






"""simple docstring"""



# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class 							__lowercase:
						lowerCAmelCase_		:				Dict=							42
						lowerCAmelCase_		:				List[str]=							42

						# setable values
						lowerCAmelCase_		:				int=							42
						lowerCAmelCase_		:				Optional[int]=							42
						lowerCAmelCase_		:				Any=							None






						@classmethod
						def 		lowerCAmelCase (       cls     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    )      ->	int:




											"""simple docstring"""





											return cls(
											    common=_SCREAMING_SNAKE_CASE     ,    final_alpha_cumprod=_SCREAMING_SNAKE_CASE     ,    init_noise_sigma=_SCREAMING_SNAKE_CASE     ,    timesteps=_SCREAMING_SNAKE_CASE     ,    )




@dataclass
class 							__lowercase(							lowercase__	):
						lowerCAmelCase_		:				Union[str, Any]=							42




class 							__lowercase(							lowercase__    ,				lowercase__	):
						lowerCAmelCase_		:				Union[str, Any]=							[e.name for e in FlaxKarrasDiffusionSchedulers]

						lowerCAmelCase_		:				Optional[int]=							42






						@property
						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											return True






						@register_to_config
						def __init__(       self     ,    _SCREAMING_SNAKE_CASE = 10_00     ,    _SCREAMING_SNAKE_CASE = 0.0001     ,    _SCREAMING_SNAKE_CASE = 0.02     ,    _SCREAMING_SNAKE_CASE = "linear"     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = 0     ,    _SCREAMING_SNAKE_CASE = "epsilon"     ,    _SCREAMING_SNAKE_CASE = jnp.floataa     ,    )      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=dtype






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE = None)      ->	DDIMSchedulerState:




											"""simple docstring"""





											if common is None:
																lowerCamelCase__					=CommonSchedulerState.create(self)

											# At every step in ddim, we are looking into the previous alphas_cumprod
											# For the final step, there is no previous alphas_cumprod because we are already at 0
											# `set_alpha_to_one` decides whether we set this parameter simply to one or
											# whether we use the final alpha of the "non-previous" one.
											lowerCamelCase__					=(
											    jnp.array(1.0     ,    dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
											)

											# standard deviation of the initial noise distribution
											lowerCamelCase__					=jnp.array(1.0     ,    dtype=self.dtype)

											lowerCamelCase__					=jnp.arange(0     ,    self.config.num_train_timesteps).round()[::-1]

											return DDIMSchedulerState.create(
											    common=_SCREAMING_SNAKE_CASE     ,    final_alpha_cumprod=_SCREAMING_SNAKE_CASE     ,    init_noise_sigma=_SCREAMING_SNAKE_CASE     ,    timesteps=_SCREAMING_SNAKE_CASE     ,    )






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None)      ->	jnp.ndarray:




											"""simple docstring"""





											return sample






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = ())      ->	DDIMSchedulerState:




											"""simple docstring"""





											lowerCamelCase__					=self.config.num_train_timesteps // num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# rounding to avoid issues when num_inference_step is power of 3
											lowerCamelCase__					=(jnp.arange(0     ,    _SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1] + self.config.steps_offset

											return state.replace(
											    num_inference_steps=_SCREAMING_SNAKE_CASE     ,    timesteps=_SCREAMING_SNAKE_CASE     ,    )






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	List[Any]:




											"""simple docstring"""





											lowerCamelCase__					=state.common.alphas_cumprod[timestep]
											lowerCamelCase__					=jnp.where(
											    prev_timestep >= 0     ,    state.common.alphas_cumprod[prev_timestep]     ,    state.final_alpha_cumprod)
											lowerCamelCase__					=1 - alpha_prod_t
											lowerCamelCase__					=1 - alpha_prod_t_prev

											lowerCamelCase__					=(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = 0.0     ,    _SCREAMING_SNAKE_CASE = True     ,    )      ->	Union[FlaxDDIMSchedulerOutput, Tuple]:




											"""simple docstring"""





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

											# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
											# Ideally, read DDIM paper in-detail understanding

											# Notation (<variable name> -> <name in paper>
											# - pred_noise_t -> e_theta(x_t, t)
											# - pred_original_sample -> f_theta(x_t, t) or x_0
											# - std_dev_t -> sigma_t
											# - eta -> η
											# - pred_sample_direction -> "direction pointing to x_t"
											# - pred_prev_sample -> "x_t-1"

											# 1. get previous step value (=t-1)
											lowerCamelCase__					=timestep - self.config.num_train_timesteps // state.num_inference_steps

											lowerCamelCase__					=state.common.alphas_cumprod
											lowerCamelCase__					=state.final_alpha_cumprod

											# 2. compute alphas, betas
											lowerCamelCase__					=alphas_cumprod[timestep]
											lowerCamelCase__					=jnp.where(prev_timestep >= 0     ,    alphas_cumprod[prev_timestep]     ,    _SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																lowerCamelCase__					=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																lowerCamelCase__					=model_output
											elif self.config.prediction_type == "sample":
																lowerCamelCase__					=model_output
																lowerCamelCase__					=(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																lowerCamelCase__					=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																lowerCamelCase__					=(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
																    ' `v_prediction`')

