






from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


A__			:    Dict         =				logging.get_logger(__name__)

# General docstring
A__			:    List[Any]         =				'RegNetConfig'

# Base docstring
A__			:    Any         =				'facebook/regnet-y-040'
A__			:    Union[str, Any]         =				[1, 10_88, 7, 7]

# Image classification docstring
A__			:    Tuple         =				'facebook/regnet-y-040'
A__			:    Optional[int]         =				'tabby, tabby cat'

A__			:    int         =				[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int = 3,						lowerCamelCase		:					int = 1,						lowerCamelCase		:					int = 1,						lowerCamelCase		:					Optional[str] = "relu",						**lowerCamelCase		:					Optional[Any],						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								# The padding and conv has been verified in
								# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
								lowercase__     				=    tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2						)
								lowercase__     				=    tf.keras.layers.ConvaD(
								    filters=lowerCamelCase,						kernel_size=lowerCamelCase,						strides=lowerCamelCase,						padding='''VALID''',						groups=lowerCamelCase,						use_bias=lowerCamelCase,						name='''convolution''',						)
								lowercase__     				=    tf.keras.layers.BatchNormalization(epsilon=1E-5,						momentum=0.9,						name='''normalization'''						)
								lowercase__     				=    ACTaFN[activation] if activation is not None else tf.identity




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.convolution(self.padding(lowerCamelCase						)						)
								lowercase__     				=    self.normalization(lowerCamelCase						)
								lowercase__     				=    self.activation(lowerCamelCase						)
								return hidden_state



class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					RegNetConfig,						**lowerCamelCase		:					Dict						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    config.num_channels
								lowercase__     				=    TFRegNetConvLayer(
								    out_channels=config.embedding_size,						kernel_size=3,						stride=2,						activation=config.hidden_act,						name='''embedder''',						)




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    shape_list(lowerCamelCase						)[1]
								if tf.executing_eagerly() and num_channels != self.num_channels:
												raise ValueError(
												    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''						)

								# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
								# So change the input format from `NCHW` to `NHWC`.
								# shape = (batch_size, in_height, in_width, in_channels=num_channels)
								lowercase__     				=    tf.transpose(lowerCamelCase,						perm=(0, 2, 3, 1)						)
								lowercase__     				=    self.embedder(lowerCamelCase						)
								return hidden_state



class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int = 2,						**lowerCamelCase		:					str						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    tf.keras.layers.ConvaD(
								    filters=lowerCamelCase,						kernel_size=1,						strides=lowerCamelCase,						use_bias=lowerCamelCase,						name='''convolution'''						)
								lowercase__     				=    tf.keras.layers.BatchNormalization(epsilon=1E-5,						momentum=0.9,						name='''normalization'''						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					tf.Tensor,						lowerCamelCase		:					bool = False						):

								'''simple docstring'''
								return self.normalization(self.convolution(lowerCamelCase						),						training=lowerCamelCase						)



class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					int,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase,						name='''pooler'''						)
								lowercase__     				=    [
								    tf.keras.layers.ConvaD(filters=lowerCamelCase,						kernel_size=1,						activation='''relu''',						name='''attention.0'''						),
								    tf.keras.layers.ConvaD(filters=lowerCamelCase,						kernel_size=1,						activation='''sigmoid''',						name='''attention.2'''						),
								]




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
								lowercase__     				=    self.pooler(lowerCamelCase						)
								for layer_module in self.attention:
												lowercase__     				=    layer_module(lowerCamelCase						)
								lowercase__     				=    hidden_state * pooled
								return hidden_state



class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					Tuple,						lowerCamelCase		:					RegNetConfig,						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int = 1,						**lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    in_channels != out_channels or stride != 1
								lowercase__     				=    max(1,						out_channels // config.groups_width						)
								lowercase__     				=    (
								    TFRegNetShortCut(lowerCamelCase,						stride=lowerCamelCase,						name='''shortcut'''						)
								    if should_apply_shortcut
								    else tf.keras.layers.Activation('''linear''',						name='''shortcut'''						)
								)
								# `self.layers` instead of `self.layer` because that is a reserved argument.
								lowercase__     				=    [
								    TFRegNetConvLayer(lowerCamelCase,						kernel_size=1,						activation=config.hidden_act,						name='''layer.0'''						),
								    TFRegNetConvLayer(
								        lowerCamelCase,						stride=lowerCamelCase,						groups=lowerCamelCase,						activation=config.hidden_act,						name='''layer.1'''						),
								    TFRegNetConvLayer(lowerCamelCase,						kernel_size=1,						activation=lowerCamelCase,						name='''layer.2'''						),
								]
								lowercase__     				=    ACTaFN[config.hidden_act]




