






"""simple docstring"""


import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()






class __a ( unittest.TestCase					):
  __lowercase     :     Tuple       						=       'hf-internal-testing/tiny-stable-diffusion-pipe'
  __lowercase     :     Dict       						=       1
  __lowercase     :     Tuple       						=       77
  __lowercase     :     Dict       						=       32
  __lowercase     :     Tuple       						=       4
  __lowercase     :     Any       						=       64
  __lowercase     :     Optional[int]       						=       torch.manual_seed(0					)



  def     A_					(self	:		List[str]					,      lowercase_	:		Tuple=0.9999 )		->				Tuple:
   __A						:							Optional[int]      =						UNetaDConditionModel.from_pretrained(self.model_id					,      subfolder='unet' )
   __A						:							str      =						unet.to(lowercase_ )
   __A						:							Dict      =						EMAModel(unet.parameters()					,      decay=lowercase_					,      model_cls=lowercase_					,      model_config=unet.config )
   return unet, ema_unet



  def     A_					(self	:		int )		->				str:
   __A						:							Optional[int]      =						torch.randn(
       self.batch_size					,      self.num_in_channels					,      self.latent_height					,      self.latent_width					,      generator=self.generator ).to(lowercase_ )
   __A						:							Optional[int]      =						torch.randint(0					,      1_000					,      size=(self.batch_size,)					,      generator=self.generator ).to(lowercase_ )
   __A						:							Union[str, Any]      =						torch.randn(
       self.batch_size					,      self.prompt_length					,      self.text_encoder_hidden_dim					,      generator=self.generator ).to(lowercase_ )
   return noisy_latents, timesteps, encoder_hidden_states



  def     A_					(self	:		List[Any]					,      lowercase_	:		Optional[Any] )		->				Optional[int]:
   __A						:							Union[str, Any]      =						{}
   for k, param in unet.state_dict().items():
    __A						:							List[Any]      =						torch.randn_like(lowercase_ ) + (param * torch.randn_like(lowercase_ ))
    updated_state_dict.update({k: updated_param} )
   unet.load_state_dict(lowercase_ )
   return unet



  def     A_					(self	:		Any )		->				Any:
   __A     ,       __A						:							Optional[Any]      =						self.get_models()
   # Take the first (hypothetical) EMA step.
   ema_unet.step(unet.parameters() )
   assert ema_unet.optimization_step == 1

   # Take two more.
   for _ in range(2 ):
    ema_unet.step(unet.parameters() )
   assert ema_unet.optimization_step == 3



  def     A_					(self	:		Tuple )		->				Any:
   __A     ,       __A						:							Tuple      =						self.get_models()
   # Since the `unet` is not being updated (i.e., backprop'd)
   # there won't be any difference between the `params` of `unet`
   # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
   ema_unet.step(unet.parameters() )
   __A						:							List[str]      =						list(unet.parameters() )
   for s_param, param in zip(ema_unet.shadow_params					,      lowercase_ ):
    assert torch.allclose(lowercase_					,      lowercase_ )

   # The above holds true even if we call `ema.step()` multiple times since
   # `unet` params are still not being updated.
   for _ in range(4 ):
    ema_unet.step(unet.parameters() )
   for s_param, param in zip(ema_unet.shadow_params					,      lowercase_ ):
    assert torch.allclose(lowercase_					,      lowercase_ )



  def     A_					(self	:		int )		->				Optional[Any]:
   __A     ,       __A						:							Optional[int]      =						self.get_models()
   # Here we simulate the parameter updates for `unet`. Since there might
   # be some parameters which are initialized to zero we take extra care to
   # initialize their values to something non-zero before the multiplication.
   __A						:							Tuple      =						self.simulate_backprop(lowercase_ )

   # Take the EMA step.
   ema_unet.step(unet_pseudo_updated_step_one.parameters() )

   # Now the EMA'd parameters won't be equal to the original model parameters.
   __A						:							Optional[int]      =						list(unet_pseudo_updated_step_one.parameters() )
   for s_param, param in zip(ema_unet.shadow_params					,      lowercase_ ):
    assert ~torch.allclose(lowercase_					,      lowercase_ )

   # Ensure this is the case when we take multiple EMA steps.
   for _ in range(4 ):
    ema_unet.step(unet.parameters() )
   for s_param, param in zip(ema_unet.shadow_params					,      lowercase_ ):
    assert ~torch.allclose(lowercase_					,      lowercase_ )



  def     A_					(self	:		Optional[int] )		->				List[Any]:
   # If we call EMA step after a backpropagation consecutively for two times,
   # the shadow params from those two steps should be different.
   __A     ,       __A						:							str      =						self.get_models()

   # First backprop + EMA
   __A						:							Optional[int]      =						self.simulate_backprop(lowercase_ )
   ema_unet.step(unet_step_one.parameters() )
   __A						:							int      =						ema_unet.shadow_params

   # Second backprop + EMA
   __A						:							str      =						self.simulate_backprop(lowercase_ )
   ema_unet.step(unet_step_two.parameters() )
   __A						:							Optional[int]      =						ema_unet.shadow_params

   for step_one, step_two in zip(lowercase_					,      lowercase_ ):
    assert ~torch.allclose(lowercase_					,      lowercase_ )



  def     A_					(self	:		Tuple )		->				str:
   # If there's no decay even if there are backprops, EMA steps
   # won't take any effect i.e., the shadow params would remain the
   # same.
   __A     ,       __A						:							Dict      =						self.get_models(decay=0.0 )
   __A						:							Dict      =						self.simulate_backprop(lowercase_ )
   ema_unet.step(unet_step_one.parameters() )
   __A						:							Optional[Any]      =						ema_unet.shadow_params

