




"""simple docstring"""






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


UpperCamelCase__						=			logging.get_logger(__name__)

# General docstring
UpperCamelCase__						=			'''RegNetConfig'''

# Base docstring
UpperCamelCase__						=			'''facebook/regnet-y-040'''
UpperCamelCase__						=			[1, 1_0_8_8, 7, 7]

# Image classification docstring
UpperCamelCase__						=			'''facebook/regnet-y-040'''
UpperCamelCase__						=			'''tabby, tabby cat'''

UpperCamelCase__						=			[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]




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





						def __init__(							self      :	Optional[int]     ,a__      :	int     ,a__      :	int = 3     ,a__      :	int = 1     ,a__      :	int = 1     ,a__      :	Optional[str] = "relu"     ,**a__      :	str     ,)						->       List[str]:




											"""simple docstring"""







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







						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.convolution(self.padding(a__))
											_lowerCAmelCase:Tuple									=  self.normalization(a__)
											_lowerCAmelCase:List[str]									=  self.activation(a__)
											return hidden_state




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





						def __init__(							self      :	str     ,a__      :	RegNetConfig     ,**a__      :	Optional[int])						->       Optional[int]:




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:str									=  config.num_channels
											_lowerCAmelCase:Tuple									=  TFRegNetConvLayer(
											    out_channels=config.embedding_size     ,kernel_size=3     ,stride=2     ,activation=config.hidden_act     ,name='''embedder'''     ,)







						def       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	Optional[Any])						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  shape_list(a__)[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)
											_lowerCAmelCase:Any									=  tf.transpose(a__     ,perm=(0, 2, 3, 1))
											_lowerCAmelCase:Dict									=  self.embedder(a__)
											return hidden_state




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





						def __init__(							self      :	Optional[int]     ,a__      :	int     ,a__      :	int = 2     ,**a__      :	int)						->       Dict:




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:List[str]									=  tf.keras.layers.ConvaD(
											    filters=a__     ,kernel_size=1     ,strides=a__     ,use_bias=a__     ,name='''convolution''')
											_lowerCAmelCase:str									=  tf.keras.layers.BatchNormalization(epsilon=1E-5     ,momentum=0.9     ,name='''normalization''')







						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	tf.Tensor     ,a__      :	bool = False)						->       tf.Tensor:




											"""simple docstring"""







											return self.normalization(self.convolution(a__)     ,training=a__)




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





						def __init__(							self      :	str     ,a__      :	int     ,a__      :	int     ,**a__      :	Any)						->       List[Any]:




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:Optional[int]									=  tf.keras.layers.GlobalAveragePoolingaD(keepdims=a__     ,name='''pooler''')
											_lowerCAmelCase:Optional[int]									=  [
											    tf.keras.layers.ConvaD(filters=a__     ,kernel_size=1     ,activation='''relu'''     ,name='''attention.0'''),
											    tf.keras.layers.ConvaD(filters=a__     ,kernel_size=1     ,activation='''sigmoid'''     ,name='''attention.2'''),
											]







						def       __UpperCamelCase     (							self      :	str     ,a__      :	str)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  self.pooler(a__)
											for layer_module in self.attention:
																_lowerCAmelCase:Optional[int]									=  layer_module(a__)
											_lowerCAmelCase:Optional[int]									=  hidden_state * pooled
											return hidden_state




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





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




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:List[Any]									=  in_channels != out_channels or stride != 1
											_lowerCAmelCase:Union[str, Any]									=  max(1     ,out_channels // config.groups_width)
											_lowerCAmelCase:List[Any]									=  (
											    TFRegNetShortCut(a__     ,stride=a__     ,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.
											_lowerCAmelCase:int									=  [
											    TFRegNetConvLayer(a__     ,kernel_size=1     ,activation=config.hidden_act     ,name='''layer.0'''),
											    TFRegNetConvLayer(
											        a__     ,stride=a__     ,groups=a__     ,activation=config.hidden_act     ,name='''layer.1'''),
											    TFRegNetConvLayer(a__     ,kernel_size=1     ,activation=a__     ,name='''layer.2'''),
											]
											_lowerCAmelCase:str									=  ACTaFN[config.hidden_act]







