



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	a						(				_UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: Union[str, Any]=0.9_99			,       _UpperCAmelCase							: Dict="cosine"			,       ):







		'''simple docstring'''





		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(_UpperCAmelCase							: Union[str, Any]						):
						return math.exp(t * -12.0						)

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

		__UpperCAmelCase   :					Dict													=     []
		for i in range(_UpperCAmelCase						):
				__UpperCAmelCase   :					Union[str, Any]													=     i / num_diffusion_timesteps
				__UpperCAmelCase   :					str													=     (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase						) / alpha_bar_fn(_UpperCAmelCase						)			,       _UpperCAmelCase						)						)
		return torch.tensor(_UpperCAmelCase			,       dtype=torch.floataa						)

class 		UpperCAmelCase__      (      __UpperCamelCase						,__UpperCamelCase       ):




							'''simple docstring'''






							UpperCamelCase      						=	[e.name for e in KarrasDiffusionSchedulers]
							UpperCamelCase      						=	2




							@register_to_config
							def __init__(       self							:			Tuple	,	a_							:			int = 10_00	,	a_							:			float = 0.0_0_0_8_5	,	a_							:			float = 0.0_1_2	,	a_							:			str = "linear"	,	a_							:			Optional[Union[np.ndarray, List[float]]] = None	,	a_							:			str = "epsilon"	,	a_							:			str = "linspace"	,	a_							:			int = 0	,	):
									'''simple docstring'''





									if trained_betas is not None:
											__UpperCAmelCase   :					Optional[int]													=     torch.tensor(a_	,	dtype=torch.floataa			)
									elif beta_schedule == "linear":
											__UpperCAmelCase   :					Optional[int]													=     torch.linspace(a_	,	a_	,	a_	,	dtype=torch.floataa			)
									elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											__UpperCAmelCase   :					str													=     (
											    torch.linspace(beta_start**0.5	,	beta_end**0.5	,	a_	,	dtype=torch.floataa			) ** 2
											)
									elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											__UpperCAmelCase   :					Union[str, Any]													=     betas_for_alpha_bar(a_			)
									else:
											raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'			)

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

									#  set all values
									self.set_timesteps(a_	,	a_	,	a_			)




							def 			snake_case__					(       self							:			List[str]	,	a_							:			Union[str, Any]	,	a_							:			Tuple=None			):
									'''simple docstring'''





									if schedule_timesteps is None:
											__UpperCAmelCase   :					Any													=     self.timesteps

									__UpperCAmelCase   :					Optional[int]													=     (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter			) == 0:
											__UpperCAmelCase   :					Any													=     1 if len(a_			) > 1 else 0
									else:
											__UpperCAmelCase   :					Any													=     timestep.cpu().item() if torch.is_tensor(a_			) else timestep
											__UpperCAmelCase   :					Any													=     self._index_counter[timestep_int]

									return indices[pos].item()




							@property
							def 			snake_case__					(       self							:			Any			):
									'''simple docstring'''





									if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5




							def 			snake_case__					(       self							:			Any	,	a_							:			torch.FloatTensor	,	a_							:			Union[float, torch.FloatTensor]	,	):
									'''simple docstring'''





									__UpperCAmelCase   :					Optional[Any]													=     self.index_for_timestep(a_			)

									if self.state_in_first_order:
											__UpperCAmelCase   :					Tuple													=     self.sigmas[step_index]
									else:
											__UpperCAmelCase   :					Dict													=     self.sigmas_interpol[step_index - 1]

									__UpperCAmelCase   :					Any													=     sample / ((sigma**2 + 1) ** 0.5)
									return sample




							def 			snake_case__					(       self							:			Dict	,	a_							:			int	,	a_							:			Union[str, torch.device] = None	,	a_							:			Optional[int] = None	,	):
									'''simple docstring'''





									__UpperCAmelCase   :					List[str]													=     num_inference_steps

									__UpperCAmelCase   :					str													=     num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
											__UpperCAmelCase   :					int													=     np.linspace(0	,	num_train_timesteps - 1	,	a_	,	dtype=a_			)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
											__UpperCAmelCase   :					int													=     num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											__UpperCAmelCase   :					int													=     (np.arange(0	,	a_			) * step_ratio).round()[::-1].copy().astype(a_			)
											timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
											__UpperCAmelCase   :					Optional[Any]													=     num_train_timesteps / self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											__UpperCAmelCase   :					int													=     (np.arange(a_	,	0	,	-step_ratio			)).round().copy().astype(a_			)
											timesteps -= 1
									else:
											raise ValueError(
											    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'			)

