






"""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  UpperCamelCase    (			unittest.TestCase   ):
 UpperCamelCase      : Tuple    =      '''hf-internal-testing/tiny-stable-diffusion-pipe'''
 UpperCamelCase      : Tuple    =      1
 UpperCamelCase      : Dict    =      77
 UpperCamelCase      : List[str]    =      32
 UpperCamelCase      : List[Any]    =      4
 UpperCamelCase      : str    =      64
 UpperCamelCase      : Optional[Any]    =      torch.manual_seed(0   )


 def _lowercase			(  self	:		Optional[int]							,		UpperCAmelCase__	:		Union[str, Any]=0.9_9_9_9							)      -> Dict:
   _a						: int    =			UNetaDConditionModel.from_pretrained(self.model_id							,		subfolder="""unet"""							)
   _a						: Optional[Any]    =			unet.to(UpperCAmelCase__							)
   _a						: Dict    =			EMAModel(unet.parameters()							,		decay=UpperCAmelCase__							,		model_cls=UpperCAmelCase__							,		model_config=unet.config							)
   return unet, ema_unet


 def _lowercase			(  self	:		Union[str, Any]							)      -> List[Any]:
   _a						: List[Any]    =			torch.randn(
       self.batch_size							,		self.num_in_channels							,		self.latent_height							,		self.latent_width							,		generator=self.generator							).to(UpperCAmelCase__							)
   _a						: Dict    =			torch.randint(0							,		1000							,		size=(self.batch_size,)							,		generator=self.generator							).to(UpperCAmelCase__							)
   _a						: Optional[Any]    =			torch.randn(
       self.batch_size							,		self.prompt_length							,		self.text_encoder_hidden_dim							,		generator=self.generator							).to(UpperCAmelCase__							)
   return noisy_latents, timesteps, encoder_hidden_states


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


 def _lowercase			(  self	:		Union[str, Any]							)      -> Dict:
   _a   ,						_a						: str    =			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 _lowercase			(  self	:		Optional[int]							)      -> Any:
   _a   ,						_a						: List[Any]    =			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						: Optional[Any]    =			list(unet.parameters()							)
   for s_param, param in zip(ema_unet.shadow_params							,		UpperCAmelCase__							):
     assert torch.allclose(UpperCAmelCase__							,		UpperCAmelCase__							)

   # 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							,		UpperCAmelCase__							):
     assert torch.allclose(UpperCAmelCase__							,		UpperCAmelCase__							)


 def _lowercase			(  self	:		Any							)      -> Optional[Any]:
   _a   ,						_a						: 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						: Union[str, Any]    =			self.simulate_backprop(UpperCAmelCase__							)

   # 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						: int    =			list(unet_pseudo_updated_step_one.parameters()							)
   for s_param, param in zip(ema_unet.shadow_params							,		UpperCAmelCase__							):
     assert ~torch.allclose(UpperCAmelCase__							,		UpperCAmelCase__							)

   # 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							,		UpperCAmelCase__							):
     assert ~torch.allclose(UpperCAmelCase__							,		UpperCAmelCase__							)


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

   # First backprop + EMA
   _a						: List[Any]    =			self.simulate_backprop(UpperCAmelCase__							)
   ema_unet.step(unet_step_one.parameters()							)
   _a						: str    =			ema_unet.shadow_params

   # Second backprop + EMA
   _a						: str    =			self.simulate_backprop(UpperCAmelCase__							)
   ema_unet.step(unet_step_two.parameters()							)
   _a						: Dict    =			ema_unet.shadow_params

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


 def _lowercase			(  self	:		int							)      -> Union[str, Any]:
   # 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						: List[str]    =			self.simulate_backprop(UpperCAmelCase__							)
   ema_unet.step(unet_step_one.parameters()							)
   _a						: Dict    =			ema_unet.shadow_params

   _a						: int    =			self.simulate_backprop(UpperCAmelCase__							)
   ema_unet.step(unet_step_two.parameters()							)
   _a						: Optional[Any]    =			ema_unet.shadow_params

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







 @skip_mps
 def _lowercase			(  self	:		Optional[Any]							)      -> int:
   _a   ,						_a						: List[str]    =			self.get_models()
   _a   ,						_a   ,						_a						: str    =			self.get_dummy_inputs()

   with tempfile.TemporaryDirectory() as tmpdir:
     ema_unet.save_pretrained(UpperCAmelCase__							)
     _a						: Tuple    =			UNetaDConditionModel.from_pretrained(UpperCAmelCase__							,		model_cls=UpperCAmelCase__							)
     _a						: int    =			loaded_unet.to(unet.device							)

   # Since no EMA step has been performed the outputs should match.
   _a						: Any    =			unet(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							).sample
   _a						: Tuple    =			loaded_unet(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							).sample

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









"""simple docstring"""




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

