A						:							List[str]      =						torch.arange(lowercase_ )
   __A						:							List[str]      =						sample.reshape(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_ )
   __A						:							int      =						sample / num_elems
   __A						:							Optional[int]      =						sample.permute(3					,      0					,      1					,      2 )

   return sample



  def     A_					(self	:		Any )		->				Any:
   def model(lowercase_	:		int					,      lowercase_	:		Any					,      *lowercase_	:		str ):
    return sample * t / (t + 1)

   return model



  def     A_					(self	:		List[str]					,      **lowercase_	:		Optional[Any] )		->				str:
   __A						:							Optional[Any]      =						{
       'num_train_timesteps': 2_000,
       'snr': 0.15,
       'sigma_min': 0.01,
       'sigma_max': 1_348,
       'sampling_eps': 1E-5,
   }

   config.update(**lowercase_ )
   return config



  def     A_					(self	:		List[Any]					,      lowercase_	:		List[str]=0					,      **lowercase_	:		Union[str, Any] )		->				Optional[int]:
   __A						:							Dict      =						dict(self.forward_default_kwargs )

   for scheduler_class in self.scheduler_classes:
    __A						:							Any      =						self.dummy_sample
    __A						:							Tuple      =						0.1 * sample

    __A						:							Optional[int]      =						self.get_scheduler_config(**lowercase_ )
    __A						:							str      =						scheduler_class(**lowercase_ )

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(lowercase_ )
     __A						:							str      =						scheduler_class.from_pretrained(lowercase_ )

    __A						:							Any      =						scheduler.step_pred(
        lowercase_					,      lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample
    __A						:							Union[str, Any]      =						new_scheduler.step_pred(
        lowercase_					,      lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample

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

    __A						:							Any      =						scheduler.step_correct(lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample
    __A						:							int      =						new_scheduler.step_correct(
        lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample

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



  def     A_					(self	:		Any					,      lowercase_	:		int=0					,      **lowercase_	:		List[Any] )		->				List[str]:
   __A						:							List[str]      =						dict(self.forward_default_kwargs )
   kwargs.update(lowercase_ )

   for scheduler_class in self.scheduler_classes:
    __A						:							Optional[int]      =						self.dummy_sample
    __A						:							Optional[int]      =						0.1 * sample

    __A						:							Union[str, Any]      =						self.get_scheduler_config()
    __A						:							Optional[Any]      =						scheduler_class(**lowercase_ )

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(lowercase_ )
     __A						:							List[Any]      =						scheduler_class.from_pretrained(lowercase_ )

    __A						:							Optional[int]      =						scheduler.step_pred(
        lowercase_					,      lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample
    __A						:							Tuple      =						new_scheduler.step_pred(
        lowercase_					,      lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample

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

    __A						:							List[str]      =						scheduler.step_correct(lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample
    __A						:							Optional[int]      =						new_scheduler.step_correct(
        lowercase_					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample

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



  def     A_					(self	:		Dict )		->				Tuple:
   for timesteps in [10, 100, 1_000]:
    self.check_over_configs(num_train_timesteps=lowercase_ )



  def     A_					(self	:		str )		->				int:
   for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]					,      [1, 100, 1_000] ):
    self.check_over_configs(sigma_min=lowercase_					,      sigma_max=lowercase_ )



  def     A_					(self	:		Any )		->				Optional[int]:
   for t in [0.1, 0.5, 0.75]:
    self.check_over_forward(time_step=lowercase_ )



  def     A_					(self	:		str )		->				List[str]:
   __A						:							str      =						dict(self.forward_default_kwargs )

   __A						:							Tuple      =						self.scheduler_classes[0]
   __A						:							Tuple      =						self.get_scheduler_config()
   __A						:							Optional[int]      =						scheduler_class(**lowercase_ )