						def       __UpperCamelCase     (							self      :	int     ,a__      :	str)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  hidden_state
											for layer_module in self.layers:
																_lowerCAmelCase:int									=  layer_module(a__)
											_lowerCAmelCase:Optional[Any]									=  self.shortcut(a__)
											hidden_state += residual
											_lowerCAmelCase:Dict									=  self.activation(a__)
											return hidden_state




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





						def __init__(							self      :	List[Any]     ,a__      :	RegNetConfig     ,a__      :	int     ,a__      :	int     ,a__      :	int = 1     ,**a__      :	Dict)						->       str:




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:Optional[Any]									=  in_channels != out_channels or stride != 1
											_lowerCAmelCase:str									=  max(1     ,out_channels // config.groups_width)
											_lowerCAmelCase:List[Any]									=  (
											    TFRegNetShortCut(a__     ,stride=a__     ,name='''shortcut''')
											    if should_apply_shortcut
											    else tf.keras.layers.Activation('''linear'''     ,name='''shortcut''')
											)
											_lowerCAmelCase:Union[str, Any]									=  [
											    TFRegNetConvLayer(a__     ,kernel_size=1     ,activation=config.hidden_act     ,name='''layer.0'''),
											    TFRegNetConvLayer(
											        a__     ,stride=a__     ,groups=a__     ,activation=config.hidden_act     ,name='''layer.1'''),
											    TFRegNetSELayer(a__     ,reduced_channels=int(round(in_channels / 4))     ,name='''layer.2'''),
											    TFRegNetConvLayer(a__     ,kernel_size=1     ,activation=a__     ,name='''layer.3'''),
											]
											_lowerCAmelCase:Optional[int]									=  ACTaFN[config.hidden_act]







						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	List[str])						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  hidden_state
											for layer_module in self.layers:
																_lowerCAmelCase:Union[str, Any]									=  layer_module(a__)
											_lowerCAmelCase:int									=  self.shortcut(a__)
											hidden_state += residual
											_lowerCAmelCase:Optional[Any]									=  self.activation(a__)
											return hidden_state




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





						def __init__(							self      :	int     ,a__      :	RegNetConfig     ,a__      :	int     ,a__      :	int     ,a__      :	int = 2     ,a__      :	int = 2     ,**a__      :	List[Any])						->       Optional[Any]:




											"""simple docstring"""







											super().__init__(**a__)

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







						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







											for layer_module in self.layers:
																_lowerCAmelCase:Optional[int]									=  layer_module(a__)
											return hidden_state




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





						def __init__(							self      :	int     ,a__      :	RegNetConfig     ,**a__      :	int)						->       Optional[int]:




											"""simple docstring"""







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







						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	tf.Tensor     ,a__      :	bool = False     ,a__      :	bool = True)						->       TFBaseModelOutputWithNoAttention:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  () if output_hidden_states else None

											for stage_module in self.stages:
																if output_hidden_states:
																					_lowerCAmelCase:Optional[int]									=  hidden_states + (hidden_state,)

																_lowerCAmelCase:Optional[Any]									=  stage_module(a__)

											if output_hidden_states:
																_lowerCAmelCase:Tuple									=  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=a__     ,hidden_states=a__)




@keras_serializable
class   a__			(       tf.keras.layers.Layer  ):
						snake_case__             = RegNetConfig





						def __init__(							self      :	Optional[Any]     ,a__      :	List[Any]     ,**a__      :	Optional[Any])						->       Tuple:




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:str									=  config
											_lowerCAmelCase:Union[str, Any]									=  TFRegNetEmbeddings(a__     ,name='''embedder''')
											_lowerCAmelCase:List[str]									=  TFRegNetEncoder(a__     ,name='''encoder''')
											_lowerCAmelCase:Optional[int]									=  tf.keras.layers.GlobalAveragePoolingaD(keepdims=a__     ,name='''pooler''')







