




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







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   Union[str, Any]=0.9999    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						UNetaDConditionModel.from_pretrained(self.model_id ,       subfolder='''unet'''    )
					A_					:			int						=						unet.to(_lowerCamelCase    )
					A_					:			Optional[int]						=						EMAModel(unet.parameters() ,       decay=_lowerCamelCase ,       model_cls=_lowerCamelCase ,       model_config=unet.config    )
					return unet, ema_unet







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						torch.randn(
					    self.batch_size ,       self.num_in_channels ,       self.latent_height ,       self.latent_width ,       generator=self.generator    ).to(_lowerCamelCase    )
					A_					:			Union[str, Any]						=						torch.randint(0 ,       10_00 ,       size=(self.batch_size,) ,       generator=self.generator    ).to(_lowerCamelCase    )
					A_					:			Optional[int]						=						torch.randn(
					    self.batch_size ,       self.prompt_length ,       self.text_encoder_hidden_dim ,       generator=self.generator    ).to(_lowerCamelCase    )
					return noisy_latents, timesteps, encoder_hidden_states







				def    a_					(							self				:   Optional[Any] ,       _lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

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







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_				,	A_					:			Tuple						=						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[int]    ):



					"""simple docstring"""

					A_				,	A_					:			str						=						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()    )
					A_					:			List[Any]						=						list(unet.parameters()    )
					for s_param, param in zip(ema_unet.shadow_params ,       _lowerCamelCase    ):
						assert torch.allclose(_lowerCamelCase ,       _lowerCamelCase    )

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







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_				,	A_					:			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.
					A_					:			List[Any]						=						self.simulate_backprop(_lowerCamelCase    )

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

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







				def    a_					(							self				:   Union[str, Any]    ):



					"""simple docstring"""

					A_				,	A_					:			Union[str, Any]						=						self.get_models()

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

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

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







				def    a_					(							self				:   int    ):



					"""simple docstring"""

					A_				,	A_					:			Union[str, Any]						=						self.get_models(decay=0.0    )
					A_					:			List[Any]						=						self.simulate_backprop(_lowerCamelCase    )
					ema_unet.step(unet_step_one.parameters()    )
					A_					:			Any						=						ema_unet.shadow_params

					A_					:			Dict						=						self.simulate_backprop(_lowerCamelCase    )
					ema_unet.step(unet_step_two.parameters()    )
					A_					:			Optional[Any]						=						ema_unet.shadow_params

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







				@skip_mps
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					A_				,	A_					:			List[Any]						=						self.get_models()
					A_				,	A_				,	A_					:			Optional[int]						=						self.get_dummy_inputs()

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

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

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











"""simple docstring"""

import bisect
import copy
from collections import defaultdict

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

from utils import logger

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						copy.deepcopy(_UpperCAmelCase		)
	A_					:			int						=						sorted(_UpperCAmelCase		)
	A_					:			Tuple						=						[bisect.bisect_right(_UpperCAmelCase					, _UpperCAmelCase		) for y in x]
	return quantized

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=0		):




	"""simple docstring"""






	A_					:			List[Any]						=						np.arange(start=3					, stop=_UpperCAmelCase					, step=4		).tolist() if k > 0 else [10]
	A_					:			List[Any]						=						_quantize(_UpperCAmelCase					, _UpperCAmelCase		)
	# count number of elements per group
	A_					:			Tuple						=						np.unique(_UpperCAmelCase					, return_counts=_UpperCAmelCase		)[1]
	A_					:			Optional[int]						=						[0] + bins + [np.inf]
	logger.info('''Using {} as bins for aspect lengths quantization'''.format(_UpperCAmelCase		)		)
	logger.info('''Count of instances per bin: {}'''.format(_UpperCAmelCase		)		)
	return groups

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""simple docstring"""

					if not isinstance(_lowerCamelCase ,       _lowerCamelCase    ):
						raise ValueError(
						    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_lowerCamelCase    )    )
					A_					:			List[str]						=						sampler
					A_					:			str						=						group_ids
					A_					:			Any						=						batch_size







				def __iter__(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			List[str]						=						defaultdict(_lowerCamelCase    )
					A_					:			Dict						=						defaultdict(_lowerCamelCase    )

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







				def __len__(							self				:   List[Any]    ):



					"""simple docstring"""

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





