



"""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 SCREAMING_SNAKE_CASE__					(		__lowercase      ):
						_lowerCAmelCase							=					4_2
						_lowerCAmelCase							=					None







def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: List[Any]=0.9_99  ,       _lowerCamelCase				: Optional[int]="cosine"  ,       ):
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

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

							else:
														raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''					)

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







class SCREAMING_SNAKE_CASE__					(		__lowercase     ,					__lowercase      ):



						@register_to_config
						def __init__(self						,     _lowercase = 1000						,     _lowercase = "fixed_small_log"						,     _lowercase = True						,     _lowercase = 1.0						,     _lowercase = "epsilon"						,     _lowercase = "squaredcos_cap_v2"						,     ):





													'''simple docstring'''

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

													__a		:		List[Any]									=       betas_for_alpha_bar(_A							)

													__a		:		List[str]									=       1.0 - self.betas
													__a		:		Optional[int]									=       torch.cumprod(self.alphas						,     dim=0							)
													__a		:		Optional[Any]									=       torch.tensor(1.0							)

													# standard deviation of the initial noise distribution
													__a		:		List[Any]									=       1.0

													# setable values
													__a		:		int									=       None
													__a		:		str									=       torch.from_numpy(np.arange(0						,     _A							)[::-1].copy()							)

													__a		:		str									=       variance_type



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													return sample



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       num_inference_steps
													__a		:		Optional[Any]									=       (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
													__a		:		Any									=       (np.arange(0						,     _A							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
													__a		:		Optional[Any]									=       torch.from_numpy(_A							).to(_A							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None							):





													'''simple docstring'''

													if prev_timestep is None:
																				__a		:		Any									=       t - 1

													__a		:		Dict									=       self.alphas_cumprod[t]
													__a		:		Tuple									=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
													__a		:		List[Any]									=       1 - alpha_prod_t
													__a		:		Dict									=       1 - alpha_prod_t_prev

													if prev_timestep == t - 1:
																				__a		:		int									=       self.betas[t]
													else:
																				__a		:		List[Any]									=       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		:		Tuple									=       beta_prod_t_prev / beta_prod_t * beta

													if variance_type is None:
																				__a		:		int									=       self.config.variance_type

													# hacks - were probably added for training stability
													if variance_type == "fixed_small_log":
																				__a		:		List[Any]									=       torch.log(torch.clamp(_A						,     min=1e-20							)							)
																				__a		:		List[Any]									=       torch.exp(0.5 * variance							)
													elif variance_type == "learned_range":
																				# NOTE difference with DDPM scheduler
																				__a		:		List[str]									=       variance.log()
																				__a		:		List[Any]									=       beta.log()

																				__a		:		Union[str, Any]									=       (predicted_variance + 1) / 2
																				__a		:		Optional[Any]									=       frac * max_log + (1 - frac) * min_log

													return variance



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase = None						,     _lowercase=None						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		Tuple									=       timestep

													if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																				__a							,     __a		:		Union[str, Any]									=       torch.split(_A						,     sample.shape[1]						,     dim=1							)
													else:
																				__a		:		str									=       None

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

													__a		:		Dict									=       self.alphas_cumprod[t]
													__a		:		Any									=       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
													__a		:		Optional[int]									=       1 - alpha_prod_t
													__a		:		Optional[Any]									=       1 - alpha_prod_t_prev

													if prev_timestep == t - 1:
																				__a		:		Any									=       self.betas[t]
																				__a		:		List[Any]									=       self.alphas[t]
													else:
																				__a		:		Optional[Any]									=       1 - alpha_prod_t / alpha_prod_t_prev
																				__a		:		int									=       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		:		Tuple									=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													elif self.config.prediction_type == "sample":
																				__a		:		str									=       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		:		Dict									=       torch.clamp(
																				    _A						,     -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		:		int									=       (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
													__a		:		List[Any]									=       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		:		Optional[int]									=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

													# 6. Add noise
													__a		:		Optional[Any]									=       0
													if t > 0:
																				__a		:		str									=       randn_tensor(
																				    model_output.shape						,     dtype=model_output.dtype						,     generator=_A						,     device=model_output.device							)

																				__a		:		List[str]									=       self._get_variance(
																				    _A						,     predicted_variance=_A						,     prev_timestep=_A						,     )

																				if self.variance_type == "fixed_small_log":
																											__a		:		List[str]									=       variance
																				elif self.variance_type == "learned_range":
																											__a		:		int									=       (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		:		List[str]									=       variance * variance_noise

													__a		:		int									=       pred_prev_sample + variance

													if not return_dict:
																				return (pred_prev_sample,)

													return UnCLIPSchedulerOutput(prev_sample=_A						,     pred_original_sample=_A							)





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     ):





													'''simple docstring'''

													__a		:		Tuple									=       self.alphas_cumprod.to(device=original_samples.device						,     dtype=original_samples.dtype							)
													__a		:		Any									=       timesteps.to(original_samples.device							)

													__a		:		List[Any]									=       alphas_cumprod[timesteps] ** 0.5
													__a		:		Optional[int]									=       sqrt_alpha_prod.flatten()
													while len(sqrt_alpha_prod.shape							) < len(original_samples.shape							):
																				__a		:		List[str]									=       sqrt_alpha_prod.unsqueeze(-1							)

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

													__a		:		Union[str, Any]									=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
													return noisy_samples






"""simple docstring"""

from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def       __magic_name__    (   ):
							__a		:		Dict									=       {
							    """repo_name""": ["""test_repo1""", """test_repo2""", """test_repo3"""],
							    """path""": ["""test_1.py""", """test_2.py""", """unit_test.py"""],
							    """content""": ["""a """ * 2_0, """a """ * 3_0, """b """ * 7],
							}
							__a		:		Optional[Any]									=       Dataset.from_dict(_lowerCamelCase					)
							return dataset







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       get_dataset()
													__a		:		List[Any]									=       make_duplicate_clusters(_lowercase						,     0.85							)
													self.assertEqual(len(duplicate_clusters[0]							)						,     2							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       get_dataset()
													__a							,     __a		:		Optional[Any]									=       deduplicate_dataset(_lowercase							)
													self.assertEqual(len(_lowercase							)						,     2							)
													print(_lowercase							)
													self.assertEqual(duplicate_clusters[0][0]["""copies"""]						,     2							)
													self.assertEqual(duplicate_clusters[0][0]["""is_extreme"""]						,     _lowercase							)

