


'''simple docstring'''





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__lowerCamelCase												=						'''scheduler_config.json'''




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					1
 UpperCAmelCase				=					2
 UpperCAmelCase				=					3
 UpperCAmelCase				=					4
 UpperCAmelCase				=					5




@dataclass
class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					42




class 				UpperCAmelCase			:
 UpperCAmelCase				=					SCHEDULER_CONFIG_NAME
 UpperCAmelCase				=					["dtype"]
 UpperCAmelCase				=					[]
 UpperCAmelCase				=					True


 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	Dict			,       __lowerCamelCase							:	Dict[str, Any] = None			,       __lowerCamelCase							:	Optional[str] = None			,       __lowerCamelCase							:	Optional[Any]=False			,       **__lowerCamelCase							:	str			,       ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      cls.load_config(
        pretrained_model_name_or_path=__lowerCamelCase			,       subfolder=__lowerCamelCase			,       return_unused_kwargs=__lowerCamelCase			,       **__lowerCamelCase			,       )
    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      cls.from_config(__lowerCamelCase			,       return_unused_kwargs=__lowerCamelCase			,       **__lowerCamelCase )

    if hasattr(__lowerCamelCase			,       '''create_state''' ) and getattr(__lowerCamelCase			,       '''has_state'''			,       __lowerCamelCase ):
       UpperCAmelCase__	:str      =      scheduler.create_state()

    if return_unused_kwargs:
       return scheduler, state, unused_kwargs

    return scheduler, state


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	Union[str, os.PathLike]			,       __lowerCamelCase							:	bool = False			,       **__lowerCamelCase							:	Optional[int] ):
    self.save_config(save_directory=__lowerCamelCase			,       push_to_hub=__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return self._get_compatibles()





 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	List[str] ):
    UpperCAmelCase__	:int      =      list(set([cls.__name__] + cls._compatibles ) )
    UpperCAmelCase__	:int      =      importlib.import_module(__name__.split('''.''' )[0] )
    UpperCAmelCase__	:Tuple      =      [
        getattr(__lowerCamelCase			,       __lowerCamelCase ) for c in compatible_classes_str if hasattr(__lowerCamelCase			,       __lowerCamelCase )
    ]
    return compatible_classes


def        a__     (					UpperCamelCase_						:      jnp.ndarray,   UpperCamelCase_						:      Tuple[int]						):
   assert len(UpperCamelCase_						) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCamelCase_						) - x.ndim)						),   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      Dict=0.999,   UpperCamelCase_						:      Dict=jnp.floataa						):

   def alpha_bar(UpperCamelCase_						:      Any						):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2						) ** 2

   UpperCAmelCase__	:Dict      =      []
   for i in range(UpperCamelCase_						):
      UpperCAmelCase__	:Optional[int]      =      i / num_diffusion_timesteps
      UpperCAmelCase__	:Union[str, Any]      =      (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(UpperCamelCase_						) / alpha_bar(UpperCamelCase_						),   UpperCamelCase_						)						)
   return jnp.array(UpperCamelCase_,   dtype=UpperCamelCase_						)




@flax.struct.dataclass
class 				UpperCAmelCase			:
 UpperCAmelCase				=					42
 UpperCAmelCase				=					42
 UpperCAmelCase				=					42


 @classmethod
 def        __SCREAMING_SNAKE_CASE					(      cls							:	Tuple			,       __lowerCamelCase							:	Union[str, Any] ):
    UpperCAmelCase__	:str      =      scheduler.config

    if config.trained_betas is not None:
       UpperCAmelCase__	:Tuple      =      jnp.asarray(config.trained_betas			,       dtype=scheduler.dtype )
    elif config.beta_schedule == "linear":
       UpperCAmelCase__	:List[str]      =      jnp.linspace(config.beta_start			,       config.beta_end			,       config.num_train_timesteps			,       dtype=scheduler.dtype )
    elif config.beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       UpperCAmelCase__	:List[Any]      =      (
           jnp.linspace(
               config.beta_start**0.5			,       config.beta_end**0.5			,       config.num_train_timesteps			,       dtype=scheduler.dtype )
           ** 2
       )
    elif config.beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       UpperCAmelCase__	:Optional[Any]      =      betas_for_alpha_bar(config.num_train_timesteps			,       dtype=scheduler.dtype )
    else:
       raise NotImplementedError(
           f'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''' )

    UpperCAmelCase__	:int      =      1.0 - betas

    UpperCAmelCase__	:Tuple      =      jnp.cumprod(__lowerCamelCase			,       axis=0 )

    return cls(
        alphas=__lowerCamelCase			,       betas=__lowerCamelCase			,       alphas_cumprod=__lowerCamelCase			,       )


def        a__     (					UpperCamelCase_						:      CommonSchedulerState,   UpperCamelCase_						:      jnp.ndarray,   UpperCamelCase_						:      jnp.ndarray,   UpperCamelCase_						:      jnp.ndarray						):
   UpperCAmelCase__	:Optional[Any]      =      state.alphas_cumprod

   UpperCAmelCase__	:List[str]      =      alphas_cumprod[timesteps] ** 0.5
   UpperCAmelCase__	:Any      =      sqrt_alpha_prod.flatten()
   UpperCAmelCase__	:Optional[Any]      =      broadcast_to_shape_from_left(UpperCamelCase_,   original_samples.shape						)

   UpperCAmelCase__	:List[str]      =      (1 - alphas_cumprod[timesteps]) ** 0.5
   UpperCAmelCase__	:Tuple      =      sqrt_one_minus_alpha_prod.flatten()
   UpperCAmelCase__	:List[Any]      =      broadcast_to_shape_from_left(UpperCamelCase_,   original_samples.shape						)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def        a__     (					UpperCamelCase_						:      CommonSchedulerState,   UpperCamelCase_						:      jnp.ndarray,   UpperCamelCase_						:      jnp.ndarray,   UpperCamelCase_						:      jnp.ndarray						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      get_sqrt_alpha_prod(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)
   UpperCAmelCase__	:List[Any]      =      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples


def        a__     (					UpperCamelCase_						:      CommonSchedulerState,   UpperCamelCase_						:      jnp.ndarray,   UpperCamelCase_						:      jnp.ndarray,   UpperCamelCase_						:      jnp.ndarray						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      get_sqrt_alpha_prod(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)
   UpperCAmelCase__	:Union[str, Any]      =      sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



