"""simple docstring"""






from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching




def 						SCREAMING_SNAKE_CASE					(  )							->   Optional[Any]:
				import os as original_os
				from os import path as original_path
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join

				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_mock__"
				with patch_submodule(_test_patching  ,					"os.path.join"  ,					__UpperCAmelCase       ):
								# Every way to access os.path.join must be patched, and the rest must stay untouched

								# check os.path.join
								assert isinstance(_test_patching.os  ,					_PatchedModuleObj       )
								assert isinstance(_test_patching.os.path  ,					_PatchedModuleObj       )
								assert _test_patching.os.path.join is mock

								# check path.join
								assert isinstance(_test_patching.path  ,					_PatchedModuleObj       )
								assert _test_patching.path.join is mock

								# check join
								assert _test_patching.join is mock

								# check that the other attributes are untouched
								assert _test_patching.os.rename is original_rename
								assert _test_patching.path.dirname is original_dirname
								assert _test_patching.os.path.dirname is original_dirname

								# Even renamed modules or objects must be patched

								# check renamed_os.path.join
								assert isinstance(_test_patching.renamed_os  ,					_PatchedModuleObj       )
								assert isinstance(_test_patching.renamed_os.path  ,					_PatchedModuleObj       )
								assert _test_patching.renamed_os.path.join is mock

								# check renamed_path.join
								assert isinstance(_test_patching.renamed_path  ,					_PatchedModuleObj       )
								assert _test_patching.renamed_path.join is mock

								# check renamed_join
								assert _test_patching.renamed_join is mock

								# check that the other attributes are untouched
								assert _test_patching.renamed_os.rename is original_rename
								assert _test_patching.renamed_path.dirname is original_dirname
								assert _test_patching.renamed_os.path.dirname is original_dirname

				# check that everthing is back to normal when the patch is over

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				assert _test_patching.open is open

				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_builtin_mock__"
				# _test_patching has "open" in its globals
				assert _test_patching.open is open
				with patch_submodule(_test_patching  ,					"open"  ,					__UpperCAmelCase       ):
								assert _test_patching.open is mock

				# check that everthing is back to normal when the patch is over

				assert _test_patching.open is open




def 						SCREAMING_SNAKE_CASE					(  )							->   Dict:
				# pandas.read_csv is not present in _test_patching
				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_missing_mock__"
				with patch_submodule(_test_patching  ,					"pandas.read_csv"  ,					__UpperCAmelCase       ):
								pass




def 						SCREAMING_SNAKE_CASE					(  )							->   str:
				# builtin should always be mocked even if they're not in the globals
				# in case they're loaded at one point
				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_missing_builtin_mock__"
				# _test_patching doesn't have "len" in its globals
				assert getattr(_test_patching  ,					"len"  ,					__UpperCAmelCase       ) is None
				with patch_submodule(_test_patching  ,					"len"  ,					__UpperCAmelCase       ):
								assert _test_patching.len is mock
				assert _test_patching.len is len




def 						SCREAMING_SNAKE_CASE					(  )							->   int:
				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_start_and_stop_mock__"
				SCREAMING_SNAKE_CASE__		  =				patch_submodule(_test_patching  ,					"open"  ,					__UpperCAmelCase       )
				assert _test_patching.open is open
				patch.start()
				assert _test_patching.open is mock
				patch.stop()
				assert _test_patching.open is open




def 						SCREAMING_SNAKE_CASE					(  )							->   Dict:
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_successive_join__"
				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_successive_dirname__"
				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_successive_rename__"
				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename

				with patch_submodule(_test_patching  ,					"os.path.join"  ,					__UpperCAmelCase       ):
								with patch_submodule(_test_patching  ,					"os.rename"  ,					__UpperCAmelCase       ):
												with patch_submodule(_test_patching  ,					"os.path.dirname"  ,					__UpperCAmelCase       ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

    # try another order
				with patch_submodule(_test_patching  ,					"os.rename"  ,					__UpperCAmelCase       ):
								with patch_submodule(_test_patching  ,					"os.path.join"  ,					__UpperCAmelCase       ):
												with patch_submodule(_test_patching  ,					"os.path.dirname"  ,					__UpperCAmelCase       ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename




def 						SCREAMING_SNAKE_CASE					(  )							->   Optional[int]:
				SCREAMING_SNAKE_CASE__		  =				"__test_patch_submodule_doesnt_exist_mock__"
				with patch_submodule(_test_patching  ,					"__module_that_doesn_exist__.__attribute_that_doesn_exist__"  ,					__UpperCAmelCase       ):
								pass
				with patch_submodule(_test_patching  ,					"os.__attribute_that_doesn_exist__"  ,					__UpperCAmelCase       ):
								pass





"""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


_A   					=    8




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=BITS       )							->   Union[str, Any]:
				SCREAMING_SNAKE_CASE__		  =				x.device

