




"""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  ):
	A_				:					int      =   """hf-internal-testing/tiny-stable-diffusion-pipe"""
	A_				:					str      =   1
	A_				:					List[str]      =   7_7
	A_				:					Tuple      =   3_2
	A_				:					Dict      =   4
	A_				:					Dict      =   6_4
	A_				:					List[str]      =   torch.manual_seed(0  )




	def 				a_		( self	:						Any   ,     lowercase	:						Union[str, Any]=0.99_99						)       ->			Union[str, Any]:
						__snake_case        =						UNetaDConditionModel.from_pretrained(self.model_id   ,     subfolder="""unet"""						)
						__snake_case        =						unet.to(lowercase						)
						__snake_case        =						EMAModel(unet.parameters()   ,     decay=lowercase   ,     model_cls=lowercase   ,     model_config=unet.config						)
						return unet, ema_unet




	def 				a_		( self	:						Any						)       ->			List[str]:
						__snake_case        =						torch.randn(
						    self.batch_size   ,     self.num_in_channels   ,     self.latent_height   ,     self.latent_width   ,     generator=self.generator						).to(lowercase						)
						__snake_case        =						torch.randint(0   ,     1_000   ,     size=(self.batch_size,)   ,     generator=self.generator						).to(lowercase						)
						__snake_case        =						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	:						Optional[int]   ,     lowercase	:						Any						)       ->			Tuple:
						__snake_case        =						{}
						for k, param in unet.state_dict().items():
											__snake_case        =						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	:						int						)       ->			str:
						__snake_case   ,   __snake_case        =						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	:						Any						)       ->			List[Any]:
						__snake_case   ,   __snake_case        =						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()						)
						__snake_case        =						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	:						Tuple						)       ->			Optional[Any]:
						__snake_case   ,   __snake_case        =						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.
						__snake_case        =						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.
						__snake_case        =						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	:						Tuple						)       ->			int:
						# If we call EMA step after a backpropagation consecutively for two times,
						# the shadow params from those two steps should be different.
						__snake_case   ,   __snake_case        =						self.get_models()

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

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

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




	def 				a_		( self	:						Union[str, Any]						)       ->			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.
						__snake_case   ,   __snake_case        =						self.get_models(decay=0.0						)
						__snake_case        =						self.simulate_backprop(lowercase						)
						ema_unet.step(unet_step_one.parameters()						)
						__snake_case        =						ema_unet.shadow_params

						__snake_case        =						self.simulate_backprop(lowercase						)
						ema_unet.step(unet_step_two.parameters()						)
						__snake_case        =						ema_unet.shadow_params

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




	@skip_mps
	def 				a_		( self	:						Union[str, Any]						)       ->			Union[str, Any]:
						__snake_case   ,   __snake_case        =						self.get_models()
						__snake_case   ,   __snake_case   ,   __snake_case        =						self.get_dummy_inputs()

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

						# Since no EMA step has been performed the outputs should match.
						__snake_case        =						unet(lowercase   ,     lowercase   ,     lowercase						).sample
						__snake_case        =						loaded_unet(lowercase   ,     lowercase   ,     lowercase						).sample

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








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__			:     List[Any]			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[str]			       =				{}
class      A__   (   lowercase_  ):
	A_				:					Dict      =   """fsmt_decoder"""




	def __init__( self	:						List[Any]   ,     lowercase	:						Any=0   ,     lowercase	:						Dict=0						)       ->			str:
						super().__init__()
						__snake_case        =						vocab_size
						__snake_case        =						bos_token_id

class      A__   (   lowercase_  ):
	A_				:					Union[str, Any]      =   """fsmt"""
	A_				:					int      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						Optional[Any]   ,     lowercase	:						Any=["en", "de"]   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Tuple="relu"   ,     lowercase	:						Tuple=1_024   ,     lowercase	:						Union[str, Any]=200   ,     lowercase	:						List[str]=1_024   ,     lowercase	:						Tuple=4_096   ,     lowercase	:						int=12   ,     lowercase	:						Any=16   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						str=4_096   ,     lowercase	:						str=12   ,     lowercase	:						Optional[int]=16   ,     lowercase	:						Dict=0.0   ,     lowercase	:						Any=0.0   ,     lowercase	:						Union[str, Any]=0.1   ,     lowercase	:						Tuple=0.0   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						int=2   ,     lowercase	:						Any=True   ,     lowercase	:						int=True   ,     lowercase	:						int=False   ,     lowercase	:						List[str]=5   ,     lowercase	:						List[Any]=1.0   ,     lowercase	:						Union[str, Any]=False   ,     lowercase	:						Optional[Any]=True   ,     lowercase	:						int=1   ,     lowercase	:						Any=0   ,     lowercase	:						Optional[Any]=2   ,     lowercase	:						Any=2   ,     **lowercase	:						List[Any]   ,     )       ->			Union[str, Any]:
						__snake_case        =						langs
						__snake_case        =						src_vocab_size
						__snake_case        =						tgt_vocab_size
						__snake_case        =						d_model  # encoder_embed_dim and decoder_embed_dim

						__snake_case        =						encoder_ffn_dim
						__snake_case        =						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						max_position_embeddings
						__snake_case        =						init_std  # Normal(0, this parameter)
						__snake_case        =						activation_function

						__snake_case        =						DecoderConfig(vocab_size=lowercase   ,     bos_token_id=lowercase						)
						if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						dropout

						__snake_case        =						use_cache
						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     decoder_start_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     tie_word_embeddings=lowercase   ,     forced_eos_token_id=lowercase   ,     max_length=lowercase   ,     num_beams=lowercase   ,     length_penalty=lowercase   ,     early_stopping=lowercase   ,     **lowercase   ,     )




	def 				a_		( self	:						Union[str, Any]						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.decoder.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


