


'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class  _lowercase   :
   # setable values
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None
   @classmethod
   def a     (	cls					:			List[Any]			)   ->       int:
        return cls()




@dataclass
class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     ScoreSdeVeSchedulerState
   _SCREAMING_SNAKE_CASE     :     jnp.ndarray
   _SCREAMING_SNAKE_CASE     :     Optional[jnp.ndarray]									=  None




class  _lowercase   (  __lowercase	,				__lowercase       ):
   @property
   def a     (	self					:			Any			)   ->       List[Any]:
        return True
   @register_to_config
   def __init__(	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			int = 2000					,      SCREAMING_SNAKE_CASE_					:			float = 0.1_5					,      SCREAMING_SNAKE_CASE_					:			float = 0.0_1					,      SCREAMING_SNAKE_CASE_					:			float = 1_3_4_8.0					,      SCREAMING_SNAKE_CASE_					:			float = 1e-5					,      SCREAMING_SNAKE_CASE_					:			int = 1					,      )   ->       Dict:
        pass
   def a     (	self					:			str			)   ->       List[str]:
        __snake_case    			=	ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            SCREAMING_SNAKE_CASE_					,      self.config.num_train_timesteps					,      self.config.sigma_min					,      self.config.sigma_max					,      self.config.sampling_eps					,      )
   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			ScoreSdeVeSchedulerState					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			Tuple = ()					,      SCREAMING_SNAKE_CASE_					:			float = None			)   ->       ScoreSdeVeSchedulerState:
        __snake_case    			=	sampling_eps if sampling_eps is not None else self.config.sampling_eps

        __snake_case    			=	jnp.linspace(1					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        return state.replace(timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			int					,      SCREAMING_SNAKE_CASE_					:			ScoreSdeVeSchedulerState					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			float = None					,      SCREAMING_SNAKE_CASE_					:			float = None					,      SCREAMING_SNAKE_CASE_					:			float = None					,      )   ->       ScoreSdeVeSchedulerState:
        __snake_case    			=	sigma_min if sigma_min is not None else self.config.sigma_min
        __snake_case    			=	sigma_max if sigma_max is not None else self.config.sigma_max
        __snake_case    			=	sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
             __snake_case    			=	self.set_timesteps(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	jnp.exp(jnp.linspace(jnp.log(SCREAMING_SNAKE_CASE_			)					,      jnp.log(SCREAMING_SNAKE_CASE_			)					,      SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]			)

        return state.replace(discrete_sigmas=SCREAMING_SNAKE_CASE_					,      sigmas=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			List[str]			)   ->       Dict:
        return jnp.where(timesteps == 0					,      jnp.zeros_like(SCREAMING_SNAKE_CASE_			)					,      state.discrete_sigmas[timesteps - 1]			)
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			ScoreSdeVeSchedulerState					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			int					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			random.KeyArray					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      )   ->       Union[FlaxSdeVeOutput, Tuple]:
        if state.timesteps is None:
             raise ValueError(
                 '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

        __snake_case    			=	timestep * jnp.ones(
            sample.shape[0]					,      )
        __snake_case    			=	(timestep * (len(state.timesteps			) - 1)).long()

        __snake_case    			=	state.discrete_sigmas[timesteps]
        __snake_case    			=	self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	jnp.zeros_like(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	(sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        __snake_case    			=	diffusion.flatten()
        __snake_case    			=	broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_					,      sample.shape			)
        __snake_case    			=	drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        __snake_case    			=	random.split(SCREAMING_SNAKE_CASE_					,      num=1			)
        __snake_case    			=	random.normal(key=SCREAMING_SNAKE_CASE_					,      shape=sample.shape			)
        __snake_case    			=	sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        __snake_case    			=	prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
             return (prev_sample, prev_sample_mean, state)

        return FlaxSdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_					,      prev_sample_mean=SCREAMING_SNAKE_CASE_					,      state=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			ScoreSdeVeSchedulerState					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			jnp.ndarray					,      SCREAMING_SNAKE_CASE_					:			random.KeyArray					,      SCREAMING_SNAKE_CASE_					:			bool = True					,      )   ->       Union[FlaxSdeVeOutput, Tuple]:
        if state.timesteps is None:
             raise ValueError(
                 '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        __snake_case    			=	random.split(SCREAMING_SNAKE_CASE_					,      num=1			)
        __snake_case    			=	random.normal(key=SCREAMING_SNAKE_CASE_					,      shape=sample.shape			)

        # compute step size from the model_output, the noise, and the snr
        __snake_case    			=	jnp.linalg.norm(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	jnp.linalg.norm(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        __snake_case    			=	step_size * jnp.ones(sample.shape[0]			)

        # compute corrected sample: model_output term and noise term
        __snake_case    			=	step_size.flatten()
        __snake_case    			=	broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE_					,      sample.shape			)
        __snake_case    			=	sample + step_size * model_output
        __snake_case    			=	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=SCREAMING_SNAKE_CASE_					,      state=SCREAMING_SNAKE_CASE_			)





   def __len__(	self					:			Optional[int]			)   ->       Any:
        return self.config.num_train_timesteps








