


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_snake_case					:		Tuple      =    logging.get_logger(__name__)

_snake_case					:		Tuple      =    {
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  "dpt"

							def __init__(							self	:  Dict   ,     lowerCamelCase	:  Union[str, Any]=768   ,     lowerCamelCase	:  List[Any]=12   ,     lowerCamelCase	:  Tuple=12   ,     lowerCamelCase	:  Dict=3072   ,     lowerCamelCase	:  Union[str, Any]="gelu"   ,     lowerCamelCase	:  Dict=0.0   ,     lowerCamelCase	:  int=0.0   ,     lowerCamelCase	:  Optional[Any]=0.02   ,     lowerCamelCase	:  List[Any]=1E-12   ,     lowerCamelCase	:  Optional[int]=384   ,     lowerCamelCase	:  Optional[Any]=16   ,     lowerCamelCase	:  List[str]=3   ,     lowerCamelCase	:  Dict=False   ,     lowerCamelCase	:  int=True   ,     lowerCamelCase	:  Dict=[2, 5, 8, 11]   ,     lowerCamelCase	:  Optional[int]="project"   ,     lowerCamelCase	:  Dict=[4, 2, 1, 0.5]   ,     lowerCamelCase	:  int=[96, 192, 384, 768]   ,     lowerCamelCase	:  List[str]=256   ,     lowerCamelCase	:  List[Any]=-1   ,     lowerCamelCase	:  Union[str, Any]=False   ,     lowerCamelCase	:  Any=True   ,     lowerCamelCase	:  int=0.4   ,     lowerCamelCase	:  List[Any]=255   ,     lowerCamelCase	:  Optional[Any]=0.1   ,     lowerCamelCase	:  List[str]=[1, 1024, 24, 24]   ,     lowerCamelCase	:  Tuple=[0, 1]   ,     lowerCamelCase	:  int=None   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Any:
														super().__init__(**lowerCamelCase    )

														__snake_case		:      List[Any]										=       hidden_size
														__snake_case		:      int										=       is_hybrid

														if self.is_hybrid:
																					if backbone_config is None:
																												logger.info("Initializing the config with a `BiT` backbone."    )
																												__snake_case		:      List[Any]										=       {
																												    "global_padding": "same",
																												    "layer_type": "bottleneck",
																												    "depths": [3, 4, 9],
																												    "out_features": ["stage1", "stage2", "stage3"],
																												    "embedding_dynamic_padding": True,
																												}
																												__snake_case		:      List[Any]										=       BitConfig(**lowerCamelCase    )
																					elif isinstance(lowerCamelCase   ,     lowerCamelCase    ):
																												logger.info("Initializing the config with a `BiT` backbone."    )
																												__snake_case		:      str										=       BitConfig(**lowerCamelCase    )
																					elif isinstance(lowerCamelCase   ,     lowerCamelCase    ):
																												__snake_case		:      Dict										=       backbone_config
																					else:
																												raise ValueError(
																												    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'    )

																					__snake_case		:      Dict										=       backbone_featmap_shape
																					__snake_case		:      Optional[int]										=       neck_ignore_stages

																					if readout_type != "project":
																												raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."    )
														else:
																					__snake_case		:      List[Any]										=       None
																					__snake_case		:      Tuple										=       None
																					__snake_case		:      Optional[int]										=       []

														__snake_case		:      Any										=       num_hidden_layers
														__snake_case		:      Optional[Any]										=       num_attention_heads
														__snake_case		:      List[str]										=       intermediate_size
														__snake_case		:      int										=       hidden_act
														__snake_case		:      int										=       hidden_dropout_prob
														__snake_case		:      Optional[Any]										=       attention_probs_dropout_prob
														__snake_case		:      Dict										=       initializer_range
														__snake_case		:      Union[str, Any]										=       layer_norm_eps
														__snake_case		:      List[Any]										=       image_size
														__snake_case		:      Optional[Any]										=       patch_size
														__snake_case		:      str										=       num_channels
														__snake_case		:      str										=       qkv_bias
														__snake_case		:      Union[str, Any]										=       backbone_out_indices
														if readout_type not in ["ignore", "add", "project"]:
																					raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"    )
														__snake_case		:      Tuple										=       readout_type
														__snake_case		:      List[str]										=       reassemble_factors
														__snake_case		:      Tuple										=       neck_hidden_sizes
														__snake_case		:      Any										=       fusion_hidden_size
														__snake_case		:      int										=       head_in_index
														__snake_case		:      List[str]										=       use_batch_norm_in_fusion_residual
														# auxiliary head attributes (semantic segmentation)
														__snake_case		:      Dict										=       use_auxiliary_head
														__snake_case		:      Optional[int]										=       auxiliary_loss_weight
														__snake_case		:      Union[str, Any]										=       semantic_loss_ignore_index
														__snake_case		:      Any										=       semantic_classifier_dropout






							def    __snake_case (							self	:  Union[str, Any]    )     ->   List[Any]:
														__snake_case		:      int										=       copy.deepcopy(self.__dict__    )

														if output["backbone_config"] is not None:
																					__snake_case		:      Any										=       self.backbone_config.to_dict()