   __A						:							Optional[Any]      =						self.simulate_backprop(lowercase_ )
   ema_unet.step(unet_step_two.parameters() )
   __A						:							List[Any]      =						ema_unet.shadow_params

   for step_one, step_two in zip(lowercase_					,      lowercase_ ):
    assert torch.allclose(lowercase_					,      lowercase_ )



  @skip_mps
  def     A_					(self	:		Dict )		->				Tuple:
   __A     ,       __A						:							str      =						self.get_models()
   __A     ,       __A     ,       __A						:							Union[str, Any]      =						self.get_dummy_inputs()

   with tempfile.TemporaryDirectory() as tmpdir:
    ema_unet.save_pretrained(lowercase_ )
    __A						:							Optional[int]      =						UNetaDConditionModel.from_pretrained(lowercase_					,      model_cls=lowercase_ )
    __A						:							int      =						loaded_unet.to(unet.device )

   # Since no EMA step has been performed the outputs should match.
   __A						:							Union[str, Any]      =						unet(lowercase_					,      lowercase_					,      lowercase_ ).sample
   __A						:							List[Any]      =						loaded_unet(lowercase_					,      lowercase_					,      lowercase_ ).sample

   assert torch.allclose(lowercase_					,      lowercase_					,      atol=1E-4 )













"""simple docstring"""


import math




def 					a			(  UpperCamelCase_				:					int		)	->		str:
 if number == 0:
  __A						:							Dict      =						'Zero'
  return words
 else:
  __A						:							List[str]      =						math.logaa(UpperCamelCase_		)
  __A						:							Union[str, Any]      =						digits + 1
  __A						:							List[str]      =						{}
  __A						:							int      =						''
  __A						:							Any      =						'One'
  __A						:							Tuple      =						'Two'
  __A						:							Any      =						'Three'
  __A						:							Any      =						'Four'
  __A						:							Optional[Any]      =						'Five'
  __A						:							List[Any]      =						'Six'
  __A						:							List[Any]      =						'Seven'
  __A						:							Dict      =						'Eight'
  __A						:							Union[str, Any]      =						'Nine'

  __A						:							List[Any]      =						{}
  __A						:							Tuple      =						''
  __A						:							List[Any]      =						'Twenty'
  __A						:							Optional[Any]      =						'Thirty'
  __A						:							Any      =						'Forty'
  __A						:							Optional[int]      =						'Fifty'
  __A						:							Dict      =						'Sixty'
  __A						:							Dict      =						'Seventy'
  __A						:							Optional[Any]      =						'Eighty'
  __A						:							Any      =						'Ninety'

  __A						:							int      =						{}
  __A						:							List[str]      =						'Ten'
  __A						:							Optional[Any]      =						'Eleven'
  __A						:							Dict      =						'Twelve'
  __A						:							Union[str, Any]      =						'Thirteen'
  __A						:							Union[str, Any]      =						'Fourteen'
  __A						:							Dict      =						'Fifteen'
  __A						:							str      =						'Sixteen'
  __A						:							str      =						'Seventeen'
  __A						:							Union[str, Any]      =						'Eighteen'
  __A						:							Union[str, Any]      =						'Nineteen'

  __A						:							Optional[Any]      =						{}
  __A						:							Optional[int]      =						'Hundred,'
  __A						:							Union[str, Any]      =						'Thousand,'
  __A						:							Union[str, Any]      =						'Lakh,'
  __A						:							Union[str, Any]      =						'Crore,'

  __A						:							Optional[Any]      =						number
  __A						:							Optional[Any]      =						''
  __A						:							List[str]      =						0
  __A						:							List[str]      =						int(UpperCamelCase_		)
  while counter < digits:
   __A						:							Any      =						temp_num % 1_0
   if counter % 2 == 0:
    __A						:							Optional[Any]      =						''
    if counter in placevalue and current != 0:
     __A						:							Union[str, Any]      =						placevalue[counter]
    if counter == 2:
     __A						:							Dict      =						singles[current] + addition + words
    elif counter == 0:
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Any      =						teens[current] + addition + words
      __A						:							int      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							Optional[Any]      =						singles[current] + addition + words

    else:
     __A						:							str      =						doubles[current] + addition + words

   else:
    if counter == 1:
     if current == 1:
      __A						:							List[Any]      =						teens[number % 1_0] + words
     else:
      __A						:							Union[str, Any]      =						''
      if counter in placevalue:
       __A						:							int      =						placevalue[counter]
      __A						:							Optional[Any]      =						doubles[current] + addition + words
    else:
     __A						:							Any      =						''
     if counter in placevalue:
      if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
       __A						:							Optional[int]      =						''
      else:
       __A						:							Optional[int]      =						placevalue[counter]
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Dict      =						teens[current] + addition + words
      __A						:							Any      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							List[str]      =						singles[current] + addition + words
   counter += 1
   __A						:							Dict      =						temp_num // 1_0
 return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()





