




"""simple docstring"""


import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class    A_     (					_lowerCAmelCase       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       42
							SCREAMING_SNAKE_CASE_     				=       None






def        lowerCAmelCase_       (		snake_case_		:      List[str]							,   snake_case_		:      Tuple=0.999							,   snake_case_		:      Any="cosine"							,   )       ->List[Any]:







			if alpha_transform_type == "cosine":

						def alpha_bar_fn(snake_case_		:      int      ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

			elif alpha_transform_type == "exp":

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

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

			lowerCamelCase__     :					int		=[]
			for i in range(__UpperCamelCase      ):
						lowerCamelCase__     :					int		=i / num_diffusion_timesteps
						lowerCamelCase__     :					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    A_     (					_lowerCAmelCase				, _lowerCAmelCase       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       [e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE_     				=       1





							@register_to_config
							def __init__(  self  :Optional[int]					,							lowerCamelCase_  :int = 1_000					,							lowerCamelCase_  :float = 0.00_01					,							lowerCamelCase_  :float = 0.02					,							lowerCamelCase_  :str = "linear"					,							lowerCamelCase_  :Optional[Union[np.ndarray, List[float]]] = None					,							lowerCamelCase_  :str = "epsilon"					,							lowerCamelCase_  :str = "linear"					,							lowerCamelCase_  :Optional[bool] = False					,							lowerCamelCase_  :str = "linspace"					,							lowerCamelCase_  :int = 0					,							):





										"""simple docstring"""

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

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

										lowerCamelCase__     :					Dict		=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
										lowerCamelCase__     :					Tuple		=np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
										lowerCamelCase__     :					Union[str, Any]		=torch.from_numpy(_lowerCamelCase	)

										# setable values
										lowerCamelCase__     :					Dict		=None
										lowerCamelCase__     :					List[Any]		=np.linspace(0					,							num_train_timesteps - 1					,							_lowerCamelCase					,							dtype=_lowerCamelCase	)[::-1].copy()
										lowerCamelCase__     :					Dict		=torch.from_numpy(_lowerCamelCase	)
										lowerCamelCase__     :					Dict		=False
										lowerCamelCase__     :					Tuple		=use_karras_sigmas





							@property
							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

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

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





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :torch.FloatTensor					,							lowerCamelCase_  :Union[float, torch.FloatTensor]	):





										"""simple docstring"""

										if isinstance(_lowerCamelCase					,							torch.Tensor	):
													lowerCamelCase__     :					Optional[Any]		=timestep.to(self.timesteps.device	)
										lowerCamelCase__     :					List[Any]		=(self.timesteps == timestep).nonzero().item()
										lowerCamelCase__     :					Optional[int]		=self.sigmas[step_index]

										lowerCamelCase__     :					Optional[Any]		=sample / ((sigma**2 + 1) ** 0.5)

										lowerCamelCase__     :					Tuple		=True
										return sample





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :int					,							lowerCamelCase_  :Union[str, torch.device] = None	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
													lowerCamelCase__     :					Tuple		=np.linspace(0					,							self.config.num_train_timesteps - 1					,							_lowerCamelCase					,							dtype=_lowerCamelCase	)[
													    ::-1
													].copy()
										elif self.config.timestep_spacing == "leading":
													lowerCamelCase__     :					Any		=self.config.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
													lowerCamelCase__     :					Tuple		=(np.arange(0					,							_lowerCamelCase	) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase	)
													timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
													lowerCamelCase__     :					Any		=self.config.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
													lowerCamelCase__     :					List[str]		=(np.arange(self.config.num_train_timesteps					,							0					,							-step_ratio	)).round().copy().astype(_lowerCamelCase	)
													timesteps -= 1
										else:
													raise ValueError(
													    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""	)

										lowerCamelCase__     :					Union[str, Any]		=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
										lowerCamelCase__     :					str		=np.log(_lowerCamelCase	)

										if self.config.interpolation_type == "linear":
													lowerCamelCase__     :					Optional[Any]		=np.interp(_lowerCamelCase					,							np.arange(0					,							len(_lowerCamelCase	)	)					,							_lowerCamelCase	)
										elif self.config.interpolation_type == "log_linear":
													lowerCamelCase__     :					int		=torch.linspace(np.log(sigmas[-1]	)					,							np.log(sigmas[0]	)					,							num_inference_steps + 1	).exp()
										else:
													raise ValueError(
													    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
													    ' \'linear\' or \'log_linear\''	)

										if self.use_karras_sigmas:
													lowerCamelCase__     :					str		=self._convert_to_karras(in_sigmas=_lowerCamelCase					,							num_inference_steps=self.num_inference_steps	)
													lowerCamelCase__     :					str		=np.array([self._sigma_to_t(_lowerCamelCase					,							_lowerCamelCase	) for sigma in sigmas]	)

										lowerCamelCase__     :					List[Any]		=np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
										lowerCamelCase__     :					int		=torch.from_numpy(_lowerCamelCase	).to(device=_lowerCamelCase	)
										if str(_lowerCamelCase	).startswith('mps'	):
													# mps does not support float64
													lowerCamelCase__     :					Union[str, Any]		=torch.from_numpy(_lowerCamelCase	).to(_lowerCamelCase					,							dtype=torch.floataa	)
										else:
													lowerCamelCase__     :					List[Any]		=torch.from_numpy(_lowerCamelCase	).to(device=_lowerCamelCase	)





							def 				UpperCAmelCase__							(  self  :Tuple					,							lowerCamelCase_  :Any					,							lowerCamelCase_  :List[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=np.log(_lowerCamelCase	)