   __A						:							Dict      =						3

   __A						:							Union[str, Any]      =						self.dummy_model()
   __A						:							Any      =						self.dummy_sample_deter

   scheduler.set_sigmas(lowercase_ )
   scheduler.set_timesteps(lowercase_ )
   __A						:							str      =						torch.manual_seed(0 )

   for i, t in enumerate(scheduler.timesteps ):
    __A						:							Union[str, Any]      =						scheduler.sigmas[i]

    for _ in range(scheduler.config.correct_steps ):
     with torch.no_grad():
      __A						:							Any      =						model(lowercase_					,      lowercase_ )
     __A						:							Dict      =						scheduler.step_correct(lowercase_					,      lowercase_					,      generator=lowercase_					,      **lowercase_ ).prev_sample

    with torch.no_grad():
     __A						:							str      =						model(lowercase_					,      lowercase_ )

    __A						:							Optional[Any]      =						scheduler.step_pred(lowercase_					,      lowercase_					,      lowercase_					,      generator=lowercase_					,      **lowercase_ )
    __A     ,       __A						:							List[str]      =						output.prev_sample, output.prev_sample_mean

   __A						:							Dict      =						torch.sum(torch.abs(lowercase_ ) )
   __A						:							str      =						torch.mean(torch.abs(lowercase_ ) )

   assert np.isclose(result_sum.item()					,      143_7275_8528.0 )
   assert np.isclose(result_mean.item()					,      1871_4530.0 )



  def     A_					(self	:		List[str] )		->				Optional[int]:
   __A						:							Tuple      =						dict(self.forward_default_kwargs )

   __A						:							str      =						kwargs.pop('num_inference_steps'					,      lowercase_ )

   for scheduler_class in self.scheduler_classes:
    __A						:							Tuple      =						self.get_scheduler_config()
    __A						:							str      =						scheduler_class(**lowercase_ )

    __A						:							List[Any]      =						self.dummy_sample
    __A						:							str      =						0.1 * sample

    if num_inference_steps is not None and hasattr(lowercase_					,      'set_timesteps' ):
     scheduler.set_timesteps(lowercase_ )
    elif num_inference_steps is not None and not hasattr(lowercase_					,      'set_timesteps' ):
     __A						:							Dict      =						num_inference_steps

    __A						:							Dict      =						scheduler.step_pred(lowercase_					,      0					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample
    __A						:							Optional[int]      =						scheduler.step_pred(lowercase_					,      1					,      lowercase_					,      generator=torch.manual_seed(0 )					,      **lowercase_ ).prev_sample

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













"""simple docstring"""


import numpy as np

_UpperCamelCase :    int      =			[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]






class __a :



  def __init__(self	:		Any )		->				None:
   __A						:							Any      =						np.array(lowercase_ )



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				np.ndarray:
   __A     ,       __A						:							Any      =						np.where(letter == self.SQUARE )
   __A						:							Tuple      =						np.concatenate([indexa + 1, indexa + 1] )
   return indexes



  def     A_					(self	:		Tuple					,      lowercase_	:		int					,      lowercase_	:		int )		->				str:
   return self.SQUARE[indexa - 1, indexa - 1]



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		str )		->				str:
   __A						:							Any      =						message.lower()
   __A						:							Optional[Any]      =						message.replace('j'					,      'i' )

   __A						:							Union[str, Any]      =						''
   for letter_index in range(len(lowercase_ ) ):
    if message[letter_index] != " ":
     __A						:							Union[str, Any]      =						self.letter_to_numbers(message[letter_index] )
     __A						:							Union[str, Any]      =						encoded_message + str(numbers[0] ) + str(numbers[1] )
    elif message[letter_index] == " ":
     __A						:							Optional[Any]      =						encoded_message + ' '

   return encoded_message



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				str:
   __A						:							List[str]      =						message.replace(' '					,      '  ' )
   __A						:							List[Any]      =						''
   for numbers_index in range(int(len(lowercase_ ) / 2 ) ):
    if message[numbers_index * 2] != " ":
     __A						:							Any      =						message[numbers_index * 2]
     __A						:							Tuple      =						message[numbers_index * 2 + 1]

     __A						:							Dict      =						self.numbers_to_letter(int(lowercase_ )					,      int(lowercase_ ) )
     __A						:							Union[str, Any]      =						decoded_message + letter
    elif message[numbers_index * 2] == " ":
     __A						:							int      =						decoded_message + ' '

   return decoded_message