									__UpperCAmelCase   :					Optional[int]													=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
									__UpperCAmelCase   :					List[Any]													=     torch.from_numpy(np.log(a_			)			).to(a_			)

									__UpperCAmelCase   :					List[Any]													=     np.interp(a_	,	np.arange(0	,	len(a_			)			)	,	a_			)
									__UpperCAmelCase   :					Optional[Any]													=     np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
									__UpperCAmelCase   :					int													=     torch.from_numpy(a_			).to(device=a_			)

									# compute up and down sigmas
									__UpperCAmelCase   :					Any													=     sigmas.roll(-1			)
									__UpperCAmelCase   :					int													=     0.0
									__UpperCAmelCase   :					List[Any]													=     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									__UpperCAmelCase   :					List[str]													=     (sigmas_next**2 - sigmas_up**2) ** 0.5
									__UpperCAmelCase   :					Dict													=     0.0

									# compute interpolated sigmas
									__UpperCAmelCase   :					Tuple													=     sigmas.log().lerp(sigmas_down.log()	,	0.5			).exp()
									__UpperCAmelCase   :					Any													=     0.0

									# set sigmas
									__UpperCAmelCase   :					List[str]													=     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
									__UpperCAmelCase   :					str													=     torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)
									__UpperCAmelCase   :					Optional[int]													=     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2			), sigmas_up[-1:]]			)
									__UpperCAmelCase   :					Tuple													=     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2			), sigmas_down[-1:]]			)

									if str(a_			).startswith('''mps'''			):
											# mps does not support float64
											__UpperCAmelCase   :					str													=     torch.from_numpy(a_			).to(a_	,	dtype=torch.floataa			)
									else:
											__UpperCAmelCase   :					Any													=     torch.from_numpy(a_			).to(a_			)

									__UpperCAmelCase   :					Tuple													=     self.sigma_to_t(a_			).to(a_	,	dtype=timesteps.dtype			)
									__UpperCAmelCase   :					Union[str, Any]													=     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])	,	dim=-1			).flatten()

									__UpperCAmelCase   :					str													=     torch.cat([timesteps[:1], interleaved_timesteps]			)

									__UpperCAmelCase   :					Tuple													=     None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									__UpperCAmelCase   :					Optional[Any]													=     defaultdict(a_			)




							def 			snake_case__					(       self							:			str	,	a_							:			Union[str, Any]			):
									'''simple docstring'''





									__UpperCAmelCase   :					Union[str, Any]													=     sigma.log()

									# get distribution
									__UpperCAmelCase   :					Optional[int]													=     log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									__UpperCAmelCase   :					Any													=     dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
									__UpperCAmelCase   :					Optional[Any]													=     low_idx + 1

									__UpperCAmelCase   :					Union[str, Any]													=     self.log_sigmas[low_idx]
									__UpperCAmelCase   :					Optional[int]													=     self.log_sigmas[high_idx]

									# interpolate sigmas
									__UpperCAmelCase   :					Optional[int]													=     (low - log_sigma) / (low - high)
									__UpperCAmelCase   :					Any													=     w.clamp(0	,	1			)

									# transform interpolation to time range
									__UpperCAmelCase   :					Union[str, Any]													=     (1 - w) * low_idx + w * high_idx
									__UpperCAmelCase   :					str													=     t.view(sigma.shape			)
									return t




							@property
							def 			snake_case__					(       self							:			List[str]			):
									'''simple docstring'''





									return self.sample is None




							def 			snake_case__					(       self							:			Optional[Any]	,	a_							:			Union[torch.FloatTensor, np.ndarray]	,	a_							:			Union[float, torch.FloatTensor]	,	a_							:			Union[torch.FloatTensor, np.ndarray]	,	a_							:			Optional[torch.Generator] = None	,	a_							:			bool = True	,	):
									'''simple docstring'''





