
'''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 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=42
 lowerCamelCase												=42







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	List[str]=0.999      ,  __SCREAMING_SNAKE_CASE    :	Any="cosine"      ,  )					->							Optional[Any]:





  """simple docstring"""

  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  __A		:  Optional[int]       				=		[]
  for i in range(__SCREAMING_SNAKE_CASE			):
    __A		:  Optional[int]       				=		i / num_diffusion_timesteps
    __A		:  str       				=		(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(__SCREAMING_SNAKE_CASE			) / alpha_bar_fn(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)			)
  return torch.tensor(__SCREAMING_SNAKE_CASE      ,  dtype=torch.floataa			)







class 			__A					(			_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=1
 @register_to_config
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     int = 10_00					, lowerCamelCase      :     float = 0.0_001					, lowerCamelCase      :     float = 0.02					, lowerCamelCase      :     str = "linear"					, lowerCamelCase      :     float = 0.0					, lowerCamelCase      :     Optional[np.ndarray] = None					, lowerCamelCase      :     bool = True					, ):



   """simple docstring"""





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

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

   __A		:  Any       				=		torch.tensor(1.0							)

   # standard deviation of the initial noise distribution
   __A		:  Union[str, Any]       				=		1.0

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

   __A		:  str       				=		eta
 def 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     Optional[int] = None							):



   """simple docstring"""





   return sample
 def 		lowercase_( self      :     List[str]					, lowerCamelCase      :     int					, lowerCamelCase      :     int = 10					, lowerCamelCase      :     int = 10					, lowerCamelCase      :     Union[str, torch.device] = None					, ):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		min(self.config.num_train_timesteps					, lowerCamelCase							)
   __A		:  Any       				=		num_inference_steps

   __A		:  Tuple       				=		[]

   __A		:  List[str]       				=		{}
   for j in range(0					, num_inference_steps - jump_length					, lowerCamelCase							):
     __A		:  Dict       				=		jump_n_sample - 1

   __A		:  List[str]       				=		num_inference_steps
   while t >= 1:
     __A		:  Union[str, Any]       				=		t - 1
     timesteps.append(lowerCamelCase							)

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

   __A		:  Union[str, Any]       				=		np.array(lowerCamelCase							) * (self.config.num_train_timesteps // self.num_inference_steps)
   __A		:  Optional[int]       				=		torch.from_numpy(lowerCamelCase							).to(lowerCamelCase							)
 def 		lowercase_( self      :     List[str]					, lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  Dict       				=		t - self.config.num_train_timesteps // self.num_inference_steps

   __A		:  List[str]       				=		self.alphas_cumprod[t]
   __A		:  List[str]       				=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
   __A		:  Union[str, Any]       				=		1 - alpha_prod_t
   __A		:  Dict       				=		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]
   __A		:  int       				=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

   return variance
 def 		lowercase_( self      :     Any					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     int					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     Optional[torch.Generator] = None					, lowerCamelCase      :     bool = True					, ):



   """simple docstring"""





   __A		:  Optional[Any]       				=		timestep
   __A		:  Dict       				=		timestep - self.config.num_train_timesteps // self.num_inference_steps

   # 1. compute alphas, betas
   __A		:  str       				=		self.alphas_cumprod[t]
   __A		:  Tuple       				=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
   __A		:  Tuple       				=		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
   __A		:  Optional[Any]       				=		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

   # 3. Clip "predicted x_0"
   if self.config.clip_sample:
     __A		:  List[str]       				=		torch.clamp(lowerCamelCase					, -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
   __A		:  Tuple       				=		model_output.device
   __A		:  Optional[int]       				=		randn_tensor(model_output.shape					, generator=lowerCamelCase					, device=lowerCamelCase					, dtype=model_output.dtype							)
   __A		:  Optional[int]       				=		self.eta * self._get_variance(lowerCamelCase							) ** 0.5

   __A		:  List[Any]       				=		0
   if t > 0 and self.eta > 0:
     __A		:  List[Any]       				=		std_dev_t * noise

   # 6. compute "direction pointing to x_t" of formula (12)
   # from https://arxiv.org/pdf/2010.02502.pdf
   __A		:  Optional[Any]       				=		(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
   __A		:  List[str]       				=		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
   __A		:  Optional[Any]       				=		(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
   __A		:  Optional[Any]       				=		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=lowerCamelCase					, pred_original_sample=lowerCamelCase							)
 def 		lowercase_( self      :     int					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     Any					, lowerCamelCase      :     Union[str, Any]=None							):



   """simple docstring"""





   __A		:  Tuple       				=		self.config.num_train_timesteps // self.num_inference_steps

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

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

   return sample
 def 		lowercase_( self      :     str					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     torch.FloatTensor					, lowerCamelCase      :     torch.IntTensor					, ):



   """simple docstring"""





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






 def __len__( self      :     str							):



   """simple docstring"""





   return self.config.num_train_timesteps





'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__ :		str					=logging.get_logger(__name__)

