






import math
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 SchedulerMixin, SchedulerOutput







class 						_UpperCAmelCase	(	lowerCAmelCase__					,				lowerCAmelCase__		):




      """simple docstring"""



      A    =     1
      @register_to_config
      def __init__( self     ,						_lowerCAmelCase = 1_000     ,						_lowerCAmelCase = None		):



          '''simple docstring'''
          self.set_timesteps(snake_case_		)

          # standard deviation of the initial noise distribution
          lowerCAmelCase__ :Any   				=					1.0

          # For now we only support F-PNDM, i.e. the runge-kutta method
          # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
          # mainly at formula (9), (12), (13) and the Algorithm 2.
          lowerCAmelCase__ :Union[str, Any]   				=					4

          # running values
          lowerCAmelCase__ :Dict   				=					[]
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase = None		):



          '''simple docstring'''
          lowerCAmelCase__ :List[Any]   				=					num_inference_steps
          lowerCAmelCase__ :Union[str, Any]   				=					torch.linspace(1     ,						0     ,						num_inference_steps + 1		)[:-1]
          lowerCAmelCase__ :str   				=					torch.cat([steps, torch.tensor([0.0]		)]		)

          if self.config.trained_betas is not None:
              lowerCAmelCase__ :Optional[int]   				=					torch.tensor(self.config.trained_betas     ,						dtype=torch.floataa		)
          else:
              lowerCAmelCase__ :List[Any]   				=					torch.sin(steps * math.pi / 2		) ** 2

          lowerCAmelCase__ :Union[str, Any]   				=					(1.0 - self.betas**2) ** 0.5

          lowerCAmelCase__ :str   				=					(torch.atana(self.betas     ,						self.alphas		) / math.pi * 2)[:-1]
          lowerCAmelCase__ :Union[str, Any]   				=					timesteps.to(snake_case_		)

          lowerCAmelCase__ :Tuple   				=					[]
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase = True     ,						):



          '''simple docstring'''

          if self.num_inference_steps is None:
              raise ValueError(
                  "Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler"		)

          lowerCAmelCase__ :str   				=					(self.timesteps == timestep).nonzero().item()
          lowerCAmelCase__ :Dict   				=					timestep_index + 1

          lowerCAmelCase__ :Optional[int]   				=					sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
          self.ets.append(snake_case_		)

          if len(self.ets		) == 1:
              lowerCAmelCase__ :Optional[Any]   				=					self.ets[-1]
          elif len(self.ets		) == 2:
              lowerCAmelCase__ :Union[str, Any]   				=					(3 * self.ets[-1] - self.ets[-2]) / 2
          elif len(self.ets		) == 3:
              lowerCAmelCase__ :List[str]   				=					(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
          else:
              lowerCAmelCase__ :List[str]   				=					(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

          lowerCAmelCase__ :int   				=					self._get_prev_sample(snake_case_     ,						snake_case_     ,						snake_case_     ,						snake_case_		)

          if not return_dict:
              return (prev_sample,)

          return SchedulerOutput(prev_sample=snake_case_		)
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						*_lowerCAmelCase     ,						**_lowerCAmelCase		):



          '''simple docstring'''
          return sample
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__ :Optional[Any]   				=					self.alphas[timestep_index]
          lowerCAmelCase__ :int   				=					self.betas[timestep_index]

          lowerCAmelCase__ :List[str]   				=					self.alphas[prev_timestep_index]
          lowerCAmelCase__ :List[Any]   				=					self.betas[prev_timestep_index]

          lowerCAmelCase__ :Union[str, Any]   				=					(sample - sigma * ets) / max(snake_case_     ,						1e-8		)
          lowerCAmelCase__ :Optional[int]   				=					next_alpha * pred + ets * next_sigma

          return prev_sample



      def __len__( self		):



          '''simple docstring'''
          return self.config.num_train_timesteps







from typing import TYPE_CHECKING

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


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


