
"""simple docstring"""






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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def __A						(   a_ :							Tuple		,		a_ :							str=0.999		,		a_ :							List[Any]="cosine"		,		)->							Optional[Any]:




		'''simple docstring'''






		if alpha_transform_type == "cosine":

				def alpha_bar_fn(a_ :							Union[str, Any]     ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(a_ :							Optional[Any]     ):
						return math.exp(t * -12.0     )

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

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




class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase   ):
	'''simple docstring'''

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





	@register_to_config
	def __init__(						self    :Dict		, lowerCamelCase_    :int = 10_00		, lowerCamelCase_    :float = 0.0_0_0_8_5		, lowerCamelCase_    :float = 0.0_1_2		, lowerCamelCase_    :str = "linear"		, lowerCamelCase_    :Optional[Union[np.ndarray, List[float]]] = None		, lowerCamelCase_    :str = "epsilon"		, lowerCamelCase_    :Optional[bool] = False		, lowerCamelCase_    :Optional[bool] = False		, lowerCamelCase_    :float = 1.0		, lowerCamelCase_    :str = "linspace"		, lowerCamelCase_    :int = 0		, )							->			Tuple:







			'''simple docstring'''




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

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

			#  set all values
			self.set_timesteps(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     int				= use_karras_sigmas





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :List[str]=None					)							->			Dict:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     str				= (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:
					SCREAMING_SNAKE_CASE       :     Dict				= 1 if len(lowerCamelCase_					) > 1 else 0
			else:
					SCREAMING_SNAKE_CASE       :     Dict				= timestep.cpu().item() if torch.is_tensor(lowerCamelCase_					) else timestep
					SCREAMING_SNAKE_CASE       :     Tuple				= self._index_counter[timestep_int]

			return indices[pos].item()





	@property
	def   __lowerCAmelCase (						self    :Dict					)							->			str:







			'''simple docstring'''




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

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





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[float, torch.FloatTensor]		, )							->			torch.FloatTensor:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.index_for_timestep(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= self.sigmas[step_index]
			SCREAMING_SNAKE_CASE       :     List[str]				= sample / ((sigma**2 + 1) ** 0.5)
			return sample





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, torch.device] = None		, lowerCamelCase_    :Optional[int] = None		, )							->			str:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_inference_steps

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

			SCREAMING_SNAKE_CASE       :     Tuple				= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
			SCREAMING_SNAKE_CASE       :     int				= np.log(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= np.interp(lowerCamelCase_		, np.arange(0		, len(lowerCamelCase_					)					)		, lowerCamelCase_					)

			if self.config.use_karras_sigmas:
					SCREAMING_SNAKE_CASE       :     int				= self._convert_to_karras(in_sigmas=lowerCamelCase_		, num_inference_steps=self.num_inference_steps					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= np.array([self._sigma_to_t(lowerCamelCase_		, lowerCamelCase_					) for sigma in sigmas]					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
			SCREAMING_SNAKE_CASE       :     int				= torch.from_numpy(lowerCamelCase_					).to(device=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

			SCREAMING_SNAKE_CASE       :     List[str]				= torch.from_numpy(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

			if str(lowerCamelCase_					).startswith('''mps'''					):
					# mps does not support float64
					SCREAMING_SNAKE_CASE       :     Tuple				= timesteps.to(lowerCamelCase_		, dtype=torch.floataa					)
			else:
					SCREAMING_SNAKE_CASE       :     List[Any]				= timesteps.to(device=lowerCamelCase_					)

			# empty dt and derivative
			SCREAMING_SNAKE_CASE       :     int				= None
			SCREAMING_SNAKE_CASE       :     List[Any]				= None

			# for exp beta schedules, such as the one for `pipeline_shap_e.py`
			# we need an index counter
			SCREAMING_SNAKE_CASE       :     int				= defaultdict(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, Any]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= np.log(lowerCamelCase_					)

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

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

			SCREAMING_SNAKE_CASE       :     List[str]				= log_sigmas[low_idx]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= log_sigmas[high_idx]

			# interpolate sigmas
			SCREAMING_SNAKE_CASE       :     Tuple				= (low - log_sigma) / (low - high)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= np.clip(lowerCamelCase_		, 0		, 1					)

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





	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :List[Any]					)							->			torch.FloatTensor:







			'''simple docstring'''




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

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





	@property
	def   __lowerCAmelCase (						self    :str					)							->			Optional[int]:







			'''simple docstring'''




			return self.dt is None





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Union[torch.FloatTensor, np.ndarray]		, lowerCamelCase_    :Union[float, torch.FloatTensor]		, lowerCamelCase_    :Union[torch.FloatTensor, np.ndarray]		, lowerCamelCase_    :bool = True		, )							->			Union[SchedulerOutput, Tuple]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.index_for_timestep(lowerCamelCase_					)