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    hidden_state
								for layer_module in self.layers:
												lowercase__     				=    layer_module(lowerCamelCase						)
								lowercase__     				=    self.shortcut(lowerCamelCase						)
								hidden_state += residual
								lowercase__     				=    self.activation(lowerCamelCase						)
								return hidden_state



class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					Tuple,						lowerCamelCase		:					RegNetConfig,						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int = 1,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    in_channels != out_channels or stride != 1
								lowercase__     				=    max(1,						out_channels // config.groups_width						)
								lowercase__     				=    (
								    TFRegNetShortCut(lowerCamelCase,						stride=lowerCamelCase,						name='''shortcut'''						)
								    if should_apply_shortcut
								    else tf.keras.layers.Activation('''linear''',						name='''shortcut'''						)
								)
								lowercase__     				=    [
								    TFRegNetConvLayer(lowerCamelCase,						kernel_size=1,						activation=config.hidden_act,						name='''layer.0'''						),
								    TFRegNetConvLayer(
								        lowerCamelCase,						stride=lowerCamelCase,						groups=lowerCamelCase,						activation=config.hidden_act,						name='''layer.1'''						),
								    TFRegNetSELayer(lowerCamelCase,						reduced_channels=int(round(in_channels / 4						)						),						name='''layer.2'''						),
								    TFRegNetConvLayer(lowerCamelCase,						kernel_size=1,						activation=lowerCamelCase,						name='''layer.3'''						),
								]
								lowercase__     				=    ACTaFN[config.hidden_act]




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    hidden_state
								for layer_module in self.layers:
												lowercase__     				=    layer_module(lowerCamelCase						)
								lowercase__     				=    self.shortcut(lowerCamelCase						)
								hidden_state += residual
								lowercase__     				=    self.activation(lowerCamelCase						)
								return hidden_state



class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					RegNetConfig,						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int = 2,						lowerCamelCase		:					int = 2,						**lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)

								lowercase__     				=    TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
								lowercase__     				=    [
								    # downsampling is done in the first layer with stride of 2
								    layer(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						stride=lowerCamelCase,						name='''layers.0'''						),
								    *[layer(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						name=F"""layers.{i+1}"""						) for i in range(depth - 1						)],
								]




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								for layer_module in self.layers:
												lowercase__     				=    layer_module(lowerCamelCase						)
								return hidden_state



class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				def __init__(  self		:					List[str],						lowerCamelCase		:					RegNetConfig,						**lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    []
								# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
								self.stages.append(
								    TFRegNetStage(
								        lowerCamelCase,						config.embedding_size,						config.hidden_sizes[0],						stride=2 if config.downsample_in_first_stage else 1,						depth=config.depths[0],						name='''stages.0''',						)						)
								lowercase__     				=    zip(config.hidden_sizes,						config.hidden_sizes[1:]						)
								for i, ((in_channels, out_channels), depth) in enumerate(zip(lowerCamelCase,						config.depths[1:]						)						):
												self.stages.append(TFRegNetStage(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						depth=lowerCamelCase,						name=F"""stages.{i+1}"""						)						)




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					tf.Tensor,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = True						):

								'''simple docstring'''
								lowercase__     				=    () if output_hidden_states else None

								for stage_module in self.stages:
												if output_hidden_states:
																lowercase__     				=    hidden_states + (hidden_state,)

												lowercase__     				=    stage_module(lowerCamelCase						)

								if output_hidden_states:
												lowercase__     				=    hidden_states + (hidden_state,)

								if not return_dict:
												return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

								return TFBaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase,						hidden_states=lowerCamelCase						)



@keras_serializable
class    _UpperCAmelCase      ( tf.keras.layers.Layer			):

				"""simple docstring"""




				lowercase__       							= RegNetConfig




				def __init__(  self		:					str,						lowerCamelCase		:					int,						**lowerCamelCase		:					Any						):

								'''simple docstring'''
								super().__init__(**lowerCamelCase						)
								lowercase__     				=    config
								lowercase__     				=    TFRegNetEmbeddings(lowerCamelCase,						name='''embedder'''						)
								lowercase__     				=    TFRegNetEncoder(lowerCamelCase,						name='''encoder'''						)
								lowercase__     				=    tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase,						name='''pooler'''						)




				@unpack_inputs
				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					tf.Tensor,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					bool = False,						):

								'''simple docstring'''
								lowercase__     				=    (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__     				=    return_dict if return_dict is not None else self.config.use_return_dict

								lowercase__     				=    self.embedder(lowerCamelCase,						training=lowerCamelCase						)

								lowercase__     				=    self.encoder(
								    lowerCamelCase,						output_hidden_states=lowerCamelCase,						return_dict=lowerCamelCase,						training=lowerCamelCase						)

								lowercase__     				=    encoder_outputs[0]
								lowercase__     				=    self.pooler(lowerCamelCase						)

