



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

import numpy as np
import torch

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


__A             =logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class 		UpperCAmelCase__      (      A__       ):




							'''simple docstring'''






							UpperCamelCase      						=	42

class 		UpperCAmelCase__      (      A__						,A__       ):




							'''simple docstring'''






							UpperCamelCase      						=	1




							@register_to_config
							def __init__(       self							:			Union[str, Any]	,	a_							:			Any = 40	,	a_							:			int = 0.0_0_2	,	a_							:			int = 8_0.0	,	a_							:			List[str] = 0.5	,	a_							:			Any = 1.0	,	a_							:			List[str] = 7.0	,	a_							:			Dict = True	,	):
									'''simple docstring'''





									__UpperCAmelCase   :					Any													=     sigma_max

									__UpperCAmelCase   :					Tuple													=     np.linspace(0	,	1	,	snake_case_			)
									__UpperCAmelCase   :					List[Any]													=     self._convert_to_karras(snake_case_			)
									__UpperCAmelCase   :					Optional[int]													=     self.sigma_to_t(snake_case_			)

									# setable values
									__UpperCAmelCase   :					Any													=     None
									__UpperCAmelCase   :					Dict													=     torch.from_numpy(snake_case_			)
									__UpperCAmelCase   :					Optional[Any]													=     torch.from_numpy(snake_case_			)
									__UpperCAmelCase   :					int													=     False
									__UpperCAmelCase   :					Optional[int]													=     False




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





									if schedule_timesteps is None:
											__UpperCAmelCase   :					Optional[int]													=     self.timesteps

									__UpperCAmelCase   :					Dict													=     (schedule_timesteps == timestep).nonzero()
									return indices.item()




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





									if isinstance(snake_case_	,	torch.Tensor			):
											__UpperCAmelCase   :					int													=     timestep.to(self.timesteps.device			)
									__UpperCAmelCase   :					Tuple													=     self.index_for_timestep(snake_case_			)
									__UpperCAmelCase   :					Dict													=     self.sigmas[step_idx]

									__UpperCAmelCase   :					str													=     sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									__UpperCAmelCase   :					Union[str, Any]													=     True
									return sample




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





									if not isinstance(snake_case_	,	np.ndarray			):
											__UpperCAmelCase   :					Any													=     np.array(snake_case_	,	dtype=np.floataa			)

									__UpperCAmelCase   :					Optional[int]													=     10_00 * 0.2_5 * np.log(sigmas + 1e-44			)

									return timesteps




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





									if num_inference_steps is None and timesteps is None:
											raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''			)

									if num_inference_steps is not None and timesteps is not None:
											raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''			)

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
											for i in range(1	,	len(snake_case_			)			):
													if timesteps[i] >= timesteps[i - 1]:
															raise ValueError('''`timesteps` must be in descending order.'''			)

											if timesteps[0] >= self.config.num_train_timesteps:
													raise ValueError(
													    F'`timesteps` must start before `self.config.train_timesteps`:'
													    F' {self.config.num_train_timesteps}.'			)

											__UpperCAmelCase   :					int													=     np.array(snake_case_	,	dtype=np.intaa			)
											__UpperCAmelCase   :					str													=     True
									else:
											if num_inference_steps > self.config.num_train_timesteps:
													raise ValueError(
													    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
													    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
													    F' maximal {self.config.num_train_timesteps} timesteps.'			)

											__UpperCAmelCase   :					Optional[int]													=     num_inference_steps

											__UpperCAmelCase   :					int													=     self.config.num_train_timesteps // self.num_inference_steps
											__UpperCAmelCase   :					str													=     (np.arange(0	,	snake_case_			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
											__UpperCAmelCase   :					str													=     False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									__UpperCAmelCase   :					str													=     self.config.num_train_timesteps
									__UpperCAmelCase   :					List[Any]													=     timesteps[::-1].copy()
									__UpperCAmelCase   :					str													=     ramp / (num_train_timesteps - 1)
									__UpperCAmelCase   :					str													=     self._convert_to_karras(snake_case_			)
									__UpperCAmelCase   :					Dict													=     self.sigma_to_t(snake_case_			)

