
"""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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	42
			lowerCamelCase						      =	None







def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=0.9_99					,				__UpperCamelCase="cosine"					,				)       ->				Dict:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(__UpperCamelCase ):
			return math.exp(t * -12.0 )

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

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





			'''simple docstring'''
			lowerCamelCase						      =	[e.name for e in KarrasDiffusionSchedulers]
			lowerCamelCase						      =	1



			@register_to_config
			def __init__(							self  ,  __UpperCAmelCase = 10_00  ,  __UpperCAmelCase = 0.0_0_0_1  ,  __UpperCAmelCase = 0.0_2  ,  __UpperCAmelCase = "linear"  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = "epsilon"  ,  __UpperCAmelCase = "linspace"  ,  __UpperCAmelCase = 0  ,  )				->  int:
				if trained_betas is not None:
					_lowerCAmelCase			=torch.tensor(__UpperCAmelCase  ,  dtype=torch.floataa						)
				elif beta_schedule == "linear":
					_lowerCAmelCase			=torch.linspace(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  dtype=torch.floataa						)
				elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					_lowerCAmelCase			=(
					    torch.linspace(beta_start**0.5  ,  beta_end**0.5  ,  __UpperCAmelCase  ,  dtype=torch.floataa						) ** 2
					)
				elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					_lowerCAmelCase			=betas_for_alpha_bar(__UpperCAmelCase						)
				else:
					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''						)

				_lowerCAmelCase			=1.0 - self.betas
				_lowerCAmelCase			=torch.cumprod(self.alphas  ,  dim=0						)

				_lowerCAmelCase			=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
				_lowerCAmelCase			=np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
				_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						)

				# setable values
				_lowerCAmelCase			=None
				_lowerCAmelCase			=np.linspace(0  ,  num_train_timesteps - 1  ,  __UpperCAmelCase  ,  dtype=__UpperCAmelCase						)[::-1].copy()
				_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						)
				_lowerCAmelCase			=False



			@property
			def       _lowerCAmelCase	(							self						)				->  str:
				# standard deviation of the initial noise distribution
				if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

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



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  torch.FloatTensor:
				if isinstance(__UpperCAmelCase  ,  torch.Tensor						):
					_lowerCAmelCase			=timestep.to(self.timesteps.device						)
				_lowerCAmelCase			=(self.timesteps == timestep).nonzero().item()
				_lowerCAmelCase			=self.sigmas[step_index]
				_lowerCAmelCase			=sample / ((sigma**2 + 1) ** 0.5)
				_lowerCAmelCase			=True
				return sample



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase = None						)				->  int:
				_lowerCAmelCase			=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			=np.linspace(0  ,  self.config.num_train_timesteps - 1  ,  __UpperCAmelCase  ,  dtype=__UpperCAmelCase						)[
					    ::-1
					].copy()
				elif self.config.timestep_spacing == "leading":
					_lowerCAmelCase			=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			=(np.arange(0  ,  __UpperCAmelCase						) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase						)
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					_lowerCAmelCase			=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			=(np.arange(self.config.num_train_timesteps  ,  0  ,  -step_ratio						)).round().copy().astype(__UpperCAmelCase						)
					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			=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
				_lowerCAmelCase			=np.interp(__UpperCAmelCase  ,  np.arange(0  ,  len(__UpperCAmelCase						)						)  ,  __UpperCAmelCase						)
				_lowerCAmelCase			=np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
				_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)
				if str(__UpperCAmelCase						).startswith("""mps"""						):
					# mps does not support float64
					_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(__UpperCAmelCase  ,  dtype=torch.floataa						)
				else:
					_lowerCAmelCase			=torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = True  ,  )				->  Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

				if (
				    isinstance(__UpperCAmelCase  ,  __UpperCAmelCase						)
				    or isinstance(__UpperCAmelCase  ,  torch.IntTensor						)
				    or isinstance(__UpperCAmelCase  ,  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(__UpperCAmelCase  ,  torch.Tensor						):
					_lowerCAmelCase			=timestep.to(self.timesteps.device						)

				_lowerCAmelCase			=(self.timesteps == timestep).nonzero().item()
				_lowerCAmelCase			=self.sigmas[step_index]

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					_lowerCAmelCase			=sample - sigma * model_output
				elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					_lowerCAmelCase			=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''						)

				_lowerCAmelCase			=self.sigmas[step_index]
				_lowerCAmelCase			=self.sigmas[step_index + 1]
				_lowerCAmelCase			=(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
				_lowerCAmelCase			=(sigma_to**2 - sigma_up**2) ** 0.5

				# 2. Convert to an ODE derivative
				_lowerCAmelCase			=(sample - pred_original_sample) / sigma

				_lowerCAmelCase			=sigma_down - sigma

				_lowerCAmelCase			=sample + derivative * dt

				_lowerCAmelCase			=model_output.device
				_lowerCAmelCase			=randn_tensor(model_output.shape  ,  dtype=model_output.dtype  ,  device=__UpperCAmelCase  ,  generator=__UpperCAmelCase						)

				_lowerCAmelCase			=prev_sample + noise * sigma_up

				if not return_dict:
					return (prev_sample,)

				return EulerAncestralDiscreteSchedulerOutput(
				    prev_sample=__UpperCAmelCase  ,  pred_original_sample=__UpperCAmelCase						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  )				->  torch.FloatTensor:
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				_lowerCAmelCase			=self.sigmas.to(device=original_samples.device  ,  dtype=original_samples.dtype						)
				if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase						):
					# mps does not support float64
					_lowerCAmelCase			=self.timesteps.to(original_samples.device  ,  dtype=torch.floataa						)
					_lowerCAmelCase			=timesteps.to(original_samples.device  ,  dtype=torch.floataa						)
				else:
					_lowerCAmelCase			=self.timesteps.to(original_samples.device						)
					_lowerCAmelCase			=timesteps.to(original_samples.device						)

				_lowerCAmelCase			=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

				_lowerCAmelCase			=original_samples + noise * sigma
				return noisy_samples



			def __len__(							self						)				->  Dict:
				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,
)


__A 		= {
    'configuration_led': ['LED_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LEDConfig'],
    'tokenization_led': ['LEDTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= ['LEDTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'LED_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LEDForConditionalGeneration',
			    'LEDForQuestionAnswering',
			    'LEDForSequenceClassification',
			    'LEDModel',
			    'LEDPreTrainedModel',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= ['TFLEDForConditionalGeneration', 'TFLEDModel', 'TFLEDPreTrainedModel']


if TYPE_CHECKING:
			from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
			from .tokenization_led import LEDTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_led_fast import LEDTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_led import (
						    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LEDForConditionalGeneration,
						    LEDForQuestionAnswering,
						    LEDForSequenceClassification,
						    LEDModel,
						    LEDPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



