


'''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


__snake_case							      =     '''scheduler_config.json'''
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				1
		_a     =				2
		_a     =				3
		_a     =				4
		_a     =				5



@dataclass
class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				42



class    lowercase	:

		"""simple docstring"""

		_a     =				SCHEDULER_CONFIG_NAME
		_a     =				['dtype']
		_a     =				[]
		_a     =				True




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_=False    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= cls.load_config(
							    pretrained_model_name_or_path=UpperCamelCase_    ,   subfolder=UpperCamelCase_    ,   return_unused_kwargs=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= cls.from_config(UpperCamelCase_    ,   return_unused_kwargs=UpperCamelCase_    ,   **UpperCamelCase_							)

							if hasattr(UpperCamelCase_    ,   '''create_state'''							) and getattr(UpperCamelCase_    ,   '''has_state'''    ,   UpperCamelCase_							):
												UpperCamelCase__   :str												= scheduler.create_state()

							if return_unused_kwargs:
												return scheduler, state, unused_kwargs

							return scheduler, state




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = False    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self._get_compatibles()




		@classmethod
		def     lowerCAmelCase__				(		cls							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= list(set([cls.__name__] + cls._compatibles							)							)
							UpperCamelCase__   :Union[str, Any]												= importlib.import_module(__name__.split('''.'''							)[0]							)
							UpperCamelCase__   :int												= [
							    getattr(UpperCamelCase_    ,   UpperCamelCase_							) for c in compatible_classes_str if hasattr(UpperCamelCase_    ,   UpperCamelCase_							)
							]
							return compatible_classes



def 			a      (			__a ,					__a   )  ->	jnp.ndarray:



					'''simple docstring'''






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


def 			a      (			__a ,					__a=0.9_9_9 ,					__a=jnp.floataa   )  ->	jnp.ndarray:



					'''simple docstring'''






					def alpha_bar(__a   ):
										return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

					UpperCamelCase__   :Tuple												= []
					for i in range(__a   ):
										UpperCamelCase__   :List[Any]												= i / num_diffusion_timesteps
										UpperCamelCase__   :Optional[int]												= (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar(__a   ) / alpha_bar(__a   ) ,					__a   )   )
					return jnp.array(__a ,					dtype=__a   )
@flax.struct.dataclass
class    lowercase	:

		"""simple docstring"""

		_a     =				42
		_a     =				42
		_a     =				42




		@classmethod
		def     lowerCAmelCase__				(		cls    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= 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__   :int												= 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__   :Tuple												= (
												    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__   :Tuple												= 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__   :List[Any]												= 1.0 - betas

							UpperCamelCase__   :Optional[Any]												= jnp.cumprod(UpperCamelCase_    ,   axis=0							)

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



def 			a      (			__a ,					__a ,					__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= state.alphas_cumprod

					UpperCamelCase__   :Optional[int]												= alphas_cumprod[timesteps] ** 0.5
					UpperCamelCase__   :Tuple												= sqrt_alpha_prod.flatten()
					UpperCamelCase__   :int												= broadcast_to_shape_from_left(__a ,					original_samples.shape   )

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

					return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def 			a      (			__a ,					__a ,					__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__					,				UpperCamelCase__   :Tuple												= get_sqrt_alpha_prod(__a ,					__a ,					__a ,					__a   )
					UpperCamelCase__   :List[str]												= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples







def 			a      (			__a ,					__a ,					__a ,					__a   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__					,				UpperCamelCase__   :List[str]												= get_sqrt_alpha_prod(__a ,					__a ,					__a ,					__a   )
					UpperCamelCase__   :Optional[Any]												= sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
					return velocity



'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case							      =     {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''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

		__snake_case							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)