



"""simple docstring"""







import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	torch.FloatTensor
		lowerCAmelCase_       :	Optional[torch.FloatTensor] 							=     None



def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=0.9_99				,      lowerCamelCase__="cosine"				,      ) ->							Optional[Any]:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__			):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(lowerCamelCase__			):
																					return math.exp(t * -12.0			)

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""			)

							A         =					[]
							for i in range(lowerCamelCase__			):
														A         =					i / num_diffusion_timesteps
														A         =					(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCamelCase__			) / alpha_bar_fn(lowerCamelCase__			)				,      lowerCamelCase__			)			)
							return torch.tensor(lowerCamelCase__				,      dtype=torch.floataa			)






class      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase     ):



		@register_to_config
		def __init__( self  :    List[str]      ,							snake_case  :    int = 1_000      ,							snake_case  :    str = "fixed_small_log"      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[float] = 1.0      ,							snake_case  :    str = "epsilon"      ,							snake_case  :    str = "squaredcos_cap_v2"      ,							)			->						List[Any]:






									'''simple docstring'''

									if beta_schedule != "squaredcos_cap_v2":
																raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''				)

									A         =					betas_for_alpha_bar(snake_case				)

									A         =					1.0 - self.betas
									A         =					torch.cumprod(self.alphas      ,							dim=0				)
									A         =					torch.tensor(1.0				)

									# standard deviation of the initial noise distribution
									A         =					1.0

									# setable values
									A         =					None
									A         =					torch.from_numpy(np.arange(0      ,							snake_case				)[::-1].copy()				)

									A         =					variance_type



		def 					A_		( self  :    Tuple      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    Optional[int] = None				)			->						torch.FloatTensor:






									'''simple docstring'''

									return sample



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    int      ,							snake_case  :    Union[str, torch.device] = None				)			->						int:






									'''simple docstring'''

									A         =					num_inference_steps
									A         =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
									A         =					(np.arange(0      ,							snake_case				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
									A         =					torch.from_numpy(snake_case				).to(snake_case				)



		def 					A_		( self  :    List[Any]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Union[str, Any]=None      ,							snake_case  :    Optional[Any]=None      ,							snake_case  :    Any=None				)			->						Union[str, Any]:






									'''simple docstring'''

									if prev_timestep is None:
																A         =					t - 1

									A         =					self.alphas_cumprod[t]
									A         =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
									A         =					1 - alpha_prod_t
									A         =					1 - alpha_prod_t_prev

									if prev_timestep == t - 1:
																A         =					self.betas[t]
									else:
																A         =					1 - alpha_prod_t / alpha_prod_t_prev

									# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
									# and sample from it to get previous sample
									# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
									A         =					beta_prod_t_prev / beta_prod_t * beta

									if variance_type is None:
																A         =					self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small_log":
																A         =					torch.log(torch.clamp(snake_case      ,							min=1E-2_0				)				)
																A         =					torch.exp(0.5 * variance				)
									elif variance_type == "learned_range":
																# NOTE difference with DDPM scheduler
																A         =					variance.log()
																A         =					beta.log()

																A         =					(predicted_variance + 1) / 2
																A         =					frac * max_log + (1 - frac) * min_log

									return variance



		def 					A_		( self  :    List[Any]      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    int      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    Optional[Any]=None      ,							snake_case  :    bool = True      ,							)			->						Union[UnCLIPSchedulerOutput, Tuple]:






									'''simple docstring'''

									A         =					timestep

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

									# 1. compute alphas, betas
									if prev_timestep is None:
																A         =					t - 1

									A         =					self.alphas_cumprod[t]
									A         =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
									A         =					1 - alpha_prod_t
									A         =					1 - alpha_prod_t_prev

									if prev_timestep == t - 1:
																A         =					self.betas[t]
																A         =					self.alphas[t]
									else:
																A         =					1 - alpha_prod_t / alpha_prod_t_prev
																A         =					1 - beta

									# 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 self.config.prediction_type == "epsilon":
																A         =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
																A         =					model_output
									else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																    ' for the UnCLIPScheduler.'				)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
																A         =					torch.clamp(
																    snake_case      ,							-self.config.clip_sample_range      ,							self.config.clip_sample_range				)

