


"""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


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



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






		'''simple docstring'''

		lowerCamelCase__           =42
		lowerCamelCase__           =None






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: List[str]=0.999	,  _snake_case		: Union[str, Any]="cosine"	,  )	->Union[str, Any]:
	"""simple docstring"""





	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_snake_case		: Tuple	):
			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}"""	)

	__snake_case      :		List[str]         =							[]
	for i in range(_snake_case	):
		__snake_case      :		List[str]         =							i / num_diffusion_timesteps
		__snake_case      :		Optional[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      _UpperCAmelCase					(				__snake_case,							__snake_case							):






		'''simple docstring'''

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



		@register_to_config
		def __init__(self							,			a_ = 10_00							,			a_ = 0.0001							,			a_ = 0.02							,			a_ = "linear"							,			a_ = None							,			a_ = "epsilon"							,			a_ = "linear"							,			a_ = False							,			a_ = "linspace"							,			a_ = 0							,			):





			'''simple docstring'''

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

			__snake_case      :		Tuple         =							1.0 - self.betas
			__snake_case      :		Optional[int]         =							torch.cumprod(self.alphas							,			dim=0   )

			__snake_case      :		str         =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
			__snake_case      :		Dict         =							np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
			__snake_case      :		List[str]         =							torch.from_numpy(a_   )

			# setable values
			__snake_case      :		List[Any]         =							None
			__snake_case      :		str         =							np.linspace(0							,			num_train_timesteps - 1							,			a_							,			dtype=a_   )[::-1].copy()
			__snake_case      :		int         =							torch.from_numpy(a_   )
			__snake_case      :		int         =							False
			__snake_case      :		Union[str, Any]         =							use_karras_sigmas



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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

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



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''simple docstring'''

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

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

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



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_ = None   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							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":
				__snake_case      :		Tuple         =							np.linspace(0							,			self.config.num_train_timesteps - 1							,			a_							,			dtype=a_   )[
				    ::-1
				].copy()
			elif self.config.timestep_spacing == "leading":
				__snake_case      :		List[str]         =							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
				__snake_case      :		Dict         =							(np.arange(0							,			a_   ) * step_ratio).round()[::-1].copy().astype(a_   )
				timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
				__snake_case      :		Optional[int]         =							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
				__snake_case      :		Union[str, Any]         =							(np.arange(self.config.num_train_timesteps							,			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'."""   )

			__snake_case      :		Dict         =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
			__snake_case      :		Tuple         =							np.log(a_   )

			if self.config.interpolation_type == "linear":
				__snake_case      :		Union[str, Any]         =							np.interp(a_							,			np.arange(0							,			len(a_   )   )							,			a_   )
			elif self.config.interpolation_type == "log_linear":
				__snake_case      :		List[str]         =							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:
				__snake_case      :		str         =							self._convert_to_karras(in_sigmas=a_							,			num_inference_steps=self.num_inference_steps   )
				__snake_case      :		Dict         =							np.array([self._sigma_to_t(a_							,			a_   ) for sigma in sigmas]   )

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



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''simple docstring'''

			__snake_case      :		int         =							np.log(a_   )

			# get distribution
			__snake_case      :		Tuple         =							log_sigma - log_sigmas[:, np.newaxis]

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

			__snake_case      :		Union[str, Any]         =							log_sigmas[low_idx]
			__snake_case      :		int         =							log_sigmas[high_idx]

			# interpolate sigmas
			__snake_case      :		int         =							(low - log_sigma) / (low - high)
			__snake_case      :		Optional[int]         =							np.clip(a_							,			0							,			1   )

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



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_   ):





			'''simple docstring'''

			__snake_case      :		float         =							in_sigmas[-1].item()
			__snake_case      :		float         =							in_sigmas[0].item()

			__snake_case      :		Dict         =							7.0  # 7.0 is the value used in the paper
			__snake_case      :		int         =							np.linspace(0							,			1							,			a_   )
			__snake_case      :		int         =							sigma_min ** (1 / rho)
			__snake_case      :		Dict         =							sigma_max ** (1 / rho)
			__snake_case      :		Any         =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
			return sigmas



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_							,			a_ = 0.0							,			a_ = 0.0							,			a_ = float('''inf'''   )							,			a_ = 1.0							,			a_ = None							,			a_ = True							,			):





			'''simple docstring'''

			if (
			    isinstance(a_							,			a_   )
			    or isinstance(a_							,			torch.IntTensor   )
			    or isinstance(a_							,			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(a_							,			torch.Tensor   ):
				__snake_case      :		Tuple         =							timestep.to(self.timesteps.device   )

			__snake_case      :		List[str]         =							(self.timesteps == timestep).nonzero().item()
			__snake_case      :		Optional[Any]         =							self.sigmas[step_index]

			__snake_case      :		str         =							min(s_churn / (len(self.sigmas   ) - 1)							,			2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

			__snake_case      :		Optional[int]         =							randn_tensor(
			    model_output.shape							,			dtype=model_output.dtype							,			device=model_output.device							,			generator=a_   )

			__snake_case      :		Union[str, Any]         =							noise * s_noise
			__snake_case      :		str         =							sigma * (gamma + 1)

			if gamma > 0:
				__snake_case      :		Union[str, 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":
				__snake_case      :		List[Any]         =							model_output
			elif self.config.prediction_type == "epsilon":
				__snake_case      :		str         =							sample - sigma_hat * model_output
			elif self.config.prediction_type == "v_prediction":
				# * c_out + input * c_skip
				__snake_case      :		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
			__snake_case      :		Union[str, Any]         =							(sample - pred_original_sample) / sigma_hat

			__snake_case      :		Optional[Any]         =							self.sigmas[step_index + 1] - sigma_hat

			__snake_case      :		Union[str, Any]         =							sample + derivative * dt

			if not return_dict:
				return (prev_sample,)

			return EulerDiscreteSchedulerOutput(prev_sample=a_							,			pred_original_sample=a_   )



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_							,			):





			'''simple docstring'''

			__snake_case      :		Any         =							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
				__snake_case      :		Union[str, Any]         =							self.timesteps.to(original_samples.device							,			dtype=torch.floataa   )
				__snake_case      :		Any         =							timesteps.to(original_samples.device							,			dtype=torch.floataa   )
			else:
				__snake_case      :		List[Any]         =							self.timesteps.to(original_samples.device   )
				__snake_case      :		List[Any]         =							timesteps.to(original_samples.device   )

			__snake_case      :		List[Any]         =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

			__snake_case      :		Optional[Any]         =							original_samples + noise * sigma
			return noisy_samples



		def __len__(self   ):





			'''simple docstring'''

			return self.config.num_train_timesteps









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE					:       Tuple							   =							{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							["""RealmTokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE					:       Dict							   =							[
			    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RealmEmbedder""",
			    """RealmForOpenQA""",
			    """RealmKnowledgeAugEncoder""",
			    """RealmPreTrainedModel""",
			    """RealmReader""",
			    """RealmScorer""",
			    """load_tf_weights_in_realm""",
			]
			SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							["""RealmRetriever"""]


if TYPE_CHECKING:
			from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
			from .tokenization_realm import RealmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_realm import RealmTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_realm import (
						    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RealmEmbedder,
						    RealmForOpenQA,
						    RealmKnowledgeAugEncoder,
						    RealmPreTrainedModel,
						    RealmReader,
						    RealmScorer,
						    load_tf_weights_in_realm,
						)
						from .retrieval_realm import RealmRetriever


else:
			import sys

			SCREAMING_SNAKE_CASE					:       str							   =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





