






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 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= 1
       lowerCamelCase							:				Optional[Any] 				= 2
       lowerCamelCase							:				List[Any] 				= 3
       lowerCamelCase							:				Any 				= 4
       lowerCamelCase							:				List[str] 				= 5




@dataclass
class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				jnp.ndarray




class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				Dict 				= SCHEDULER_CONFIG_NAME
       lowerCamelCase							:				Optional[Any] 				= ["dtype"]
       lowerCamelCase							:				List[Any] 				= []
       lowerCamelCase							:				List[str] 				= True



       @classmethod
       def 					__lowercase						(							cls			,						a__ = None			,						a__ = None			,						a__=False			,						**a__			,						) ->      List[str]:






            '''simple docstring'''





            __snake_case       , __snake_case			:Dict          =						cls.load_config(
                pretrained_model_name_or_path=a__			,						subfolder=a__			,						return_unused_kwargs=a__			,						**a__			,						)
            __snake_case       , __snake_case			:int          =						cls.from_config(a__			,						return_unused_kwargs=a__			,						**a__    )

            if hasattr(a__			,						"""create_state"""    ) and getattr(a__			,						"""has_state"""			,						a__    ):
                 __snake_case			:Optional[int]          =						scheduler.create_state()

            if return_unused_kwargs:
                 return scheduler, state, unused_kwargs

            return scheduler, state



       def 					__lowercase						(							self			,						a__			,						a__ = False			,						**a__    ) ->      Dict:






            '''simple docstring'''





            self.save_config(save_directory=a__			,						push_to_hub=a__			,						**a__    )



       @property
       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            return self._get_compatibles()



       @classmethod
       def 					__lowercase						(							cls    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Any          =						list(set([cls.__name__] + cls._compatibles    )    )
            __snake_case			:Optional[Any]          =						importlib.import_module(__name__.split("""."""    )[0]    )
            __snake_case			:Any          =						[
                getattr(a__			,						a__    ) for c in compatible_classes_str if hasattr(a__			,						a__    )
            ]
            return compatible_classes






def   UpperCamelCase  (       snake_case__     :   jnp.ndarray	,snake_case__     :   Tuple[int]  ):




     '''simple docstring'''






     assert len(snake_case__  ) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case__  ) - x.ndim)  )	,snake_case__  )






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   Dict=0.9_9_9	,snake_case__     :   str=jnp.floataa  ):




     '''simple docstring'''






     def alpha_bar(snake_case__     :   Optional[int]  ):
          return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2  ) ** 2

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




@flax.struct.dataclass
class 			snake_case__   :

       '''simple docstring'''
       lowerCamelCase							:				jnp.ndarray
       lowerCamelCase							:				jnp.ndarray
       lowerCamelCase							:				jnp.ndarray



       @classmethod
       def 					__lowercase						(							cls			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Tuple          =						scheduler.config

            if config.trained_betas is not None:
                 __snake_case			:str          =						jnp.asarray(config.trained_betas			,						dtype=scheduler.dtype    )
            elif config.beta_schedule == "linear":
                 __snake_case			:Union[str, Any]          =						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.
                 __snake_case			:int          =						(
                     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
                 __snake_case			:List[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__}'''    )

            __snake_case			:List[Any]          =						1.0 - betas

            __snake_case			:Dict          =						jnp.cumprod(a__			,						axis=0    )

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






def   UpperCamelCase  (       snake_case__     :   CommonSchedulerState	,snake_case__     :   jnp.ndarray	,snake_case__     :   jnp.ndarray	,snake_case__     :   jnp.ndarray  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						state.alphas_cumprod

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

     __snake_case			:Tuple          =						(1 - alphas_cumprod[timesteps]) ** 0.5
     __snake_case			:Dict          =						sqrt_one_minus_alpha_prod.flatten()
     __snake_case			:Union[str, Any]          =						broadcast_to_shape_from_left(snake_case__	,original_samples.shape  )

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def   UpperCamelCase  (       snake_case__     :   CommonSchedulerState	,snake_case__     :   jnp.ndarray	,snake_case__     :   jnp.ndarray	,snake_case__     :   jnp.ndarray  ):




     '''simple docstring'''






     __snake_case       , __snake_case			:Tuple          =						get_sqrt_alpha_prod(snake_case__	,snake_case__	,snake_case__	,snake_case__  )
     __snake_case			:int          =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples






def   UpperCamelCase  (       snake_case__     :   CommonSchedulerState	,snake_case__     :   jnp.ndarray	,snake_case__     :   jnp.ndarray	,snake_case__     :   jnp.ndarray  ):




     '''simple docstring'''






     __snake_case       , __snake_case			:List[str]          =						get_sqrt_alpha_prod(snake_case__	,snake_case__	,snake_case__	,snake_case__  )
     __snake_case			:Optional[int]          =						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity










from typing import TYPE_CHECKING

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


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase__							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


