

# 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   __UpperCamelCase      :
	# setable values
	SCREAMING_SNAKE_CASE__      			=       None
	SCREAMING_SNAKE_CASE__      			=       None
	SCREAMING_SNAKE_CASE__      			=       None




	@classmethod
	def  __A   (						cls		:  Dict							):


		'''simple docstring'''







		return cls()


@dataclass
class   __UpperCamelCase      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       None


class   __UpperCamelCase      (   lowercase       ,       lowercase  ):




	@property
	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		return True




	@register_to_config
	def __init__(						self		:  List[str]						,		lowerCAmelCase		:  int = 2_000						,		lowerCAmelCase		:  float = 0.15						,		lowerCAmelCase		:  float = 0.01						,		lowerCAmelCase		:  float = 1_348.0						,		lowerCAmelCase		:  float = 1e-5						,		lowerCAmelCase		:  int = 1						,		):


		'''simple docstring'''







		pass




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     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  __A   (						self		:  int						,		lowerCAmelCase		:  ScoreSdeVeSchedulerState						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  Tuple = ()						,		lowerCAmelCase		:  float = None							):


		'''simple docstring'''







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

		UpperCAmelCase_    			=     jnp.linspace(1						,		lowerCAmelCase						,		lowerCAmelCase							)
		return state.replace(timesteps=lowerCAmelCase							)




	def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  ScoreSdeVeSchedulerState						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  float = None						,		lowerCAmelCase		:  float = None						,		lowerCAmelCase		:  float = None						,		):


		'''simple docstring'''







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

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

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




	def  __A   (						self		:  str						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  Union[str, Any]							):


		'''simple docstring'''







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




	def  __A   (						self		:  str						,		lowerCAmelCase		:  ScoreSdeVeSchedulerState						,		lowerCAmelCase		:  jnp.ndarray						,		lowerCAmelCase		:  int						,		lowerCAmelCase		:  jnp.ndarray						,		lowerCAmelCase		:  random.KeyArray						,		lowerCAmelCase		:  bool = 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"							)

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

		UpperCAmelCase_    			=     state.discrete_sigmas[timesteps]
		UpperCAmelCase_    			=     self.get_adjacent_sigma(lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     jnp.zeros_like(lowerCAmelCase							)
		UpperCAmelCase_    			=     (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
		UpperCAmelCase_    			=     diffusion.flatten()
		UpperCAmelCase_    			=     broadcast_to_shape_from_left(lowerCAmelCase						,		sample.shape							)
		UpperCAmelCase_    			=     drift - diffusion**2 * model_output

		#  equation 6: sample noise for the diffusion term of
		UpperCAmelCase_    			=     random.split(lowerCAmelCase						,		num=1							)
		UpperCAmelCase_    			=     random.normal(key=lowerCAmelCase						,		shape=sample.shape							)
		UpperCAmelCase_    			=     sample - drift  # subtract because `dt` is a small negative timestep
		# TODO is the variable diffusion the correct scaling term for the noise?
		UpperCAmelCase_    			=     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  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  ScoreSdeVeSchedulerState						,		lowerCAmelCase		:  jnp.ndarray						,		lowerCAmelCase		:  jnp.ndarray						,		lowerCAmelCase		:  random.KeyArray						,		lowerCAmelCase		:  bool = 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
		UpperCAmelCase_    			=     random.split(lowerCAmelCase						,		num=1							)
		UpperCAmelCase_    			=     random.normal(key=lowerCAmelCase						,		shape=sample.shape							)

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

		# compute corrected sample: model_output term and noise term
		UpperCAmelCase_    			=     step_size.flatten()
		UpperCAmelCase_    			=     broadcast_to_shape_from_left(lowerCAmelCase						,		sample.shape							)
		UpperCAmelCase_    			=     sample + step_size * model_output
		UpperCAmelCase_    			=     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		:  Union[str, Any]							):


		'''simple docstring'''







		return self.config.num_train_timesteps


from typing import TYPE_CHECKING

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


_a:      Tuple								=     {
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      List[str]								=     ["""SegformerFeatureExtractor"""]
				_a:      Union[str, Any]								=     ["""SegformerImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SegformerDecodeHead""",
				    """SegformerForImageClassification""",
				    """SegformerForSemanticSegmentation""",
				    """SegformerLayer""",
				    """SegformerModel""",
				    """SegformerPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Optional[Any]								=     [
				    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFSegformerDecodeHead""",
				    """TFSegformerForImageClassification""",
				    """TFSegformerForSemanticSegmentation""",
				    """TFSegformerModel""",
				    """TFSegformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				_a:      Optional[int]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)