A__ :		Optional[int]					={
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Tuple					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Union[str, Any]=10_24					, lowerCamelCase      :     List[str]=16					, lowerCamelCase      :     str=16					, lowerCamelCase      :     Union[str, Any]=4					, lowerCamelCase      :     Optional[Any]="gelu"					, lowerCamelCase      :     Union[str, Any]=1e-6					, **lowerCamelCase      :     Union[str, Any]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[str]       				=		hidden_size
   __A		:  Any       				=		image_size
   __A		:  Dict       				=		patch_size
   __A		:  Union[str, Any]       				=		image_size // patch_size
   __A		:  List[Any]       				=		mask_input_channels
   __A		:  str       				=		num_point_embeddings
   __A		:  Tuple       				=		hidden_act
   __A		:  List[str]       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     int=2_56					, lowerCamelCase      :     Tuple="relu"					, lowerCamelCase      :     Optional[Any]=20_48					, lowerCamelCase      :     int=2					, lowerCamelCase      :     Tuple=8					, lowerCamelCase      :     int=2					, lowerCamelCase      :     List[str]=3					, lowerCamelCase      :     Dict=3					, lowerCamelCase      :     Any=2_56					, lowerCamelCase      :     int=1e-6					, **lowerCamelCase      :     Tuple					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[Any]       				=		hidden_size
   __A		:  Union[str, Any]       				=		hidden_act
   __A		:  Tuple       				=		mlp_dim
   __A		:  Any       				=		num_hidden_layers
   __A		:  Dict       				=		num_attention_heads
   __A		:  Optional[Any]       				=		attention_downsample_rate
   __A		:  List[Any]       				=		num_multimask_outputs
   __A		:  Optional[Any]       				=		iou_head_depth
   __A		:  Optional[int]       				=		iou_head_hidden_dim
   __A		:  Any       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     List[str]=7_68					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Optional[Any]=12					, lowerCamelCase      :     Tuple=12					, lowerCamelCase      :     int=3					, lowerCamelCase      :     Dict=10_24					, lowerCamelCase      :     int=16					, lowerCamelCase      :     Any="gelu"					, lowerCamelCase      :     Tuple=1e-0_6					, lowerCamelCase      :     Optional[int]=0.0					, lowerCamelCase      :     List[str]=1e-1_0					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     Union[str, Any]=4.0					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Optional[Any]=True					, lowerCamelCase      :     str=14					, lowerCamelCase      :     str=[2, 5, 8, 11]					, lowerCamelCase      :     Dict=1_28					, lowerCamelCase      :     Any=None					, **lowerCamelCase      :     Optional[int]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)

   __A		:  Any       				=		hidden_size
   __A		:  Union[str, Any]       				=		output_channels
   __A		:  int       				=		num_hidden_layers
   __A		:  Any       				=		num_attention_heads
   __A		:  Tuple       				=		num_channels
   __A		:  Tuple       				=		image_size
   __A		:  Optional[Any]       				=		patch_size
   __A		:  List[str]       				=		hidden_act
   __A		:  Dict       				=		layer_norm_eps
   __A		:  Optional[int]       				=		attention_dropout
   __A		:  Dict       				=		initializer_range
   __A		:  Union[str, Any]       				=		qkv_bias
   __A		:  Optional[Any]       				=		mlp_ratio
   __A		:  Optional[int]       				=		use_abs_pos
   __A		:  int       				=		use_rel_pos
   __A		:  str       				=		window_size
   __A		:  Union[str, Any]       				=		global_attn_indexes
   __A		:  Dict       				=		num_pos_feats
   __A		:  int       				=		int(hidden_size * mlp_ratio							) if mlp_dim is None else mlp_dim







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''sam'''
 lowerCamelCase												=True
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Optional[Any]=None					, lowerCamelCase      :     List[Any]=0.02					, **lowerCamelCase      :     Any					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  str       				=		vision_config if vision_config is not None else {}
   __A		:  int       				=		prompt_encoder_config if prompt_encoder_config is not None else {}
   __A		:  Dict       				=		mask_decoder_config if mask_decoder_config is not None else {}

   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  List[str]       				=		vision_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		prompt_encoder_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		mask_decoder_config.to_dict()

   __A		:  Any       				=		SamVisionConfig(**lowerCamelCase							)
   __A		:  List[Any]       				=		SamPromptEncoderConfig(**lowerCamelCase							)
   __A		:  Union[str, Any]       				=		SamMaskDecoderConfig(**lowerCamelCase							)
   __A		:  Any       				=		initializer_range






 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  str       				=		copy.deepcopy(self.__dict__							)
   __A		:  Dict       				=		self.vision_config.to_dict()
   __A		:  Any       				=		self.prompt_encoder_config.to_dict()
   __A		:  str       				=		self.mask_decoder_config.to_dict()
   __A		:  Optional[int]       				=		self.__class__.model_type
   return output