									# 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 * beta) / beta_prod_t
									A         =					alpha ** 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         =					randn_tensor(
																    model_output.shape      ,							dtype=model_output.dtype      ,							generator=snake_case      ,							device=model_output.device				)

																A         =					self._get_variance(
																    snake_case      ,							predicted_variance=snake_case      ,							prev_timestep=snake_case      ,							)

																if self.variance_type == "fixed_small_log":
																							A         =					variance
																elif self.variance_type == "learned_range":
																							A         =					(0.5 * variance).exp()
																else:
																							raise ValueError(
																							    f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																							    ' for the UnCLIPScheduler.'				)

																A         =					variance * variance_noise

									A         =					pred_prev_sample + variance

									if not return_dict:
																return (pred_prev_sample,)

									return UnCLIPSchedulerOutput(prev_sample=snake_case      ,							pred_original_sample=snake_case				)



		def 					A_		( self  :    Any      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.IntTensor      ,							)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					self.alphas_cumprod.to(device=original_samples.device      ,							dtype=original_samples.dtype				)
									A         =					timesteps.to(original_samples.device				)

									A         =					alphas_cumprod[timesteps] ** 0.5
									A         =					sqrt_alpha_prod.flatten()
									while len(sqrt_alpha_prod.shape				) < len(original_samples.shape				):
																A         =					sqrt_alpha_prod.unsqueeze(-1				)

									A         =					(1 - alphas_cumprod[timesteps]) ** 0.5
									A         =					sqrt_one_minus_alpha_prod.flatten()
									while len(sqrt_one_minus_alpha_prod.shape				) < len(original_samples.shape				):
																A         =					sqrt_one_minus_alpha_prod.unsqueeze(-1				)

									A         =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
									return noisy_samples











"""simple docstring"""







from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching

def   lowerCAmelCase__			(     ) ->							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

							A         =					'__test_patch_submodule_mock__'
							with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
														# 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   lowerCAmelCase__			(     ) ->							str:
							assert _test_patching.open is open

							A         =					'__test_patch_submodule_builtin_mock__'
							# _test_patching has "open" in its globals
							assert _test_patching.open is open
							with patch_submodule(_test_patching				,      'open'				,      lowerCamelCase__			):
														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   lowerCAmelCase__			(     ) ->							List[Any]:
							# pandas.read_csv is not present in _test_patching
							A         =					'__test_patch_submodule_missing_mock__'
							with patch_submodule(_test_patching				,      'pandas.read_csv'				,      lowerCamelCase__			):
														pass

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

def   lowerCAmelCase__			(     ) ->							Union[str, Any]:
							A         =					'__test_patch_submodule_start_and_stop_mock__'
							A         =					patch_submodule(_test_patching				,      'open'				,      lowerCamelCase__			)
							assert _test_patching.open is open
							patch.start()
							assert _test_patching.open is mock
							patch.stop()
							assert _test_patching.open is open

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

							A         =					'__test_patch_submodule_successive_join__'
							A         =					'__test_patch_submodule_successive_dirname__'
							A         =					'__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'				,      lowerCamelCase__			):
														with patch_submodule(_test_patching				,      'os.rename'				,      lowerCamelCase__			):
																					with patch_submodule(_test_patching				,      'os.path.dirname'				,      lowerCamelCase__			):
																												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'				,      lowerCamelCase__			):
														with patch_submodule(_test_patching				,      'os.path.join'				,      lowerCamelCase__			):
																					with patch_submodule(_test_patching				,      'os.path.dirname'				,      lowerCamelCase__			):
																												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   lowerCAmelCase__			(     ) ->							Optional[Any]:
							A         =					'__test_patch_submodule_doesnt_exist_mock__'
							with patch_submodule(_test_patching				,      '__module_that_doesn_exist__.__attribute_that_doesn_exist__'				,      lowerCamelCase__			):
														pass
							with patch_submodule(_test_patching				,      'os.__attribute_that_doesn_exist__'				,      lowerCamelCase__			):
														pass



