





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


_lowerCamelCase					:				Dict  							=					logging.get_logger(__name__)


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def __init__(					self	:     int						)      -> Any:





														"""simple docstring"""
														super().__init__()
														if version.parse(torch.__version__						) < version.parse("1.12.0"						):
																					raise ImportError(
																					    F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
																					    "PytorchGELUTanh. Please upgrade torch."						)






							def 					A_	(					self	:     Tuple, _UpperCAmelCase	:     str						)      -> int:





														"""simple docstring"""
														return nn.functional.gelu(__SCREAMING_SNAKE_CASE, approximate="tanh"						)


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def 					A_	(					self	:     int, _UpperCAmelCase	:     str						)      -> str:





														"""simple docstring"""
														return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.044715 * torch.pow(__SCREAMING_SNAKE_CASE, 3.0						))						))


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def __init__(					self	:     Optional[Any], _UpperCAmelCase	:     Union[str, Any] = False						)      -> List[str]:





														"""simple docstring"""
														super().__init__()
														if use_gelu_python:
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						self._gelu_python
														else:
																					SCREAMING_SNAKE_CASE__   :			List[Any]      =						nn.functional.gelu






							def 					A_	(					self	:     Optional[Any], _UpperCAmelCase	:     Tuple						)      -> Union[str, Any]:





														"""simple docstring"""
														return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0						)						))






							def 					A_	(					self	:     Optional[int], _UpperCAmelCase	:     Optional[Any]						)      -> str:





														"""simple docstring"""
														return self.act(__SCREAMING_SNAKE_CASE						)


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def 					A_	(					self	:     Optional[int], _UpperCAmelCase	:     List[Any]						)      -> List[str]:





														"""simple docstring"""
														return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)						))


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def 					A_	(					self	:     Union[str, Any], _UpperCAmelCase	:     List[str]						)      -> Optional[int]:





														"""simple docstring"""
														return input * torch.sigmoid(1.702 * input						)


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def __init__(					self	:     List[Any], _UpperCAmelCase	:     Tuple, _UpperCAmelCase	:     str						)      -> int:





														"""simple docstring"""

														if min > max:
																					raise ValueError(F'''min should be < max (got min: {min}, max: {max})'''						)

														super().__init__()
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						min
														SCREAMING_SNAKE_CASE__   :			List[str]      =						max






							def 					A_	(					self	:     List[str], _UpperCAmelCase	:     Optional[Any]						)      -> int:





														"""simple docstring"""
														return torch.clip(gelu(__SCREAMING_SNAKE_CASE						), self.min, self.max						)


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def __init__(					self	:     int						)      -> Optional[Any]:





														"""simple docstring"""
														super().__init__()
														SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						math.sqrt(2 / math.pi						)






							def 					A_	(					self	:     str, _UpperCAmelCase	:     List[str]						)      -> Optional[int]:





														"""simple docstring"""
														return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(__SCREAMING_SNAKE_CASE, 3						))						))


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def 					A_	(					self	:     Tuple, _UpperCAmelCase	:     Any						)      -> str:





														"""simple docstring"""
														return nn.functional.silu(__SCREAMING_SNAKE_CASE						)


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def __init__(					self	:     int						)      -> Union[str, Any]:





														"""simple docstring"""
														super().__init__()
														if version.parse(torch.__version__						) < version.parse("1.9.0"						):
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						self._mish_python
														else:
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						nn.functional.mish






							def 					A_	(					self	:     str, _UpperCAmelCase	:     Dict						)      -> List[str]:





														"""simple docstring"""
														return input * torch.tanh(nn.functional.softplus(__SCREAMING_SNAKE_CASE						)						)






							def 					A_	(					self	:     Optional[int], _UpperCAmelCase	:     Tuple						)      -> Optional[int]:





														"""simple docstring"""
														return self.act(__SCREAMING_SNAKE_CASE						)


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def 					A_	(					self	:     int, _UpperCAmelCase	:     Dict						)      -> Any:





														"""simple docstring"""
														return input


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def 					A_	(					self	:     Tuple, _UpperCAmelCase	:     Union[str, Any], _UpperCAmelCase	:     Dict=0.707107, _UpperCAmelCase	:     Union[str, Any]=0.282095						)      -> int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						(input - mu).div(sigma * math.sqrt(2.0						)						)
														return 0.5 * (1.0 + torch.erf(__SCREAMING_SNAKE_CASE						))


class 						lowerCamelCase   (nn.Module			):







							"""simple docstring"""




							def 					A_	(					self	:     int, _UpperCAmelCase	:     List[str]						)      -> Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			int      =						nn.functional.relu(__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						torch.square(__SCREAMING_SNAKE_CASE						)
														return squared


class 						lowerCamelCase   (__lowerCAmelCase			):







