




'''simple docstring'''


import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    Adafactor,
				    AdamW,
				    get_constant_schedule,
				    get_constant_schedule_with_warmup,
				    get_cosine_schedule_with_warmup,
				    get_cosine_with_hard_restarts_schedule_with_warmup,
				    get_inverse_sqrt_schedule,
				    get_linear_schedule_with_warmup,
				    get_polynomial_decay_schedule_with_warmup,
				)
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Optional[Any]=10 ):





							'''simple docstring'''


							_a      	=							[]
							for _ in range(UpperCamelCase ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
							return lrs
def 						snake_case_       (UpperCamelCase			:    Optional[Any]					,		UpperCamelCase			:    Tuple=10 ):





							'''simple docstring'''


							_a      	=							[]
							for step in range(UpperCamelCase ):
														lrs.append(scheduler.get_lr()[0] )
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												_a      	=							os.path.join(UpperCamelCase					,		'''schedule.bin''' )
																												torch.save(scheduler.state_dict()					,		UpperCamelCase )

																												_a      	=							torch.load(UpperCamelCase )
																												scheduler.load_state_dict(UpperCamelCase )
							return lrs

@require_torch
class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[Any]   )     ->				int:


														"""simple docstring"""


														self.assertEqual(len(lowerCAmelCase_   )				,     len(lowerCAmelCase_   )   )
														for a, b in zip(lowerCAmelCase_				,     lowerCAmelCase_   ):
																					self.assertAlmostEqual(lowerCAmelCase_				,     lowerCAmelCase_				,     delta=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Any:


														"""simple docstring"""


														_a      	=							torch.tensor([0.1, -0.2, -0.1]				,     requires_grad=lowerCAmelCase_   )
														_a      	=							torch.tensor([0.4, 0.2, -0.5]   )
														_a      	=							nn.MSELoss()
														# No warmup, constant schedule, no gradient clipping
														_a      	=							AdamW(params=[w]				,     lr=2e-1				,     weight_decay=0.0   )
														for _ in range(1_00   ):
																					_a      	=							criterion(lowerCAmelCase_				,     lowerCAmelCase_   )
																					loss.backward()
																					optimizer.step()
																					w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																					w.grad.zero_()
														self.assertListAlmostEqual(w.tolist()				,     [0.4, 0.2, -0.5]				,     tol=1e-2   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							torch.tensor([0.1, -0.2, -0.1]				,     requires_grad=lowerCAmelCase_   )
														_a      	=							torch.tensor([0.4, 0.2, -0.5]   )
														_a      	=							nn.MSELoss()
														# No warmup, constant schedule, no gradient clipping
														_a      	=							Adafactor(
														    params=[w]				,     lr=1e-2				,     eps=(1e-30, 1e-3)				,     clip_threshold=1.0				,     decay_rate=-0.8				,     betaa=lowerCAmelCase_				,     weight_decay=0.0				,     relative_step=lowerCAmelCase_				,     scale_parameter=lowerCAmelCase_				,     warmup_init=lowerCAmelCase_				,     )
														for _ in range(10_00   ):
																					_a      	=							criterion(lowerCAmelCase_				,     lowerCAmelCase_   )
																					loss.backward()
																					optimizer.step()
																					w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																					w.grad.zero_()
														self.assertListAlmostEqual(w.tolist()				,     [0.4, 0.2, -0.5]				,     tol=1e-2   )



@require_torch
class      A       (						unittest.TestCase       ):
							lowercase_							     =							nn.Linear(50	,50       ) if is_torch_available() else None
							lowercase_							     =							AdamW(m.parameters()	,lr=10.0       ) if is_torch_available() else None
							lowercase_							     =							10







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[int]=None   )     ->				Optional[int]:


