



"""simple docstring"""






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class        __snake_case       (	_lowercase):
  snake_case__				:   torch.FloatTensor
  snake_case__				:   Optional[torch.FloatTensor]  			=						None






def        snake_case_  (      A_					:		List[Any],				A_					:		Union[str, Any]=0.999,				A_					:		str="cosine",				):


       '''simple docstring'''





       if alpha_transform_type == "cosine":

              def alpha_bar_fn(A_					:		Any						):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

       elif alpha_transform_type == "exp":

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

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

       _lowerCamelCase						:				List[str]    			= []
       for i in range(A_						):
              _lowerCamelCase						:				List[str]    			= i / num_diffusion_timesteps
              _lowerCamelCase						:				Any    			= (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        __snake_case       (	_lowercase  ,     _lowercase):




  @register_to_config
  def __init__( self     :						int						,					__lowerCAmelCase     :						int = 1_0_0_0						,					__lowerCAmelCase     :						str = "fixed_small_log"						,					__lowerCAmelCase     :						bool = True						,					__lowerCAmelCase     :						Optional[float] = 1.0						,					__lowerCAmelCase     :						str = "epsilon"						,					__lowerCAmelCase     :						str = "squaredcos_cap_v2"						,					):






         """simple docstring"""






         if beta_schedule != "squaredcos_cap_v2":
                raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''		)

         _lowerCamelCase						:				str    			= betas_for_alpha_bar(__lowerCAmelCase		)

         _lowerCamelCase						:				Any    			= 1.0 - self.betas
         _lowerCamelCase						:				List[str]    			= torch.cumprod(self.alphas						,					dim=0		)
         _lowerCamelCase						:				Dict    			= torch.tensor(1.0		)

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

         # setable values
         _lowerCamelCase						:				str    			= None
         _lowerCamelCase						:				int    			= torch.from_numpy(np.arange(0						,					__lowerCAmelCase		)[::-1].copy()		)

         _lowerCamelCase						:				int    			= variance_type




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					__lowerCAmelCase     :						torch.FloatTensor						,					__lowerCAmelCase     :						Optional[int] = None		):






         """simple docstring"""






         return sample




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]						,					__lowerCAmelCase     :						int						,					__lowerCAmelCase     :						Union[str, torch.device] = None		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= num_inference_steps
         _lowerCamelCase						:				Any    			= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         _lowerCamelCase						:				Union[str, Any]    			= (np.arange(0						,					__lowerCAmelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
         _lowerCamelCase						:				str    			= torch.from_numpy(__lowerCAmelCase		).to(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						Optional[int]						,					__lowerCAmelCase     :						Any=None						,					__lowerCAmelCase     :						List[str]=None						,					__lowerCAmelCase     :						int=None		):






         """simple docstring"""






         if prev_timestep is None:
                _lowerCamelCase						:				int    			= t - 1

         _lowerCamelCase						:				int    			= self.alphas_cumprod[t]
         _lowerCamelCase						:				List[str]    			= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         _lowerCamelCase						:				Optional[int]    			= 1 - alpha_prod_t
         _lowerCamelCase						:				Optional[int]    			= 1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
                _lowerCamelCase						:				Optional[int]    			= self.betas[t]
         else:
                _lowerCamelCase						:				Union[str, Any]    			= 1 - alpha_prod_t / alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         _lowerCamelCase						:				Dict    			= beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
                _lowerCamelCase						:				List[Any]    			= self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
                _lowerCamelCase						:				Tuple    			= torch.log(torch.clamp(__lowerCAmelCase						,					min=1E-20		)		)
                _lowerCamelCase						:				Optional[Any]    			= torch.exp(0.5 * variance		)
         elif variance_type == "learned_range":
                # NOTE difference with DDPM scheduler
                _lowerCamelCase						:				Dict    			= variance.log()
                _lowerCamelCase						:				Optional[int]    			= beta.log()

                _lowerCamelCase						:				Dict    			= (predicted_variance + 1) / 2
                _lowerCamelCase						:				str    			= frac * max_log + (1 - frac) * min_log

         return variance




  def 						SCREAMING_SNAKE_CASE				( self     :						Any						,					__lowerCAmelCase     :						torch.FloatTensor						,					__lowerCAmelCase     :						int						,					__lowerCAmelCase     :						torch.FloatTensor						,					__lowerCAmelCase     :						Optional[int] = None						,					__lowerCAmelCase     :						Optional[int]=None						,					__lowerCAmelCase     :						bool = True						,					):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                _lowerCamelCase			,    _lowerCamelCase						:				int    			= torch.split(__lowerCAmelCase						,					sample.shape[1]						,					dim=1		)
         else:
                _lowerCamelCase						:				Any    			= None