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

			if self.state_in_first_order:
					SCREAMING_SNAKE_CASE       :     str				= self.sigmas[step_index]
					SCREAMING_SNAKE_CASE       :     str				= self.sigmas[step_index + 1]
			else:
					# 2nd order / Heun's method
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.sigmas[step_index - 1]
					SCREAMING_SNAKE_CASE       :     List[Any]				= self.sigmas[step_index]

			# 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
			SCREAMING_SNAKE_CASE       :     int				= 0
			SCREAMING_SNAKE_CASE       :     List[str]				= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			if self.config.prediction_type == "epsilon":
					SCREAMING_SNAKE_CASE       :     Optional[int]				= sigma_hat if self.state_in_first_order else sigma_next
					SCREAMING_SNAKE_CASE       :     Tuple				= sample - sigma_input * model_output
			elif self.config.prediction_type == "v_prediction":
					SCREAMING_SNAKE_CASE       :     Dict				= sigma_hat if self.state_in_first_order else sigma_next
					SCREAMING_SNAKE_CASE       :     Tuple				= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
					    sample / (sigma_input**2 + 1)
					)
			elif self.config.prediction_type == "sample":
					SCREAMING_SNAKE_CASE       :     Tuple				= model_output
			else:
					raise ValueError(
					    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

			if self.config.clip_sample:
					SCREAMING_SNAKE_CASE       :     int				= pred_original_sample.clamp(
					    -self.config.clip_sample_range		, self.config.clip_sample_range					)

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

					# store for 2nd order step
					SCREAMING_SNAKE_CASE       :     Any				= derivative
					SCREAMING_SNAKE_CASE       :     List[str]				= dt
					SCREAMING_SNAKE_CASE       :     Optional[int]				= sample
			else:
					# 2. 2nd order / Heun's method
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= (sample - pred_original_sample) / sigma_next
					SCREAMING_SNAKE_CASE       :     List[str]				= (self.prev_derivative + derivative) / 2

					# 3. take prev timestep & sample
					SCREAMING_SNAKE_CASE       :     List[str]				= self.dt
					SCREAMING_SNAKE_CASE       :     List[str]				= self.sample

					# free dt and derivative
					# Note, this puts the scheduler in "first order mode"
					SCREAMING_SNAKE_CASE       :     Optional[int]				= None
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= None
					SCREAMING_SNAKE_CASE       :     List[Any]				= None

			SCREAMING_SNAKE_CASE       :     int				= sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return SchedulerOutput(prev_sample=lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :torch.FloatTensor		, )							->			torch.FloatTensor:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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
					SCREAMING_SNAKE_CASE       :     Tuple				= self.timesteps.to(original_samples.device		, dtype=torch.floataa					)
					SCREAMING_SNAKE_CASE       :     List[str]				= timesteps.to(original_samples.device		, dtype=torch.floataa					)
			else:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.timesteps.to(original_samples.device					)
					SCREAMING_SNAKE_CASE       :     Dict				= timesteps.to(original_samples.device					)

			SCREAMING_SNAKE_CASE       :     Tuple				= [self.index_for_timestep(lowerCamelCase_		, lowerCamelCase_					) for t in timesteps]

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

			SCREAMING_SNAKE_CASE       :     Tuple				= original_samples + noise * sigma
			return noisy_samples







	def __len__(						self    :int					)							->			int:







			'''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__ :       Optional[Any]						     =			{
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Dict						     =			[
					    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FunnelBaseModel",
					    "FunnelForMaskedLM",
					    "FunnelForMultipleChoice",
					    "FunnelForPreTraining",
					    "FunnelForQuestionAnswering",
					    "FunnelForSequenceClassification",
					    "FunnelForTokenClassification",
					    "FunnelModel",
					    "FunnelPreTrainedModel",
					    "load_tf_weights_in_funnel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Tuple						     =			[
					    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFFunnelBaseModel",
					    "TFFunnelForMaskedLM",
					    "TFFunnelForMultipleChoice",
					    "TFFunnelForPreTraining",
					    "TFFunnelForQuestionAnswering",
					    "TFFunnelForSequenceClassification",
					    "TFFunnelForTokenClassification",
					    "TFFunnelModel",
					    "TFFunnelPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
					from .tokenization_funnel import FunnelTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_funnel_fast import FunnelTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_funnel import (
										    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FunnelBaseModel,
										    FunnelForMaskedLM,
										    FunnelForMultipleChoice,
										    FunnelForPreTraining,
										    FunnelForQuestionAnswering,
										    FunnelForSequenceClassification,
										    FunnelForTokenClassification,
										    FunnelModel,
										    FunnelPreTrainedModel,
										    load_tf_weights_in_funnel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_funnel import (
										    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFunnelBaseModel,
										    TFFunnelForMaskedLM,
										    TFFunnelForMultipleChoice,
										    TFFunnelForPreTraining,
										    TFFunnelForQuestionAnswering,
										    TFFunnelForSequenceClassification,
										    TFFunnelForTokenClassification,
										    TFFunnelModel,
										    TFFunnelPreTrainedModel,
										)

else:
					import sys

					lowerCamelCase__ :       Optional[int]						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





