




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					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				"""hf-internal-testing/tiny-stable-diffusion-pipe"""
    lowerCamelCase_				:     Dict     					=				1
    lowerCamelCase_				:     int     					=				7_7
    lowerCamelCase_				:     Optional[int]     					=				3_2
    lowerCamelCase_				:     Optional[Any]     					=				4
    lowerCamelCase_				:     Optional[int]     					=				6_4
    lowerCamelCase_				:     Union[str, Any]     					=				torch.manual_seed(0					)







    def    _lowercase					(   self					,  UpperCamelCase__=0.9999					)					->				Tuple:
           lowerCamelCase       :				Union[str, Any]      			= UNetaDConditionModel.from_pretrained(self.model_id					,  subfolder="unet"					)
           lowerCamelCase       :				Any      			= unet.to(UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= EMAModel(unet.parameters()					,  decay=UpperCamelCase__					,  model_cls=UpperCamelCase__					,  model_config=unet.config					)
           return unet, ema_unet







    def    _lowercase					(   self					)					->				str:
           lowerCamelCase       :				List[str]      			= torch.randn(
               self.batch_size					,  self.num_in_channels					,  self.latent_height					,  self.latent_width					,  generator=self.generator					).to(UpperCamelCase__					)
           lowerCamelCase       :				List[Any]      			= torch.randint(0					,  1000					,  size=(self.batch_size,)					,  generator=self.generator					).to(UpperCamelCase__					)
           lowerCamelCase       :				Union[str, 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					,  UpperCamelCase__					)					->				Optional[int]:
           lowerCamelCase       :				List[Any]      			= {}
           for k, param in unet.state_dict().items():
                  lowerCamelCase       :				Optional[int]      			= 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					)					->				int:
           lowerCamelCase			,				lowerCamelCase       :				int      			= 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					)					->				List[str]:
           lowerCamelCase			,				lowerCamelCase       :				List[str]      			= 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()					)
           lowerCamelCase       :				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					)					->				str:
           lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= 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.
           lowerCamelCase       :				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.
           lowerCamelCase       :				Dict      			= 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					)					->				Optional[int]:
           # If we call EMA step after a backpropagation consecutively for two times,
           # the shadow params from those two steps should be different.
           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= self.get_models()

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

           # Second backprop + EMA
           lowerCamelCase       :				Optional[Any]      			= self.simulate_backprop(UpperCamelCase__					)
           ema_unet.step(unet_step_two.parameters()					)
           lowerCamelCase       :				Dict      			= ema_unet.shadow_params

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







    def    _lowercase					(   self					)					->				int:
           # 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.
           lowerCamelCase			,				lowerCamelCase       :				List[str]      			= self.get_models(decay=0.0					)
           lowerCamelCase       :				Tuple      			= self.simulate_backprop(UpperCamelCase__					)
           ema_unet.step(unet_step_one.parameters()					)
           lowerCamelCase       :				Optional[int]      			= ema_unet.shadow_params

           lowerCamelCase       :				Union[str, Any]      			= self.simulate_backprop(UpperCamelCase__					)
           ema_unet.step(unet_step_two.parameters()					)
           lowerCamelCase       :				int      			= ema_unet.shadow_params

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







    @skip_mps
    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= self.get_models()
           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				int      			= self.get_dummy_inputs()

           with tempfile.TemporaryDirectory() as tmpdir:
                  ema_unet.save_pretrained(UpperCamelCase__					)
                  lowerCamelCase       :				str      			= UNetaDConditionModel.from_pretrained(UpperCamelCase__					,  model_cls=UpperCamelCase__					)
                  lowerCamelCase       :				List[str]      			= loaded_unet.to(unet.device					)

           # Since no EMA step has been performed the outputs should match.
           lowerCamelCase       :				List[str]      			= unet(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					).sample
           lowerCamelCase       :				Tuple      			= loaded_unet(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					).sample

           assert torch.allclose(UpperCamelCase__					,  UpperCamelCase__					,  atol=1e-4					)












# 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/