														"""simple docstring"""


														self.assertEqual(len(lowerCAmelCase_   )				,     len(lowerCAmelCase_   )   )
														for a, b in zip(lowerCAmelCase_				,     lowerCAmelCase_   ):
																					self.assertAlmostEqual(lowerCAmelCase_				,     lowerCAmelCase_				,     delta=lowerCAmelCase_				,     msg=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
														# schedulers doct format
														# function: (sched_args_dict, expected_learning_rates)
														_a      	=							{
														    get_constant_schedule: ({}, [1_0.0] * self.num_steps),
														    get_constant_schedule_with_warmup: (
														        {'''num_warmup_steps''': 4},
														        [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
														    ),
														    get_linear_schedule_with_warmup: (
														        {**common_kwargs},
														        [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
														    ),
														    get_cosine_schedule_with_warmup: (
														        {**common_kwargs},
														        [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
														    ),
														    get_cosine_with_hard_restarts_schedule_with_warmup: (
														        {**common_kwargs, '''num_cycles''': 2},
														        [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
														    ),
														    get_polynomial_decay_schedule_with_warmup: (
														        {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
														        [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
														    ),
														    get_inverse_sqrt_schedule: (
														        {'''num_warmup_steps''': 2},
														        [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
														    ),
														}

														for scheduler_func, data in scheds.items():
																					_a	,				_a      	=							data

																					_a      	=							scheduler_func(self.optimizer				,     **lowerCAmelCase_   )
																					self.assertEqual(len([scheduler.get_lr()[0]]   )				,     1   )
																					_a      	=							unwrap_schedule(lowerCAmelCase_				,     self.num_steps   )
																					self.assertListAlmostEqual(
																					    lowerCAmelCase_				,     lowerCAmelCase_				,     tol=1e-2				,     msg=F'failed for {scheduler_func} in normal scheduler'				,     )

																					_a      	=							scheduler_func(self.optimizer				,     **lowerCAmelCase_   )
																					if scheduler_func.__name__ != "get_constant_schedule":
																												LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase_   )  # wrap to test picklability of the schedule
																					_a      	=							unwrap_and_save_reload_schedule(lowerCAmelCase_				,     self.num_steps   )
																					self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_				,     msg=F'failed for {scheduler_func} in save and reload'   )



class      A       :







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							fn







							def __call__(    self			:    List[str]				,     *lowerCAmelCase_			:    Optional[int]				,     **lowerCAmelCase_			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														return self.fn(*lowerCAmelCase_				,     **lowerCAmelCase_   )







							@classmethod
							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    List[str]   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							list(map(self				,     scheduler.lr_lambdas   )   )







'''simple docstring'''


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_snake_case : Optional[Any]  				=						8
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Dict=BITS ):





							'''simple docstring'''


							_a      	=							x.device

							_a      	=							(x * 255).int().clamp(0					,		255 )

							_a      	=							2 ** torch.arange(bits - 1					,		-1					,		-1					,		device=UpperCamelCase )
							_a      	=							rearrange(UpperCamelCase					,		'''d -> d 1 1''' )
							_a      	=							rearrange(UpperCamelCase					,		'''b c h w -> b c 1 h w''' )

							_a      	=							((x & mask) != 0).float()
							_a      	=							rearrange(UpperCamelCase					,		'''b c d h w -> b (c d) h w''' )
							_a      	=							bits * 2 - 1
							return bits
def 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    Any=BITS ):





							'''simple docstring'''


							_a      	=							x.device

							_a      	=							(x > 0).int()
							_a      	=							2 ** torch.arange(bits - 1					,		-1					,		-1					,		device=UpperCamelCase					,		dtype=torch.intaa )

							_a      	=							rearrange(UpperCamelCase					,		'''d -> d 1 1''' )
							_a      	=							rearrange(UpperCamelCase					,		'''b (c d) h w -> b c d h w'''					,		d=8 )
							_a      	=							reduce(x * mask					,		'''b c d h w -> b c h w'''					,		'''sum''' )
							return (dec / 255).clamp(0.0					,		1.0 )
def 						snake_case_       (self			:    Union[str, Any]					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    float = 0.0					,		UpperCamelCase			:    bool = True					,		UpperCamelCase			:    Any=None					,		UpperCamelCase			:    bool = True					,		):





							'''simple docstring'''


							if self.num_inference_steps is None:
														raise ValueError(
														    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							_a      	=							timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							_a      	=							self.alphas_cumprod[timestep]
							_a      	=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							_a      	=							1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_a      	=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							_a      	=							self.bit_scale
							if self.config.clip_sample:
														_a      	=							torch.clamp(UpperCamelCase					,		-scale					,		UpperCamelCase )