											# 4. compute variance: "sigma_t(η)" -> see formula (16)
											# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
											lowerCamelCase__					=self._get_variance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=eta * variance ** 0.5

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

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											lowerCamelCase__					=alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																return (prev_sample, state)

											return FlaxDDIMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE     ,    state=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    )      ->	jnp.ndarray:




											"""simple docstring"""





											return add_noise_common(state.common     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    )      ->	jnp.ndarray:




											"""simple docstring"""





											return get_velocity_common(state.common     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






						def __len__(       self)      ->	str:




											"""simple docstring"""





											return self.config.num_train_timesteps






import copy

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


__lowercase							:List[Any]							=	logging.get_logger(__name__)

__lowercase							:Optional[Any]							=	{
    "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  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  List[Any]				,						a					:  int=2_56				,						a					:  Tuple=10_24				,						a					:  Optional[Any]=16				,						a					:  Dict=16				,						a					:  str=4				,						a					:  Union[str, Any]="gelu"				,						a					:  Optional[Any]=1E-6				,						**a					:  Dict				,						)						->Union[str, Any]:
													super().__init__(**a     )
													SCREAMING_SNAKE_CASE__  :					Any =       hidden_size
													SCREAMING_SNAKE_CASE__  :					List[Any] =       image_size
													SCREAMING_SNAKE_CASE__  :					List[str] =       patch_size
													SCREAMING_SNAKE_CASE__  :					str =       image_size // patch_size
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       mask_input_channels
													SCREAMING_SNAKE_CASE__  :					Any =       num_point_embeddings
													SCREAMING_SNAKE_CASE__  :					str =       hidden_act
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       layer_norm_eps







class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Union[str, Any]				,						a					:  Optional[int]=2_56				,						a					:  Dict="relu"				,						a					:  Dict=20_48				,						a					:  Union[str, Any]=2				,						a					:  str=8				,						a					:  str=2				,						a					:  List[Any]=3				,						a					:  str=3				,						a					:  List[str]=2_56				,						a					:  str=1E-6				,						**a					:  Optional[int]				,						)						->Dict:
													super().__init__(**a     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       hidden_size
													SCREAMING_SNAKE_CASE__  :					List[Any] =       hidden_act
													SCREAMING_SNAKE_CASE__  :					Dict =       mlp_dim
													SCREAMING_SNAKE_CASE__  :					List[Any] =       num_hidden_layers
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       num_attention_heads
													SCREAMING_SNAKE_CASE__  :					List[Any] =       attention_downsample_rate
													SCREAMING_SNAKE_CASE__  :					List[str] =       num_multimask_outputs
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       iou_head_depth
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       iou_head_hidden_dim
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       layer_norm_eps







class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Tuple				,						a					:  str=7_68				,						a					:  Tuple=2_56				,						a					:  Tuple=12				,						a					:  List[str]=12				,						a					:  Optional[Any]=3				,						a					:  str=10_24				,						a					:  str=16				,						a					:  Union[str, Any]="gelu"				,						a					:  Optional[int]=1E-06				,						a					:  Optional[Any]=0.0				,						a					:  str=1E-10				,						a					:  Optional[int]=True				,						a					:  Dict=4.0				,						a					:  Optional[Any]=True				,						a					:  List[str]=True				,						a					:  str=14				,						a					:  List[str]=[2, 5, 8, 11]				,						a					:  int=1_28				,						a					:  int=None				,						**a					:  List[str]				,						)						->Union[str, Any]:
													super().__init__(**a     )

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







class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    "sam"
							snake_case_							=    True

							def __init__(   self					:  str				,						a					:  Optional[int]=None				,						a					:  Any=None				,						a					:  Any=None				,						a					:  str=0.02				,						**a					:  int				,						)						->Optional[int]:
													super().__init__(**a     )
													SCREAMING_SNAKE_CASE__  :					Dict =       vision_config if vision_config is not None else {}
													SCREAMING_SNAKE_CASE__  :					str =       prompt_encoder_config if prompt_encoder_config is not None else {}
													SCREAMING_SNAKE_CASE__  :					Tuple =       mask_decoder_config if mask_decoder_config is not None else {}

													if isinstance(a				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					Tuple =       vision_config.to_dict()
													if isinstance(a				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					Dict =       prompt_encoder_config.to_dict()
													if isinstance(a				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       mask_decoder_config.to_dict()

													SCREAMING_SNAKE_CASE__  :					str =       SamVisionConfig(**a     )
													SCREAMING_SNAKE_CASE__  :					str =       SamPromptEncoderConfig(**a     )
													SCREAMING_SNAKE_CASE__  :					int =       SamMaskDecoderConfig(**a     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       initializer_range






							def   A_	(   self					:  str     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					int =       copy.deepcopy(self.__dict__     )
													SCREAMING_SNAKE_CASE__  :					str =       self.vision_config.to_dict()
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.prompt_encoder_config.to_dict()
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.mask_decoder_config.to_dict()
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.__class__.model_type
													return output