




'''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						):




				'''simple docstring'''





				A       =							"hf-internal-testing/tiny-stable-diffusion-pipe"
				A       =							1
				A       =							7_7
				A       =							3_2
				A       =							4
				A       =							A       =							6_4
				A       =							torch.manual_seed(0						)
				def 			a_			(self					,    _UpperCAmelCase=0.9_999     )      ->			Union[str, Any]:
							__UpperCamelCase :     str										=  UNetaDConditionModel.from_pretrained(self.model_id					,    subfolder="unet"     )
							__UpperCamelCase :     List[str]										=  unet.to(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  EMAModel(unet.parameters()					,    decay=_UpperCAmelCase					,    model_cls=_UpperCAmelCase					,    model_config=unet.config     )
							return unet, ema_unet
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase :     Any										=  torch.randn(
							    self.batch_size					,    self.num_in_channels					,    self.latent_height					,    self.latent_width					,    generator=self.generator     ).to(_UpperCAmelCase     )
							__UpperCamelCase :     Tuple										=  torch.randint(0					,    1_0_0_0					,    size=(self.batch_size,)					,    generator=self.generator     ).to(_UpperCAmelCase     )
							__UpperCamelCase :     List[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 			a_			(self					,    _UpperCAmelCase     )      ->			Optional[int]:
							__UpperCamelCase :     Tuple										=  {}
							for k, param in unet.state_dict().items():
										__UpperCamelCase :     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 			a_			(self     )      ->			List[Any]:
							__UpperCamelCase						,   __UpperCamelCase :     List[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 			a_			(self     )      ->			Optional[Any]:
							__UpperCamelCase						,   __UpperCamelCase :     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()     )
							__UpperCamelCase :     Dict										=  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 			a_			(self     )      ->			Optional[int]:
							__UpperCamelCase						,   __UpperCamelCase :     Union[str, 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.
							__UpperCamelCase :     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.
							__UpperCamelCase :     List[Any]										=  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 			a_			(self     )      ->			List[str]:
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							__UpperCamelCase						,   __UpperCamelCase :     Any										=  self.get_models()

							# First backprop + EMA
							__UpperCamelCase :     List[Any]										=  self.simulate_backprop(_UpperCAmelCase     )
							ema_unet.step(unet_step_one.parameters()     )
							__UpperCamelCase :     Union[str, Any]										=  ema_unet.shadow_params

							# Second backprop + EMA
							__UpperCamelCase :     Dict										=  self.simulate_backprop(_UpperCAmelCase     )
							ema_unet.step(unet_step_two.parameters()     )
							__UpperCamelCase :     Union[str, Any]										=  ema_unet.shadow_params

							for step_one, step_two in zip(_UpperCAmelCase					,    _UpperCAmelCase     ):
										assert ~torch.allclose(_UpperCAmelCase					,    _UpperCAmelCase     )
				def 			a_			(self     )      ->			Dict:
							# 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.
							__UpperCamelCase						,   __UpperCamelCase :     Dict										=  self.get_models(decay=0.0     )
							__UpperCamelCase :     Union[str, Any]										=  self.simulate_backprop(_UpperCAmelCase     )
							ema_unet.step(unet_step_one.parameters()     )
							__UpperCamelCase :     Tuple										=  ema_unet.shadow_params

							__UpperCamelCase :     Tuple										=  self.simulate_backprop(_UpperCAmelCase     )
							ema_unet.step(unet_step_two.parameters()     )
							__UpperCamelCase :     int										=  ema_unet.shadow_params

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





				@skip_mps
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase						,   __UpperCamelCase :     Dict										=  self.get_models()
							__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase :     List[str]										=  self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(_UpperCAmelCase     )
										__UpperCamelCase :     List[Any]										=  UNetaDConditionModel.from_pretrained(_UpperCAmelCase					,    model_cls=_UpperCAmelCase     )
										__UpperCamelCase :     int										=  loaded_unet.to(unet.device     )

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

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









'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				@register_to_config
				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = False					,    )      ->			Tuple:
							super().__init__()

							__UpperCamelCase :     Optional[int]										=  nn.Linear(_UpperCAmelCase					,    _UpperCAmelCase					,    bias=_UpperCAmelCase     )

							__UpperCamelCase :     str										=  nn.Embedding(_UpperCAmelCase					,    _UpperCAmelCase     )
							__UpperCamelCase :     Any										=  False

							__UpperCamelCase :     Union[str, Any]										=  nn.Dropout(p=_UpperCAmelCase     )

							__UpperCamelCase :     Dict										=  TaConfig(
							    d_model=_UpperCAmelCase					,    num_heads=_UpperCAmelCase					,    d_kv=_UpperCAmelCase					,    d_ff=_UpperCAmelCase					,    feed_forward_proj=_UpperCAmelCase					,    dropout_rate=_UpperCAmelCase					,    is_decoder=_UpperCAmelCase					,    is_encoder_decoder=_UpperCAmelCase					,    )
							__UpperCamelCase :     Tuple										=  nn.ModuleList()
							for lyr_num in range(_UpperCAmelCase     ):
										__UpperCamelCase :     List[str]										=  TaBlock(_UpperCAmelCase     )
										self.encoders.append(_UpperCAmelCase     )

							__UpperCamelCase :     Tuple										=  TaLayerNorm(_UpperCAmelCase     )
							__UpperCamelCase :     Any										=  nn.Dropout(p=_UpperCAmelCase     )





				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			int:
							__UpperCamelCase :     Any										=  self.input_proj(_UpperCAmelCase     )

							# terminal relative positional encodings
							__UpperCamelCase :     Optional[Any]										=  encoder_inputs.shape[1]
							__UpperCamelCase :     Optional[int]										=  torch.arange(_UpperCAmelCase					,    device=encoder_inputs.device     )

							__UpperCamelCase :     Tuple										=  encoder_inputs_mask.sum(-1     )
							__UpperCamelCase :     Optional[Any]										=  torch.roll(input_positions.unsqueeze(0     )					,    tuple(seq_lens.tolist()     )					,    dims=0     )
							x += self.position_encoding(_UpperCAmelCase     )

							__UpperCamelCase :     Any										=  self.dropout_pre(_UpperCAmelCase     )

							# inverted the attention mask
							__UpperCamelCase :     Union[str, Any]										=  encoder_inputs.size()
							__UpperCamelCase :     Optional[Any]										=  self.get_extended_attention_mask(_UpperCAmelCase					,    _UpperCAmelCase     )

							for lyr in self.encoders:
										__UpperCamelCase :     List[Any]										=  lyr(_UpperCAmelCase					,    _UpperCAmelCase     )[0]
							__UpperCamelCase :     Union[str, Any]										=  self.layer_norm(_UpperCAmelCase     )

							return self.dropout_post(_UpperCAmelCase     ), encoder_inputs_mask