'''simple docstring'''




import copy

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


_a      :      List[str]       				=      logging.get_logger(__name__)

_a      :      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  _lowercase   (  __lowercase       ):
   def __init__(	self					:			int					,      SCREAMING_SNAKE_CASE_					:			List[Any]=256					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=1024					,      SCREAMING_SNAKE_CASE_					:			Any=16					,      SCREAMING_SNAKE_CASE_					:			int=16					,      SCREAMING_SNAKE_CASE_					:			List[Any]=4					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			Tuple=1e-6					,      **SCREAMING_SNAKE_CASE_					:			Optional[Any]					,      )   ->       Tuple:
        super().__init__(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	hidden_size
        __snake_case    			=	image_size
        __snake_case    			=	patch_size
        __snake_case    			=	image_size // patch_size
        __snake_case    			=	mask_input_channels
        __snake_case    			=	num_point_embeddings
        __snake_case    			=	hidden_act
        __snake_case    			=	layer_norm_eps




class  _lowercase   (  __lowercase       ):
   def __init__(	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[Any]=256					,      SCREAMING_SNAKE_CASE_					:			Dict="relu"					,      SCREAMING_SNAKE_CASE_					:			Any=2048					,      SCREAMING_SNAKE_CASE_					:			int=2					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=8					,      SCREAMING_SNAKE_CASE_					:			int=2					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=3					,      SCREAMING_SNAKE_CASE_					:			str=3					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=256					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=1e-6					,      **SCREAMING_SNAKE_CASE_					:			int					,      )   ->       str:
        super().__init__(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	hidden_size
        __snake_case    			=	hidden_act
        __snake_case    			=	mlp_dim
        __snake_case    			=	num_hidden_layers
        __snake_case    			=	num_attention_heads
        __snake_case    			=	attention_downsample_rate
        __snake_case    			=	num_multimask_outputs
        __snake_case    			=	iou_head_depth
        __snake_case    			=	iou_head_hidden_dim
        __snake_case    			=	layer_norm_eps




class  _lowercase   (  __lowercase       ):
   def __init__(	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			int=768					,      SCREAMING_SNAKE_CASE_					:			int=256					,      SCREAMING_SNAKE_CASE_					:			str=12					,      SCREAMING_SNAKE_CASE_					:			Tuple=12					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=3					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=1024					,      SCREAMING_SNAKE_CASE_					:			List[str]=16					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			Any=1e-06					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=0.0					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=1e-10					,      SCREAMING_SNAKE_CASE_					:			List[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=4.0					,      SCREAMING_SNAKE_CASE_					:			List[Any]=True					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=True					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=14					,      SCREAMING_SNAKE_CASE_					:			str=[2, 5, 8, 11]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=128					,      SCREAMING_SNAKE_CASE_					:			Tuple=None					,      **SCREAMING_SNAKE_CASE_					:			List[Any]					,      )   ->       Optional[Any]:
        super().__init__(**SCREAMING_SNAKE_CASE_			)

        __snake_case    			=	hidden_size
        __snake_case    			=	output_channels
        __snake_case    			=	num_hidden_layers
        __snake_case    			=	num_attention_heads
        __snake_case    			=	num_channels
        __snake_case    			=	image_size
        __snake_case    			=	patch_size
        __snake_case    			=	hidden_act
        __snake_case    			=	layer_norm_eps
        __snake_case    			=	attention_dropout
        __snake_case    			=	initializer_range
        __snake_case    			=	qkv_bias
        __snake_case    			=	mlp_ratio
        __snake_case    			=	use_abs_pos
        __snake_case    			=	use_rel_pos
        __snake_case    			=	window_size
        __snake_case    			=	global_attn_indexes
        __snake_case    			=	num_pos_feats
        __snake_case    			=	int(hidden_size * mlp_ratio			) if mlp_dim is None else mlp_dim




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Union[str, Any]									=  "sam"
   _SCREAMING_SNAKE_CASE     :     Any									=  True
   def __init__(	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			Dict=None					,      SCREAMING_SNAKE_CASE_					:			List[Any]=None					,      SCREAMING_SNAKE_CASE_					:			List[str]=None					,      SCREAMING_SNAKE_CASE_					:			str=0.0_2					,      **SCREAMING_SNAKE_CASE_					:			str					,      )   ->       str:
        super().__init__(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	vision_config if vision_config is not None else {}
        __snake_case    			=	prompt_encoder_config if prompt_encoder_config is not None else {}
        __snake_case    			=	mask_decoder_config if mask_decoder_config is not None else {}

        if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	vision_config.to_dict()
        if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	prompt_encoder_config.to_dict()
        if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	mask_decoder_config.to_dict()

        __snake_case    			=	SamVisionConfig(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	SamPromptEncoderConfig(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	SamMaskDecoderConfig(**SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	initializer_range





   def a     (	self					:			int			)   ->       str:
        __snake_case    			=	copy.deepcopy(self.__dict__			)
        __snake_case    			=	self.vision_config.to_dict()
        __snake_case    			=	self.prompt_encoder_config.to_dict()
        __snake_case    			=	self.mask_decoder_config.to_dict()
        __snake_case    			=	self.__class__.model_type
        return output