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

				SCREAMING_SNAKE_CASE__		  =				2 ** torch.arange(bits - 1  ,					-1  ,					-1  ,					device=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"d -> d 1 1"       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b c h w -> b c 1 h w"       )

				SCREAMING_SNAKE_CASE__		  =				((x & mask) != 0).float()
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b c d h w -> b (c d) h w"       )
				SCREAMING_SNAKE_CASE__		  =				bits * 2 - 1
				return bits




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=BITS       )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				x.device

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

				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"d -> d 1 1"       )
				SCREAMING_SNAKE_CASE__		  =				rearrange(__UpperCAmelCase  ,					"b (c d) h w -> b c d h w"  ,					d=8       )
				SCREAMING_SNAKE_CASE__		  =				reduce(x * mask  ,					"b c d h w -> b c h w"  ,					"sum"       )
				return (dec / 255).clamp(0.0  ,					1.0       )




def 						SCREAMING_SNAKE_CASE					(  self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 0.0  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase = True  ,					)							->   Union[DDIMSchedulerOutput, Tuple]:
				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)
				SCREAMING_SNAKE_CASE__		  =				timestep - self.config.num_train_timesteps // self.num_inference_steps

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

				SCREAMING_SNAKE_CASE__		  =				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
				SCREAMING_SNAKE_CASE__		  =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				SCREAMING_SNAKE_CASE__		  =				self.bit_scale
				if self.config.clip_sample:
								SCREAMING_SNAKE_CASE__		  =				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)
				SCREAMING_SNAKE_CASE__		  =				self._get_variance(__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								SCREAMING_SNAKE_CASE__		  =				(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
				SCREAMING_SNAKE_CASE__		  =				(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
				SCREAMING_SNAKE_CASE__		  =				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
								SCREAMING_SNAKE_CASE__		  =				model_output.device if torch.is_tensor(__UpperCAmelCase       ) else "cpu"
								SCREAMING_SNAKE_CASE__		  =				torch.randn(model_output.shape  ,					dtype=model_output.dtype  ,					generator=__UpperCAmelCase       ).to(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				self._get_variance(__UpperCAmelCase  ,					__UpperCAmelCase       ) ** 0.5 * eta * noise

								SCREAMING_SNAKE_CASE__		  =				prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase  ,					pred_original_sample=__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase="epsilon"  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase = True  ,					)							->   Union[DDPMSchedulerOutput, Tuple]:
				SCREAMING_SNAKE_CASE__		  =				timestep

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

				# 1. compute alphas, betas
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE__		  =				self.alphas_cumprod[t - 1] if t > 0 else self.one
				SCREAMING_SNAKE_CASE__		  =				1 - alpha_prod_t
				SCREAMING_SNAKE_CASE__		  =				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":
								SCREAMING_SNAKE_CASE__		  =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								SCREAMING_SNAKE_CASE__		  =				model_output
				else:
								raise ValueError(F"""Unsupported prediction_type {prediction_type}."""       )

				# 3. Clip "predicted x_0"
				SCREAMING_SNAKE_CASE__		  =				self.bit_scale
				if self.config.clip_sample:
								SCREAMING_SNAKE_CASE__		  =				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
				SCREAMING_SNAKE_CASE__		  =				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				SCREAMING_SNAKE_CASE__		  =				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
				SCREAMING_SNAKE_CASE__		  =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

				SCREAMING_SNAKE_CASE__		  =				pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase  ,					pred_original_sample=__UpperCAmelCase       )

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Optional[int]   , _snake_case			:				UNetaDConditionModel   , _snake_case			:				Union[DDIMScheduler, DDPMScheduler]   , _snake_case			:				Optional[float] = 1.0   , )							->       Optional[int]:
									super().__init__()
									SCREAMING_SNAKE_CASE__		  =				bit_scale
									SCREAMING_SNAKE_CASE__		  =				(
									    ddim_bit_scheduler_step if isinstance(_snake_case   , _snake_case		) else ddpm_bit_scheduler_step
									)

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



					@torch.no_grad()
					def __call__(						self			:				Any   , _snake_case			:				Optional[int] = 256   , _snake_case			:				Optional[int] = 256   , _snake_case			:				Optional[int] = 50   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				Optional[int] = 1   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , **_snake_case			:				Tuple   , )							->       Union[Tuple, ImagePipelineOutput]:
									SCREAMING_SNAKE_CASE__		  =				torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)   , generator=_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				decimal_to_bits(_snake_case		) * self.bit_scale
									SCREAMING_SNAKE_CASE__		  =				latents.to(self.device		)

									self.scheduler.set_timesteps(_snake_case		)

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

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

									SCREAMING_SNAKE_CASE__		  =				bits_to_decimal(_snake_case		)

									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