									__UpperCAmelCase   :					Optional[Any]													=     self.index_for_timestep(a_			)

									# advance index counter by 1
									__UpperCAmelCase   :					str													=     timestep.cpu().item() if torch.is_tensor(a_			) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
											__UpperCAmelCase   :					Tuple													=     self.sigmas[step_index]
											__UpperCAmelCase   :					str													=     self.sigmas_interpol[step_index]
											__UpperCAmelCase   :					Optional[int]													=     self.sigmas_up[step_index]
											__UpperCAmelCase   :					int													=     self.sigmas_down[step_index - 1]
									else:
											# 2nd order / KPDM2's method
											__UpperCAmelCase   :					Tuple													=     self.sigmas[step_index - 1]
											__UpperCAmelCase   :					Optional[int]													=     self.sigmas_interpol[step_index - 1]
											__UpperCAmelCase   :					List[str]													=     self.sigmas_up[step_index - 1]
											__UpperCAmelCase   :					Union[str, Any]													=     self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									__UpperCAmelCase   :					Optional[int]													=     0
									__UpperCAmelCase   :					Optional[Any]													=     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									__UpperCAmelCase   :					Union[str, Any]													=     model_output.device
									__UpperCAmelCase   :					str													=     randn_tensor(model_output.shape	,	dtype=model_output.dtype	,	device=a_	,	generator=a_			)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
											__UpperCAmelCase   :					Optional[int]													=     sigma_hat if self.state_in_first_order else sigma_interpol
											__UpperCAmelCase   :					str													=     sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
											__UpperCAmelCase   :					Optional[Any]													=     sigma_hat if self.state_in_first_order else sigma_interpol
											__UpperCAmelCase   :					Optional[Any]													=     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
											    sample / (sigma_input**2 + 1)
											)
									elif self.config.prediction_type == "sample":
											raise NotImplementedError('''prediction_type not implemented yet: sample'''			)
									else:
											raise ValueError(
											    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'			)

									if self.state_in_first_order:
											# 2. Convert to an ODE derivative for 1st order
											__UpperCAmelCase   :					Union[str, Any]													=     (sample - pred_original_sample) / sigma_hat
											# 3. delta timestep
											__UpperCAmelCase   :					List[Any]													=     sigma_interpol - sigma_hat

											# store for 2nd order step
											__UpperCAmelCase   :					Optional[Any]													=     sample
											__UpperCAmelCase   :					str													=     dt
											__UpperCAmelCase   :					Union[str, Any]													=     sample + derivative * dt
									else:
											# DPM-Solver-2
											# 2. Convert to an ODE derivative for 2nd order
											__UpperCAmelCase   :					int													=     (sample - pred_original_sample) / sigma_interpol
											# 3. delta timestep
											__UpperCAmelCase   :					Dict													=     sigma_down - sigma_hat

											__UpperCAmelCase   :					Any													=     self.sample
											__UpperCAmelCase   :					str													=     None

											__UpperCAmelCase   :					Any													=     sample + derivative * dt
											__UpperCAmelCase   :					Optional[int]													=     prev_sample + noise * sigma_up

									if not return_dict:
											return (prev_sample,)

									return SchedulerOutput(prev_sample=a_			)




							def 			snake_case__					(       self							:			str	,	a_							:			torch.FloatTensor	,	a_							:			torch.FloatTensor	,	a_							:			torch.FloatTensor	,	):
									'''simple docstring'''





									__UpperCAmelCase   :					List[str]													=     self.sigmas.to(device=original_samples.device	,	dtype=original_samples.dtype			)
									if original_samples.device.type == "mps" and torch.is_floating_point(a_			):
											# mps does not support float64
											__UpperCAmelCase   :					str													=     self.timesteps.to(original_samples.device	,	dtype=torch.floataa			)
											__UpperCAmelCase   :					Optional[int]													=     timesteps.to(original_samples.device	,	dtype=torch.floataa			)
									else:
											__UpperCAmelCase   :					List[str]													=     self.timesteps.to(original_samples.device			)
											__UpperCAmelCase   :					str													=     timesteps.to(original_samples.device			)

									__UpperCAmelCase   :					str													=     [self.index_for_timestep(a_	,	a_			) for t in timesteps]

									__UpperCAmelCase   :					Union[str, Any]													=     sigmas[step_indices].flatten()
									while len(sigma.shape			) < len(original_samples.shape			):
											__UpperCAmelCase   :					Dict													=     sigma.unsqueeze(-1			)

									__UpperCAmelCase   :					Union[str, Any]													=     original_samples + noise * sigma
									return noisy_samples




							def __len__(       self							:			Tuple			):
									'''simple docstring'''





									return self.config.num_train_timesteps










import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class 		UpperCAmelCase__      :




			'''simple docstring'''






			UpperCamelCase      						=	42

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     get_tests_dir('''fixtures/test_sentencepiece.model'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''			)

					__UpperCAmelCase   :					List[str]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Optional[int]													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	0			)

					__UpperCAmelCase   :					Optional[int]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Dict													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	1			)

					self.assertIn(
					    '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
					    ''' which is not implemented in the fast tokenizers.'''	,	str(w[0].message			)	,	)





