

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   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     1



			@register_to_config
			def __init__( self  :     Any   ,						lowerCAmelCase__  :     int = 1000   ,						lowerCAmelCase__  :     Optional[Union[np.ndarray, List[float]]] = None):
									# set `betas`, `alphas`, `timesteps`
									self.set_timesteps(lowerCAmelCase__)

									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE_: 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.
									SCREAMING_SNAKE_CASE_: int  		=       4

									# running values
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Union[str, torch.device] = None):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       num_inference_steps
									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.linspace(1   ,						0   ,						num_inference_steps + 1)[:-1]
									SCREAMING_SNAKE_CASE_: int  		=       torch.cat([steps, torch.tensor([0.0])])

									if self.config.trained_betas is not None:
															SCREAMING_SNAKE_CASE_: str  		=       torch.tensor(self.config.trained_betas   ,						dtype=torch.floataa)
									else:
															SCREAMING_SNAKE_CASE_: Dict  		=       torch.sin(steps * math.pi / 2) ** 2

									SCREAMING_SNAKE_CASE_: str  		=       (1.0 - self.betas**2) ** 0.5

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

									SCREAMING_SNAKE_CASE_: Dict  		=       []



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     torch.FloatTensor   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     torch.FloatTensor   ,						lowerCAmelCase__  :     bool = True   ,						):
									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")

									SCREAMING_SNAKE_CASE_: List[str]  		=       (self.timesteps == timestep).nonzero().item()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       timestep_index + 1

									SCREAMING_SNAKE_CASE_: Any  		=       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
									self.ets.append(lowerCAmelCase__)

									if len(self.ets) == 1:
															SCREAMING_SNAKE_CASE_: Tuple  		=       self.ets[-1]
									elif len(self.ets) == 2:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       (3 * self.ets[-1] - self.ets[-2]) / 2
									elif len(self.ets) == 3:
															SCREAMING_SNAKE_CASE_: int  		=       (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
									else:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

									SCREAMING_SNAKE_CASE_: Dict  		=       self._get_prev_sample(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     torch.FloatTensor   ,						*lowerCAmelCase__  :     Dict   ,						**lowerCAmelCase__  :     Optional[int]):
									return sample



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.alphas[timestep_index]
									SCREAMING_SNAKE_CASE_: int  		=       self.betas[timestep_index]

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.alphas[prev_timestep_index]
									SCREAMING_SNAKE_CASE_: str  		=       self.betas[prev_timestep_index]

									SCREAMING_SNAKE_CASE_: List[str]  		=       (sample - sigma * ets) / max(lowerCAmelCase__   ,						1E-8)
									SCREAMING_SNAKE_CASE_: Dict  		=       next_alpha * pred + ets * next_sigma

									return prev_sample




			def __len__( self  :     Union[str, Any]):
									return self.config.num_train_timesteps








from typing import TYPE_CHECKING

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


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

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


