



from typing import TYPE_CHECKING

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


_lowercase:				Any       =	{"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:				Union[str, Any]       =	["ViltFeatureExtractor"]
				_lowercase:				Any       =	["ViltImageProcessor"]
				_lowercase:				Optional[Any]       =	["ViltProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:				Optional[int]       =	[
				    "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViltForImageAndTextRetrieval",
				    "ViltForImagesAndTextClassification",
				    "ViltForTokenClassification",
				    "ViltForMaskedLM",
				    "ViltForQuestionAnswering",
				    "ViltLayer",
				    "ViltModel",
				    "ViltPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vilt import ViltFeatureExtractor
								from .image_processing_vilt import ViltImageProcessor
								from .processing_vilt import ViltProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vilt import (
								    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViltForImageAndTextRetrieval,
								    ViltForImagesAndTextClassification,
								    ViltForMaskedLM,
								    ViltForQuestionAnswering,
								    ViltForTokenClassification,
								    ViltLayer,
								    ViltModel,
								    ViltPreTrainedModel,
								)


else:
				import sys

				_lowercase:				Union[str, Any]       =	_LazyModule(__name__, globals()["__file__"], _import_structure)





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

						a        =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_	)	, jnp.log(lowerCamelCase_	)	, lowerCamelCase_	)	)
						a        =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowerCamelCase_	, sigmas=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						a        =     timestep * jnp.ones(
						    sample.shape[0]	, )
						a        =     (timestep * (len(state.timesteps	) - 1)).long()

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (sigma**2 - adjacent_sigma**2) ** 0.5

						# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
						# also equation 47 shows the analog from SDE models to ancestral sampling methods
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)
						a        =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, prev_sample_mean=lowerCamelCase_	, state=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

						# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
						# sample noise for correction
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a        =     step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						a        =     step_size.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     sample + step_size * model_output
						a        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
