


import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Any   ,    lowercase__	:Optional[int]					)							->	Optional[int]:
						__magic_name__					:				Optional[int]				=   copy.deepcopy(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   sorted(lowercase__					)
						__magic_name__					:				Optional[Any]				=   [bisect.bisect_right(lowercase__   ,    lowercase__					) for y in x]
						return quantized
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[Any]   ,    lowercase__	:Optional[int]=0					)							->	List[str]:
						__magic_name__					:				int				=   np.arange(start=3   ,    stop=lowercase__   ,    step=4					).tolist() if k > 0 else [1_0]
						__magic_name__					:				Union[str, Any]				=   _quantize(lowercase__   ,    lowercase__					)
						# count number of elements per group
						__magic_name__					:				Dict				=   np.unique(lowercase__   ,    return_counts=lowercase__					)[1]
						__magic_name__					:				List[str]				=   [0] + bins + [np.inf]
						logger.info('Using {} as bins for aspect lengths quantization'.format(lowercase__					)					)
						logger.info('Count of instances per bin: {}'.format(lowercase__					)					)
						return groups



class      snake_case__	(   _A	):
				def __init__(	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										if not isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																raise ValueError(
																    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(_UpperCAmelCase   )   )
										__magic_name__					:				Dict				=   sampler
										__magic_name__					:				str				=   group_ids
										__magic_name__					:				Optional[int]				=   batch_size
				def __iter__(	self   ):




										"""simple docstring"""




										__magic_name__					:				int				=   defaultdict(_UpperCAmelCase   )
										__magic_name__					:				Optional[Any]				=   defaultdict(_UpperCAmelCase   )

										__magic_name__					:				Tuple				=   0
										for idx in self.sampler:
																__magic_name__					:				Optional[int]				=   self.group_ids[idx]
																buffer_per_group[group_id].append(_UpperCAmelCase   )
																samples_per_group[group_id].append(_UpperCAmelCase   )
																if len(buffer_per_group[group_id]   ) == self.batch_size:
																						yield buffer_per_group[group_id]  # TODO
																						num_batches += 1
																						del buffer_per_group[group_id]
																assert len(buffer_per_group[group_id]   ) < self.batch_size

										# now we have run out of elements that satisfy
										# the group criteria, let's return the remaining
										# elements so that the size of the sampler is
										# deterministic
										__magic_name__					:				List[Any]				=   len(self   )
										__magic_name__					:				Tuple				=   expected_num_batches - num_batches
										if num_remaining > 0:
																# for the remaining batches, group the batches by similar lengths
																__magic_name__					:				List[Any]				=   []
																for group_id, idxs in sorted(buffer_per_group.items()  ,   key=lambda _UpperCAmelCase   : x[0]   ):
																						batch_idx.extend(_UpperCAmelCase   )
																						if len(_UpperCAmelCase   ) >= self.batch_size:
																												yield batch_idx[: self.batch_size]
																												__magic_name__					:				int				=   batch_idx[self.batch_size :]
																												num_remaining -= 1
																if len(_UpperCAmelCase   ) > 0:
																						yield batch_idx
																						num_remaining -= 1
										assert num_remaining == 0





				def __len__(	self   ):




										"""simple docstring"""




										return (len(self.sampler   ) + self.batch_size - 1) // self.batch_size






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      snake_case__	(   unittest.TestCase	):
				__lowerCAmelCase		=   '''hf-internal-testing/tiny-stable-diffusion-pipe'''
				__lowerCAmelCase		=   1
				__lowerCAmelCase		=   77
				__lowerCAmelCase		=   32
				__lowerCAmelCase		=   4
				__lowerCAmelCase		=   __lowerCAmelCase		=   64
				__lowerCAmelCase		=   torch.manual_seed(0	)
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase=0.9999   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   UNetaDConditionModel.from_pretrained(self.model_id  ,   subfolder='unet'   )
										__magic_name__					:				int				=   unet.to(_UpperCAmelCase   )
										__magic_name__					:				Optional[int]				=   EMAModel(unet.parameters()  ,   decay=_UpperCAmelCase  ,   model_cls=_UpperCAmelCase  ,   model_config=unet.config   )
										return unet, ema_unet
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   torch.randn(
										    self.batch_size  ,   self.num_in_channels  ,   self.latent_height  ,   self.latent_width  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				List[str]				=   torch.randint(0  ,   1000  ,   size=(self.batch_size,)  ,   generator=self.generator   ).to(_UpperCAmelCase   )
										__magic_name__					:				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 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   {}
										for k, param in unet.state_dict().items():
																__magic_name__					:				str				=   torch.randn_like(_UpperCAmelCase   ) + (param * torch.randn_like(_UpperCAmelCase   ))
																updated_state_dict.update({k: updated_param}   )
										unet.load_state_dict(_UpperCAmelCase   )
										return unet
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Union[str, 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()   )
										__magic_name__					:				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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[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.
										__magic_name__					:				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.
										__magic_name__					:				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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				List[str]				=   self.get_models()

										# First backprop + EMA
										__magic_name__					:				int				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				Any				=   ema_unet.shadow_params

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

										for step_one, step_two in zip(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																assert ~torch.allclose(_UpperCAmelCase  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[Any]				=   self.get_models(decay=0.0   )
										__magic_name__					:				str				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_one.parameters()   )
										__magic_name__					:				List[Any]				=   ema_unet.shadow_params

										__magic_name__					:				Optional[Any]				=   self.simulate_backprop(_UpperCAmelCase   )
										ema_unet.step(unet_step_two.parameters()   )
										__magic_name__					:				Optional[Any]				=   ema_unet.shadow_params

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





				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[int]				=   self.get_models()
										__magic_name__		,			__magic_name__		,			__magic_name__					:				str				=   self.get_dummy_inputs()

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

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

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


