
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


lowercase__		:List[Any]			  =   "scheduler_config.json"







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[int]	 =1
							lowercase_	:	Tuple	 =2
							lowercase_	:	List[Any]	 =3
							lowercase_	:	List[Any]	 =4
							lowercase_	:	List[Any]	 =5







@dataclass
class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	jnp.ndarray







class     lowercase		:
							lowercase_	:	Dict	 =SCHEDULER_CONFIG_NAME
							lowercase_	:	List[str]	 =['''dtype''']
							lowercase_	:	Any	 =[]
							lowercase_	:	str	 =True




							@classmethod
							def 			A__				(    cls       ,A__ = None       ,A__ = None       ,A__=False       ,**A__       ,):
												lowercase     ,						lowercase						     =						cls.load_config(
												    pretrained_model_name_or_path=A__       ,subfolder=A__       ,return_unused_kwargs=A__       ,**A__       ,)
												lowercase     ,						lowercase						     =						cls.from_config(A__       ,return_unused_kwargs=A__       ,**A__)

												if hasattr(A__       ,'''create_state''') and getattr(A__       ,'''has_state'''       ,A__):
																	lowercase						     =						scheduler.create_state()

												if return_unused_kwargs:
																	return scheduler, state, unused_kwargs

												return scheduler, state




							def 			A__				(    self       ,A__       ,A__ = False       ,**A__):
												self.save_config(save_directory=A__       ,push_to_hub=A__       ,**A__)




							@property
							def 			A__				(    self):
												return self._get_compatibles()





							@classmethod
							def 			A__				(    cls):
												lowercase						     =						list(set([cls.__name__] + cls._compatibles))
												lowercase						     =						importlib.import_module(__name__.split('''.''')[0])
												lowercase						     =						[
												    getattr(A__       ,A__) for c in compatible_classes_str if hasattr(A__       ,A__)
												]
												return compatible_classes



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					assert len(lowerCAmelCase__		) >= x.ndim
					return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase__		) - x.ndim)		)			,      lowerCAmelCase__		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__=0.9_99			,      lowerCAmelCase__=jnp.floataa		):
					'''simple docstring'''

					def alpha_bar(lowerCAmelCase__		):
										return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2		) ** 2

					lowercase						     =						[]
					for i in range(lowerCAmelCase__		):
										lowercase						     =						i / num_diffusion_timesteps
										lowercase						     =						(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     lowercase		:
							lowercase_	:	jnp.ndarray
							lowercase_	:	jnp.ndarray
							lowercase_	:	jnp.ndarray




							@classmethod
							def 			A__				(    cls       ,A__):
												lowercase						     =						scheduler.config

												if config.trained_betas is not None:
																	lowercase						     =						jnp.asarray(config.trained_betas       ,dtype=scheduler.dtype)
												elif config.beta_schedule == "linear":
																	lowercase						     =						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.
																	lowercase						     =						(
																	    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
																	lowercase						     =						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__}')

												lowercase						     =						1.0 - betas

												lowercase						     =						jnp.cumprod(A__       ,axis=0)

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



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						state.alphas_cumprod

					lowercase						     =						alphas_cumprod[timesteps] ** 0.5
					lowercase						     =						sqrt_alpha_prod.flatten()
					lowercase						     =						broadcast_to_shape_from_left(lowerCAmelCase__			,      original_samples.shape		)

					lowercase						     =						(1 - alphas_cumprod[timesteps]) ** 0.5
					lowercase						     =						sqrt_one_minus_alpha_prod.flatten()
					lowercase						     =						broadcast_to_shape_from_left(lowerCAmelCase__			,      original_samples.shape		)

					return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase     ,						lowercase						     =						get_sqrt_alpha_prod(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)
					lowercase						     =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase     ,						lowercase						     =						get_sqrt_alpha_prod(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)
					lowercase						     =						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
					return velocity






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__		:str			  =   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:str			  =   ["ViTFeatureExtractor"]
							lowercase__		:int			  =   ["ViTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:int			  =   [
							    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTForImageClassification",
							    "ViTForMaskedImageModeling",
							    "ViTModel",
							    "ViTPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Any			  =   [
							    "TFViTForImageClassification",
							    "TFViTModel",
							    "TFViTPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "FlaxViTForImageClassification",
							    "FlaxViTModel",
							    "FlaxViTPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_vit import ViTFeatureExtractor
														from .image_processing_vit import ViTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit import (
														    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTForImageClassification,
														    ViTForMaskedImageModeling,
														    ViTModel,
														    ViTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
							import sys

							lowercase__		:Dict			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