         # 1. compute alphas, betas
         if prev_timestep is None:
                _lowerCamelCase						:				List[str]    			= t - 1

         _lowerCamelCase						:				Any    			= self.alphas_cumprod[t]
         _lowerCamelCase						:				Dict    			= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         _lowerCamelCase						:				List[str]    			= 1 - alpha_prod_t
         _lowerCamelCase						:				List[str]    			= 1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
                _lowerCamelCase						:				Optional[Any]    			= self.betas[t]
                _lowerCamelCase						:				List[Any]    			= self.alphas[t]
         else:
                _lowerCamelCase						:				Tuple    			= 1 - alpha_prod_t / alpha_prod_t_prev
                _lowerCamelCase						:				Any    			= 1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
                _lowerCamelCase						:				Optional[int]    			= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
                _lowerCamelCase						:				Dict    			= model_output
         else:
                raise ValueError(
                    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                    ''' for the UnCLIPScheduler.'''		)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
                _lowerCamelCase						:				Dict    			= torch.clamp(
                    __lowerCAmelCase						,					-self.config.clip_sample_range						,					self.config.clip_sample_range		)

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         _lowerCamelCase						:				Dict    			= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         _lowerCamelCase						:				int    			= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         _lowerCamelCase						:				Union[str, Any]    			= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         _lowerCamelCase						:				Optional[Any]    			= 0
         if t > 0:
                _lowerCamelCase						:				Dict    			= randn_tensor(
                    model_output.shape						,					dtype=model_output.dtype						,					generator=__lowerCAmelCase						,					device=model_output.device		)

                _lowerCamelCase						:				Any    			= self._get_variance(
                    __lowerCAmelCase						,					predicted_variance=__lowerCAmelCase						,					prev_timestep=__lowerCAmelCase						,					)

                if self.variance_type == "fixed_small_log":
                       _lowerCamelCase						:				Optional[int]    			= variance
                elif self.variance_type == "learned_range":
                       _lowerCamelCase						:				Optional[int]    			= (0.5 * variance).exp()
                else:
                       raise ValueError(
                           f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                           ''' for the UnCLIPScheduler.'''		)

                _lowerCamelCase						:				Optional[int]    			= variance * variance_noise

         _lowerCamelCase						:				int    			= pred_prev_sample + variance

         if not return_dict:
                return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase						,					pred_original_sample=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any						,					__lowerCAmelCase     :						torch.FloatTensor						,					__lowerCAmelCase     :						torch.FloatTensor						,					__lowerCAmelCase     :						torch.IntTensor						,					):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= self.alphas_cumprod.to(device=original_samples.device						,					dtype=original_samples.dtype		)
         _lowerCamelCase						:				Optional[Any]    			= timesteps.to(original_samples.device		)

         _lowerCamelCase						:				str    			= alphas_cumprod[timesteps] ** 0.5
         _lowerCamelCase						:				int    			= sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape		) < len(original_samples.shape		):
                _lowerCamelCase						:				List[str]    			= sqrt_alpha_prod.unsqueeze(-1		)

         _lowerCamelCase						:				Optional[int]    			= (1 - alphas_cumprod[timesteps]) ** 0.5
         _lowerCamelCase						:				str    			= sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape		) < len(original_samples.shape		):
                _lowerCamelCase						:				List[Any]    			= sqrt_one_minus_alpha_prod.unsqueeze(-1		)

         _lowerCamelCase						:				Tuple    			= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples









from typing import TYPE_CHECKING

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


UpperCAmelCase						:   Any			  =     {
    '''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Any			  =     [
				    '''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''FalconForCausalLM''',
				    '''FalconModel''',
				    '''FalconPreTrainedModel''',
				    '''FalconForSequenceClassification''',
				    '''FalconForTokenClassification''',
				    '''FalconForQuestionAnswering''',
				]


if TYPE_CHECKING:
				from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_falcon import (
								    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FalconForCausalLM,
								    FalconForQuestionAnswering,
								    FalconForSequenceClassification,
								    FalconForTokenClassification,
								    FalconModel,
								    FalconPreTrainedModel,
								)


else:
				import sys

				UpperCAmelCase						:   List[str]			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