								# Change to NCHW output format have uniformity in the modules
								lowercase__     				=    tf.transpose(lowerCamelCase,						perm=(0, 3, 1, 2)						)
								lowercase__     				=    tf.transpose(lowerCamelCase,						perm=(0, 3, 1, 2)						)

								# Change the other hidden state outputs to NCHW as well
								if output_hidden_states:
												lowercase__     				=    tuple([tf.transpose(lowerCamelCase,						perm=(0, 3, 1, 2)						) for h in encoder_outputs[1]]						)

								if not return_dict:
												return (last_hidden_state, pooled_output) + encoder_outputs[1:]

								return TFBaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=lowerCamelCase,						pooler_output=lowerCamelCase,						hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,						)



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= RegNetConfig
				lowercase__       							= """regnet"""
				lowercase__       							= """pixel_values"""




				@property
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224),						dtype=tf.floataa						)}



A__			:    List[str]         =				r'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

A__			:    Dict         =				r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""	,A__	,)
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[Any],						lowerCamelCase		:					RegNetConfig,						*lowerCamelCase		:					List[str],						**lowerCamelCase		:					Tuple						):

								'''simple docstring'''
								super().__init__(lowerCamelCase,						*lowerCamelCase,						**lowerCamelCase						)
								lowercase__     				=    TFRegNetMainLayer(lowerCamelCase,						name='''regnet'''						)




				@unpack_inputs
				@add_start_docstrings_to_model_forward(lowerCamelCase						)
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC,						output_type=lowerCamelCase,						config_class=_CONFIG_FOR_DOC,						modality='''vision''',						expected_output=_EXPECTED_OUTPUT_SHAPE,						)
				def        lowercase__     (  self		:					str,						lowerCamelCase		:					tf.Tensor,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					Optional[bool] = None,						lowerCamelCase		:					Tuple=False,						):

								'''simple docstring'''
								lowercase__     				=    (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__     				=    return_dict if return_dict is not None else self.config.use_return_dict

								lowercase__     				=    self.regnet(
								    pixel_values=lowerCamelCase,						output_hidden_states=lowerCamelCase,						return_dict=lowerCamelCase,						training=lowerCamelCase,						)
								if not return_dict:
												return (outputs[0],) + outputs[1:]

								return TFBaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=outputs.last_hidden_state,						pooler_output=outputs.pooler_output,						hidden_states=outputs.hidden_states,						)



@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """	,A__	,)
class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					RegNetConfig,						*lowerCamelCase		:					Dict,						**lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								super().__init__(lowerCamelCase,						*lowerCamelCase,						**lowerCamelCase						)
								lowercase__     				=    config.num_labels
								lowercase__     				=    TFRegNetMainLayer(lowerCamelCase,						name='''regnet'''						)
								# classification head
								lowercase__     				=    [
								    tf.keras.layers.Flatten(),
								    tf.keras.layers.Dense(config.num_labels,						name='''classifier.1'''						) if config.num_labels > 0 else tf.identity,
								]




				@unpack_inputs
				@add_start_docstrings_to_model_forward(lowerCamelCase						)
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT,						output_type=lowerCamelCase,						config_class=_CONFIG_FOR_DOC,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,						)
				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					tf.Tensor = None,						lowerCamelCase		:					tf.Tensor = None,						lowerCamelCase		:					bool = None,						lowerCamelCase		:					bool = None,						lowerCamelCase		:					Tuple=False,						):

								'''simple docstring'''
								lowercase__     				=    (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__     				=    return_dict if return_dict is not None else self.config.use_return_dict

								lowercase__     				=    self.regnet(
								    lowerCamelCase,						output_hidden_states=lowerCamelCase,						return_dict=lowerCamelCase,						training=lowerCamelCase						)

								lowercase__     				=    outputs.pooler_output if return_dict else outputs[1]

								lowercase__     				=    self.classifier[0](lowerCamelCase						)
								lowercase__     				=    self.classifier[1](lowerCamelCase						)

								lowercase__     				=    None if labels is None else self.hf_compute_loss(labels=lowerCamelCase,						logits=lowerCamelCase						)

								if not return_dict:
												lowercase__     				=    (logits,) + outputs[2:]
												return ((loss,) + output) if loss is not None else output

								return TFSequenceClassifierOutput(loss=lowerCamelCase,						logits=lowerCamelCase,						hidden_states=outputs.hidden_states						)












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

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

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

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (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
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.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
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