									__UpperCAmelCase   :					Tuple													=     np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
									__UpperCAmelCase   :					Optional[int]													=     torch.from_numpy(snake_case_			).to(device=snake_case_			)

									if str(snake_case_			).startswith('''mps'''			):
											# mps does not support float64
											__UpperCAmelCase   :					int													=     torch.from_numpy(snake_case_			).to(snake_case_	,	dtype=torch.floataa			)
									else:
											__UpperCAmelCase   :					List[Any]													=     torch.from_numpy(snake_case_			).to(device=snake_case_			)




							def 			snake_case__					(       self							:			Any	,	a_							:			Tuple			):
									'''simple docstring'''





									__UpperCAmelCase   :					List[Any]													=     self.config.sigma_min
									__UpperCAmelCase   :					Optional[Any]													=     self.config.sigma_max

									__UpperCAmelCase   :					List[str]													=     self.config.rho
									__UpperCAmelCase   :					Optional[Any]													=     sigma_min ** (1 / rho)
									__UpperCAmelCase   :					Optional[int]													=     sigma_max ** (1 / rho)
									__UpperCAmelCase   :					str													=     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas




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





									__UpperCAmelCase   :					Tuple													=     self.config.sigma_data

									__UpperCAmelCase   :					Any													=     sigma_data**2 / (sigma**2 + sigma_data**2)
									__UpperCAmelCase   :					List[str]													=     sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out




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





									__UpperCAmelCase   :					str													=     self.config.sigma_min
									__UpperCAmelCase   :					str													=     self.config.sigma_data

									__UpperCAmelCase   :					Dict													=     sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									__UpperCAmelCase   :					int													=     (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out




							def 			snake_case__					(       self							:			int	,	a_							:			List[Any]	,	a_							:			Dict	,	a_							:			Union[str, Any]	,	a_							:			Optional[int] = None	,	a_							:			int = True	,	):
									'''simple docstring'''





									if (
									    isinstance(snake_case_	,	snake_case_			)
									    or isinstance(snake_case_	,	torch.IntTensor			)
									    or isinstance(snake_case_	,	torch.LongTensor			)
									):
											raise ValueError(
											    (
											        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
											        F' `{self.__class__}.step()` is not supported. Make sure to pass'
											        ''' one of the `scheduler.timesteps` as a timestep.'''
											    )	,	)

									if not self.is_scale_input_called:
											logger.warning(
											    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
											    '''See `StableDiffusionPipeline` for a usage example.'''			)

									if isinstance(snake_case_	,	torch.Tensor			):
											__UpperCAmelCase   :					List[str]													=     timestep.to(self.timesteps.device			)

									__UpperCAmelCase   :					Tuple													=     self.config.sigma_min
									__UpperCAmelCase   :					Union[str, Any]													=     self.config.sigma_max

									__UpperCAmelCase   :					Optional[int]													=     self.index_for_timestep(snake_case_			)

									# sigma_next corresponds to next_t in original implementation
									__UpperCAmelCase   :					int													=     self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
											__UpperCAmelCase   :					Optional[Any]													=     self.sigmas[step_index + 1]
									else:
											# Set sigma_next to sigma_min
											__UpperCAmelCase   :					Union[str, Any]													=     self.sigmas[-1]

									# Get scalings for boundary conditions
									__UpperCAmelCase							, __UpperCAmelCase   :					List[Any]													=     self.get_scalings_for_boundary_condition(snake_case_			)

									# 1. Denoise model output using boundary conditions
									__UpperCAmelCase   :					Optional[int]													=     c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
											__UpperCAmelCase   :					List[str]													=     denoised.clamp(-1	,	1			)

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps			) > 1:
											__UpperCAmelCase   :					List[str]													=     randn_tensor(
											    model_output.shape	,	dtype=model_output.dtype	,	device=model_output.device	,	generator=snake_case_			)
									else:
											__UpperCAmelCase   :					List[Any]													=     torch.zeros_like(snake_case_			)
									__UpperCAmelCase   :					Any													=     noise * self.config.s_noise

									__UpperCAmelCase   :					Any													=     sigma_next.clamp(min=snake_case_	,	max=snake_case_			)

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									__UpperCAmelCase   :					List[str]													=     denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
											return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case_			)




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





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

									__UpperCAmelCase   :					Optional[Any]													=     [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

									__UpperCAmelCase   :					Optional[int]													=     original_samples + noise * sigma
									return noisy_samples




							def __len__(       self							:			Optional[int]			):
									'''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			)	,	)