														__snake_case		:      int										=       self.__class__.model_type
														return output





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					:		int      =    "scheduler_config.json"

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  1
							__UpperCAmelCase   :							Tuple					  =  2
							__UpperCAmelCase   :							Union[str, Any]					  =  3
							__UpperCAmelCase   :							List[Any]					  =  4
							__UpperCAmelCase   :							Tuple					  =  5

@dataclass
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray

class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  SCHEDULER_CONFIG_NAME
							__UpperCAmelCase   :							Union[str, Any]					  =  ["dtype"]
							__UpperCAmelCase   :							Tuple					  =  []
							__UpperCAmelCase   :							int					  =  True

							@classmethod
							def    __snake_case (							cls	:  List[str]   ,     lowerCamelCase	:  Dict[str, Any] = None   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  List[str]=False   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   List[str]:
														__snake_case			,      __snake_case		:      List[str]										=       cls.load_config(
														    pretrained_model_name_or_path=lowerCamelCase   ,     subfolder=lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase   ,     )
														__snake_case			,      __snake_case		:      Dict										=       cls.from_config(lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase    )

														if hasattr(lowerCamelCase   ,     "create_state"    ) and getattr(lowerCamelCase   ,     "has_state"   ,     lowerCamelCase    ):
																					__snake_case		:      Tuple										=       scheduler.create_state()

														if return_unused_kwargs:
																					return scheduler, state, unused_kwargs

														return scheduler, state

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Union[str, os.PathLike]   ,     lowerCamelCase	:  bool = False   ,     **lowerCamelCase	:  List[Any]    )     ->   int:
														self.save_config(save_directory=lowerCamelCase   ,     push_to_hub=lowerCamelCase   ,     **lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														return self._get_compatibles()






							@classmethod
							def    __snake_case (							cls	:  int    )     ->   Dict:
														__snake_case		:      Tuple										=       list(set([cls.__name__] + cls._compatibles    )    )
														__snake_case		:      int										=       importlib.import_module(__name__.split("."    )[0]    )
														__snake_case		:      Tuple										=       [
														    getattr(lowerCamelCase   ,     lowerCamelCase    ) for c in compatible_classes_str if hasattr(lowerCamelCase   ,     lowerCamelCase    )
														]
														return compatible_classes







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							assert len(__lowerCamelCase     ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCamelCase     ) - x.ndim)     )							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=0.9_9_9							,     __lowerCamelCase=jnp.floataa     ):

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

							__snake_case		:      List[Any]										=       []
							for i in range(__lowerCamelCase     ):
														__snake_case		:      Dict										=       i / num_diffusion_timesteps
														__snake_case		:      str										=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(__lowerCamelCase     ) / alpha_bar(__lowerCamelCase     )							,     __lowerCamelCase     )     )
							return jnp.array(__lowerCamelCase							,     dtype=__lowerCamelCase     )

@flax.struct.dataclass
class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray

							@classmethod
							def    __snake_case (							cls	:  Union[str, Any]   ,     lowerCamelCase	:  int    )     ->   List[Any]:
														__snake_case		:      Dict										=       scheduler.config

														if config.trained_betas is not None:
																					__snake_case		:      Dict										=       jnp.asarray(config.trained_betas   ,     dtype=scheduler.dtype    )
														elif config.beta_schedule == "linear":
																					__snake_case		:      Optional[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.
																					__snake_case		:      str										=       (
																					    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		:      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__}'    )

														__snake_case		:      Any										=       1.0 - betas

														__snake_case		:      int										=       jnp.cumprod(lowerCamelCase   ,     axis=0    )

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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       state.alphas_cumprod

							__snake_case		:      str										=       alphas_cumprod[timesteps] ** 0.5
							__snake_case		:      Dict										=       sqrt_alpha_prod.flatten()
							__snake_case		:      str										=       broadcast_to_shape_from_left(__lowerCamelCase							,     original_samples.shape     )

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

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Union[str, Any]										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      List[str]										=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Dict										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      Optional[int]										=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity