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							_a      	=							self._get_variance(UpperCamelCase					,		UpperCamelCase )
							_a      	=							eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														_a      	=							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_a      	=							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_a      	=							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														_a      	=							model_output.device if torch.is_tensor(UpperCamelCase ) else '''cpu'''
														_a      	=							torch.randn(model_output.shape					,		dtype=model_output.dtype					,		generator=UpperCamelCase ).to(UpperCamelCase )
														_a      	=							self._get_variance(UpperCamelCase					,		UpperCamelCase ) ** 0.5 * eta * noise

														_a      	=							prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=UpperCamelCase					,		pred_original_sample=UpperCamelCase )
def 						snake_case_       (self			:    Any					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    int					,		UpperCamelCase			:    torch.FloatTensor					,		UpperCamelCase			:    str="epsilon"					,		UpperCamelCase			:    Dict=None					,		UpperCamelCase			:    bool = True					,		):





							'''simple docstring'''


							_a      	=							timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														_a	,				_a      	=							torch.split(UpperCamelCase					,		sample.shape[1]					,		dim=1 )
							else:
														_a      	=							None

							# 1. compute alphas, betas
							_a      	=							self.alphas_cumprod[t]
							_a      	=							self.alphas_cumprod[t - 1] if t > 0 else self.one
							_a      	=							1 - alpha_prod_t
							_a      	=							1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if prediction_type == "epsilon":
														_a      	=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														_a      	=							model_output
							else:
														raise ValueError(f'Unsupported prediction_type {prediction_type}.' )

							# 3. Clip "predicted x_0"
							_a      	=							self.bit_scale
							if self.config.clip_sample:
														_a      	=							torch.clamp(UpperCamelCase					,		-scale					,		UpperCamelCase )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							_a      	=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							_a      	=							self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							_a      	=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							_a      	=							0
							if t > 0:
														_a      	=							torch.randn(
														    model_output.size()					,		dtype=model_output.dtype					,		layout=model_output.layout					,		generator=UpperCamelCase ).to(model_output.device )
														_a      	=							(self._get_variance(UpperCamelCase					,		predicted_variance=UpperCamelCase ) ** 0.5) * noise

							_a      	=							pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=UpperCamelCase					,		pred_original_sample=UpperCamelCase )

class      A       (						_a       ):







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    UNetaDConditionModel				,     lowerCAmelCase_			:    Union[DDIMScheduler, DDPMScheduler]				,     lowerCAmelCase_			:    Optional[float] = 1.0				,     )     ->				int:


														"""simple docstring"""


														super().__init__()
														_a      	=							bit_scale
														_a      	=							(
														    ddim_bit_scheduler_step if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ) else ddpm_bit_scheduler_step
														)

														self.register_modules(unet=lowerCAmelCase_				,     scheduler=lowerCAmelCase_   )







							@torch.no_grad()
							def __call__(    self			:    List[Any]				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 2_56				,     lowerCAmelCase_			:    Optional[int] = 50				,     lowerCAmelCase_			:    Optional[torch.Generator] = None				,     lowerCAmelCase_			:    Optional[int] = 1				,     lowerCAmelCase_			:    Optional[str] = "pil"				,     lowerCAmelCase_			:    bool = True				,     **lowerCAmelCase_			:    Any				,     )     ->				Union[Tuple, ImagePipelineOutput]:


														"""simple docstring"""


														_a      	=							torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)				,     generator=lowerCAmelCase_				,     )
														_a      	=							decimal_to_bits(lowerCAmelCase_   ) * self.bit_scale
														_a      	=							latents.to(self.device   )

														self.scheduler.set_timesteps(lowerCAmelCase_   )

														for t in self.progress_bar(self.scheduler.timesteps   ):
																					# predict the noise residual
																					_a      	=							self.unet(lowerCAmelCase_				,     lowerCAmelCase_   ).sample

																					# compute the previous noisy sample x_t -> x_t-1
																					_a      	=							self.scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   ).prev_sample

														_a      	=							bits_to_decimal(lowerCAmelCase_   )

														if output_type == "pil":
																					_a      	=							self.numpy_to_pil(lowerCAmelCase_   )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowerCAmelCase_   )