										# get distribution
										lowerCamelCase__     :					List[str]		=log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										lowerCamelCase__     :					int		=np.cumsum((dists >= 0)					,							axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
										lowerCamelCase__     :					str		=low_idx + 1

										lowerCamelCase__     :					int		=log_sigmas[low_idx]
										lowerCamelCase__     :					Optional[int]		=log_sigmas[high_idx]

										# interpolate sigmas
										lowerCamelCase__     :					List[Any]		=(low - log_sigma) / (low - high)
										lowerCamelCase__     :					int		=np.clip(_lowerCamelCase					,							0					,							1	)

										# transform interpolation to time range
										lowerCamelCase__     :					Dict		=(1 - w) * low_idx + w * high_idx
										lowerCamelCase__     :					Any		=t.reshape(sigma.shape	)
										return t





							def 				UpperCAmelCase__							(  self  :str					,							lowerCamelCase_  :torch.FloatTensor					,							lowerCamelCase_  :int	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=in_sigmas[-1].item()
										lowerCamelCase__     :					int		=in_sigmas[0].item()

										lowerCamelCase__     :					List[Any]		=7.0  # 7.0 is the value used in the paper
										lowerCamelCase__     :					List[Any]		=np.linspace(0					,							1					,							_lowerCamelCase	)
										lowerCamelCase__     :					Optional[int]		=sigma_min ** (1 / rho)
										lowerCamelCase__     :					List[Any]		=sigma_max ** (1 / rho)
										lowerCamelCase__     :					List[Any]		=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :torch.FloatTensor					,							lowerCamelCase_  :Union[float, torch.FloatTensor]					,							lowerCamelCase_  :torch.FloatTensor					,							lowerCamelCase_  :float = 0.0					,							lowerCamelCase_  :float = 0.0					,							lowerCamelCase_  :float = float('inf'	)					,							lowerCamelCase_  :float = 1.0					,							lowerCamelCase_  :Optional[torch.Generator] = None					,							lowerCamelCase_  :bool = True					,							):





										"""simple docstring"""

										if (
										    isinstance(_lowerCamelCase					,							_lowerCamelCase	)
										    or isinstance(_lowerCamelCase					,							torch.IntTensor	)
										    or isinstance(_lowerCamelCase					,							torch.LongTensor	)
										):
													raise ValueError(
													    (
													        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
													        ' `EulerDiscreteScheduler.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(_lowerCamelCase					,							torch.Tensor	):
													lowerCamelCase__     :					List[str]		=timestep.to(self.timesteps.device	)

										lowerCamelCase__     :					List[Any]		=(self.timesteps == timestep).nonzero().item()
										lowerCamelCase__     :					Dict		=self.sigmas[step_index]

										lowerCamelCase__     :					List[Any]		=min(s_churn / (len(self.sigmas	) - 1)					,							2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

										lowerCamelCase__     :					Any		=randn_tensor(
										    model_output.shape					,							dtype=model_output.dtype					,							device=model_output.device					,							generator=_lowerCamelCase	)

										lowerCamelCase__     :					Optional[Any]		=noise * s_noise
										lowerCamelCase__     :					Optional[int]		=sigma * (gamma + 1)

										if gamma > 0:
													lowerCamelCase__     :					List[Any]		=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										# NOTE: "original_sample" should not be an expected prediction_type but is left in for
										# backwards compatibility
										if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													lowerCamelCase__     :					List[Any]		=model_output
										elif self.config.prediction_type == "epsilon":
													lowerCamelCase__     :					List[str]		=sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													lowerCamelCase__     :					Dict		=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
													raise ValueError(
													    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

										# 2. Convert to an ODE derivative
										lowerCamelCase__     :					Union[str, Any]		=(sample - pred_original_sample) / sigma_hat

										lowerCamelCase__     :					List[str]		=self.sigmas[step_index + 1] - sigma_hat

										lowerCamelCase__     :					List[str]		=sample + derivative * dt

										if not return_dict:
													return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=_lowerCamelCase					,							pred_original_sample=_lowerCamelCase	)





							def 				UpperCAmelCase__							(  self  :Any					,							lowerCamelCase_  :torch.FloatTensor					,							lowerCamelCase_  :torch.FloatTensor					,							lowerCamelCase_  :torch.FloatTensor					,							):





										"""simple docstring"""

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

										lowerCamelCase__     :					Dict		=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										lowerCamelCase__     :					List[str]		=sigmas[step_indices].flatten()
										while len(sigma.shape	) < len(original_samples.shape	):
													lowerCamelCase__     :					List[str]		=sigma.unsqueeze(-1	)

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





							def __len__(  self  :Tuple	):





										"""simple docstring"""

										return self.config.num_train_timesteps

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase								=						{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""DebertaV2TokenizerFast"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDebertaV2ForMaskedLM""",
							    """TFDebertaV2ForQuestionAnswering""",
							    """TFDebertaV2ForSequenceClassification""",
							    """TFDebertaV2ForTokenClassification""",
							    """TFDebertaV2Model""",
							    """TFDebertaV2PreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaV2ForMaskedLM""",
							    """DebertaV2ForMultipleChoice""",
							    """DebertaV2ForQuestionAnswering""",
							    """DebertaV2ForSequenceClassification""",
							    """DebertaV2ForTokenClassification""",
							    """DebertaV2Model""",
							    """DebertaV2PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)