


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 (     UpperCAmelCase__   ,UpperCAmelCase__							)     ->   Optional[Any]:

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = copy.deepcopy(lowercase_							)
				_SCREAMING_SNAKE_CASE      = sorted(lowercase_							)
				_SCREAMING_SNAKE_CASE      = [bisect.bisect_right(lowercase_   ,lowercase_							) for y in x]
				return quantized


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=0							)     ->   Optional[Any]:

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = np.arange(start=3   ,stop=lowercase_   ,step=4							).tolist() if k > 0 else [10]
				_SCREAMING_SNAKE_CASE      = _quantize(lowercase_   ,lowercase_							)
				# count number of elements per group
				_SCREAMING_SNAKE_CASE      = np.unique(lowercase_   ,return_counts=lowercase_							)[1]
				_SCREAMING_SNAKE_CASE      = [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    __lowercase  (       __UpperCAmelCase							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_				, A_	)->	Any:

											if not isinstance(lowercase_				, lowercase_	):
															raise ValueError(
															    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_	)	)
											_SCREAMING_SNAKE_CASE      = sampler
											_SCREAMING_SNAKE_CASE      = group_ids
											_SCREAMING_SNAKE_CASE      = batch_size





							def __iter__(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = defaultdict(lowercase_	)
											_SCREAMING_SNAKE_CASE      = defaultdict(lowercase_	)

											_SCREAMING_SNAKE_CASE      = 0
											for idx in self.sampler:
															_SCREAMING_SNAKE_CASE      = self.group_ids[idx]
															buffer_per_group[group_id].append(lowercase_	)
															samples_per_group[group_id].append(lowercase_	)
															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
											_SCREAMING_SNAKE_CASE      = len(self	)
											_SCREAMING_SNAKE_CASE      = expected_num_batches - num_batches
											if num_remaining > 0:
															# for the remaining batches, group the batches by similar lengths
															_SCREAMING_SNAKE_CASE      = []
															for group_id, idxs in sorted(buffer_per_group.items()				, key=lambda A_	: x[0]	):
																			batch_idx.extend(lowercase_	)
																			if len(lowercase_	) >= self.batch_size:
																							yield batch_idx[: self.batch_size]
																							_SCREAMING_SNAKE_CASE      = batch_idx[self.batch_size :]
																							num_remaining -= 1
															if len(lowercase_	) > 0:
																			yield batch_idx
																			num_remaining -= 1
											assert num_remaining == 0





							def __len__(    self	)->	int:
											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    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


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





							def 		__magic_name__			(    self				, A_=0.9999	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(self.model_id				, subfolder='unet'	)
											_SCREAMING_SNAKE_CASE      = unet.to(A_	)
											_SCREAMING_SNAKE_CASE      = EMAModel(unet.parameters()				, decay=A_				, model_cls=A_				, model_config=unet.config	)
											return unet, ema_unet





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.num_in_channels				, self.latent_height				, self.latent_width				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randint(0				, 1000				, size=(self.batch_size,)				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.prompt_length				, self.text_encoder_hidden_dim				, generator=self.generator	).to(A_	)
											return noisy_latents, timesteps, encoder_hidden_states





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





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_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 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_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()	)
											_SCREAMING_SNAKE_CASE      = list(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)

											# 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				, A_	):
															assert torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_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.
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)

											# 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.
											_SCREAMING_SNAKE_CASE      = list(unet_pseudo_updated_step_one.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)

											# 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				, A_	):
															assert ~torch.allclose(A_				, A_	)





							def 		__magic_name__			(    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.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()

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

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

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





							def 		__magic_name__			(    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.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models(decay=0.0	)
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

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





							@skip_mps
							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_dummy_inputs()

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

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

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