
'''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      ):
	snake_case__ :str				       =       """hf-internal-testing/tiny-stable-diffusion-pipe"""
	snake_case__ :Dict				       =       1
	snake_case__ :int				       =       77
	snake_case__ :Optional[int]				       =       32
	snake_case__ :Optional[Any]				       =       4
	snake_case__ :Optional[int]				       =       64
	snake_case__ :Union[str, Any]				       =       torch.manual_seed(0      )





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							__magic_name__     :    Dict=0.9999							):


				"""simple docstring"""

				lowerCAmelCase__									=							UNetaDConditionModel.from_pretrained(self.model_id ,							subfolder="unet"							)
				lowerCAmelCase__									=							unet.to(UpperCamelCase__							)
				lowerCAmelCase__									=							EMAModel(unet.parameters() ,							decay=UpperCamelCase__ ,							model_cls=UpperCamelCase__ ,							model_config=unet.config							)
				return unet, ema_unet





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							torch.randn(
				    self.batch_size ,							self.num_in_channels ,							self.latent_height ,							self.latent_width ,							generator=self.generator							).to(UpperCamelCase__							)
				lowerCAmelCase__									=							torch.randint(0 ,							1000 ,							size=(self.batch_size,) ,							generator=self.generator							).to(UpperCamelCase__							)
				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{}
				for k, param in unet.state_dict().items():
							lowerCAmelCase__									=							torch.randn_like(UpperCamelCase__							) + (param * torch.randn_like(UpperCamelCase__							))
							updated_state_dict.update({k: updated_param}							)
				unet.load_state_dict(UpperCamelCase__							)
				return unet





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							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__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							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__									=							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__									=							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 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_models()

				# First backprop + EMA
				lowerCAmelCase__									=							self.simulate_backprop(UpperCamelCase__							)
				ema_unet.step(unet_step_one.parameters()							)
				lowerCAmelCase__									=							ema_unet.shadow_params

				# Second backprop + EMA
				lowerCAmelCase__									=							self.simulate_backprop(UpperCamelCase__							)
				ema_unet.step(unet_step_two.parameters()							)
				lowerCAmelCase__									=							ema_unet.shadow_params

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_models(decay=0.0							)
				lowerCAmelCase__									=							self.simulate_backprop(UpperCamelCase__							)
				ema_unet.step(unet_step_one.parameters()							)
				lowerCAmelCase__									=							ema_unet.shadow_params

				lowerCAmelCase__									=							self.simulate_backprop(UpperCamelCase__							)
				ema_unet.step(unet_step_two.parameters()							)
				lowerCAmelCase__									=							ema_unet.shadow_params

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







	@skip_mps
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.get_models()
				lowerCAmelCase__									=							self.get_dummy_inputs()

				with tempfile.TemporaryDirectory() as tmpdir:
							ema_unet.save_pretrained(UpperCamelCase__							)
							lowerCAmelCase__									=							UNetaDConditionModel.from_pretrained(UpperCamelCase__ ,							model_cls=UpperCamelCase__							)
							lowerCAmelCase__									=							loaded_unet.to(unet.device							)

				# Since no EMA step has been performed the outputs should match.
				lowerCAmelCase__									=							unet(UpperCamelCase__ ,							UpperCamelCase__ ,							UpperCamelCase__							).sample
				lowerCAmelCase__									=							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/