							"""simple docstring"""




							def __getitem__(					self	:     Any, _UpperCAmelCase	:     List[str]						)      -> Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			str      =						super().__getitem__(__SCREAMING_SNAKE_CASE						)
														SCREAMING_SNAKE_CASE__    ,SCREAMING_SNAKE_CASE__   :			Dict      =						content if isinstance(__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE						) else (content, {})
														return cls(**__SCREAMING_SNAKE_CASE						)


_lowerCamelCase					:				Dict  							=					{
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -1_0, """max""": 1_0}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
_lowerCamelCase					:				List[Any]  							=					ClassInstantier(ACTaCLS)




def _a					(  SCREAMING_SNAKE_CASE__			:  Optional[Any]   )       ->				Dict:

							'''simple docstring'''






							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()   )}'''   )


# For backwards compatibility with: from activations import gelu_python
_lowerCamelCase					:				str  							=					get_activation('''gelu_python''')
_lowerCamelCase					:				List[Any]  							=					get_activation('''gelu_new''')
_lowerCamelCase					:				Tuple  							=					get_activation('''gelu''')
_lowerCamelCase					:				List[str]  							=					get_activation('''gelu_fast''')
_lowerCamelCase					:				Optional[int]  							=					get_activation('''quick_gelu''')
_lowerCamelCase					:				List[Any]  							=					get_activation('''silu''')
_lowerCamelCase					:				Optional[Any]  							=					get_activation('''mish''')







_lowerCamelCase					:				Any  							=					get_activation('''linear''')













"""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
class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							torch.FloatTensor
       snake_case__ :							torch.FloatTensor





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=0.999		,   _UpperCAmelCase="cosine"		,   )			->     str:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_UpperCAmelCase		):
   return math.cos((t + 0.008) / 1.008 * 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}"		)

 snake_case__										= []
 for i in range(_UpperCAmelCase		):
  snake_case__										= i / num_diffusion_timesteps
  snake_case__										= (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    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):
       snake_case__ :							List[Any] 							=   1

       @register_to_config
       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 1000      ,    __SCREAMING_SNAKE_CASE = 0.0001      ,    __SCREAMING_SNAKE_CASE = 0.02      ,    __SCREAMING_SNAKE_CASE = "linear"      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "linear":
         snake_case__										= torch.linspace(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         snake_case__										= (
             torch.linspace(beta_start**0.5      ,    beta_end**0.5      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         snake_case__										= betas_for_alpha_bar(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         snake_case__										= torch.linspace(-6      ,    6      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= torch.sigmoid(__SCREAMING_SNAKE_CASE   ) * (beta_end - beta_start) + beta_start
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

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

        snake_case__										= torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        snake_case__										= 1.0

        # setable values
        snake_case__										= None
        snake_case__										= torch.from_numpy(np.arange(0      ,    __SCREAMING_SNAKE_CASE   )[::-1].copy()   )

        snake_case__										= eta

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        snake_case__										= min(self.config.num_train_timesteps      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= num_inference_steps

        snake_case__										= []

        snake_case__										= {}
        for j in range(0      ,    num_inference_steps - jump_length      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= jump_n_sample - 1

        snake_case__										= num_inference_steps
        while t >= 1:
         snake_case__										= t - 1
         timesteps.append(__SCREAMING_SNAKE_CASE   )

         if jumps.get(__SCREAMING_SNAKE_CASE      ,    0   ) > 0:
          snake_case__										= jumps[t] - 1
          for _ in range(__SCREAMING_SNAKE_CASE   ):
           snake_case__										= t + 1
           timesteps.append(__SCREAMING_SNAKE_CASE   )

        snake_case__										= np.array(__SCREAMING_SNAKE_CASE   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= t - self.config.num_train_timesteps // self.num_inference_steps

        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t
        snake_case__										= 1 - 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
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        snake_case__										= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        snake_case__										= timestep
        snake_case__										= timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        snake_case__										= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         snake_case__										= torch.clamp(__SCREAMING_SNAKE_CASE      ,    -1      ,    1   )

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        snake_case__										= model_output.device
        snake_case__										= randn_tensor(model_output.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=model_output.dtype   )
        snake_case__										= self.eta * self._get_variance(__SCREAMING_SNAKE_CASE   ) ** 0.5

        snake_case__										= 0
        if t > 0 and self.eta > 0:
         snake_case__										= std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
         return (
             pred_prev_sample,
             pred_original_sample,
         )

        return RePaintSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,    pred_original_sample=__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= self.config.num_train_timesteps // self.num_inference_steps

        for i in range(__SCREAMING_SNAKE_CASE   ):
         snake_case__										= self.betas[timestep + i]
         if sample.device.type == "mps":
          # randn does not work reproducibly on mps
          snake_case__										= randn_tensor(sample.shape      ,    dtype=sample.dtype      ,    generator=__SCREAMING_SNAKE_CASE   )
          snake_case__										= noise.to(sample.device   )
         else:
          snake_case__										= randn_tensor(sample.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=sample.device      ,    dtype=sample.dtype   )

         # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
         snake_case__										= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."   )

       def __len__(	self   ):






        '''simple docstring'''

        return self.config.num_train_timesteps