						@unpack_inputs
						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	tf.Tensor     ,a__      :	Optional[bool] = None     ,a__      :	Optional[bool] = None     ,a__      :	bool = False     ,)						->       TFBaseModelOutputWithPoolingAndNoAttention:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											_lowerCAmelCase:int									=  return_dict if return_dict is not None else self.config.use_return_dict

											_lowerCAmelCase:str									=  self.embedder(a__     ,training=a__)

											_lowerCAmelCase:int									=  self.encoder(
											    a__     ,output_hidden_states=a__     ,return_dict=a__     ,training=a__)

											_lowerCAmelCase:Dict									=  encoder_outputs[0]
											_lowerCAmelCase:List[Any]									=  self.pooler(a__)

											# Change to NCHW output format have uniformity in the modules
											_lowerCAmelCase:List[str]									=  tf.transpose(a__     ,perm=(0, 3, 1, 2))
											_lowerCAmelCase:List[str]									=  tf.transpose(a__     ,perm=(0, 3, 1, 2))

											# Change the other hidden state outputs to NCHW as well
											if output_hidden_states:
																_lowerCAmelCase:Optional[int]									=  tuple([tf.transpose(a__     ,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=a__     ,pooler_output=a__     ,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states     ,)




class   a__			(       UpperCamelCase_  ):
						snake_case__             = RegNetConfig
						snake_case__             = '''regnet'''
						snake_case__             = '''pixel_values'''





						@property
						def       __UpperCamelCase     (							self      :	str)						->       Optional[int]:




											"""simple docstring"""







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



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

UpperCamelCase__						=			r'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''




@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.'''					,	UpperCamelCase_					,	)
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Any     ,a__      :	RegNetConfig     ,*a__      :	Optional[Any]     ,**a__      :	Dict)						->       str:




											"""simple docstring"""







											super().__init__(a__     ,*a__     ,**a__)
											_lowerCAmelCase:Union[str, Any]									=  TFRegNetMainLayer(a__     ,name='''regnet''')







						@unpack_inputs
						@add_start_docstrings_to_model_forward(a__)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC     ,output_type=a__     ,config_class=_CONFIG_FOR_DOC     ,modality='''vision'''     ,expected_output=_EXPECTED_OUTPUT_SHAPE     ,)
						def       __UpperCamelCase     (							self      :	int     ,a__      :	tf.Tensor     ,a__      :	Optional[bool] = None     ,a__      :	Optional[bool] = None     ,a__      :	List[Any]=False     ,)						->       Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											_lowerCAmelCase:Dict									=  return_dict if return_dict is not None else self.config.use_return_dict

											_lowerCAmelCase:List[Any]									=  self.regnet(
											    pixel_values=a__     ,output_hidden_states=a__     ,return_dict=a__     ,training=a__     ,)
											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.
    '''					,	UpperCamelCase_					,	)
class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):





						def __init__(							self      :	List[str]     ,a__      :	RegNetConfig     ,*a__      :	Optional[int]     ,**a__      :	int)						->       Any:




											"""simple docstring"""







											super().__init__(a__     ,*a__     ,**a__)
											_lowerCAmelCase:Dict									=  config.num_labels
											_lowerCAmelCase:Optional[int]									=  TFRegNetMainLayer(a__     ,name='''regnet''')
											# classification head
											_lowerCAmelCase:Optional[int]									=  [
											    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(a__)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,output_type=a__     ,config_class=_CONFIG_FOR_DOC     ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,)
						def       __UpperCamelCase     (							self      :	Any     ,a__      :	tf.Tensor = None     ,a__      :	tf.Tensor = None     ,a__      :	bool = None     ,a__      :	bool = None     ,a__      :	Optional[int]=False     ,)						->       Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											_lowerCAmelCase:Dict									=  return_dict if return_dict is not None else self.config.use_return_dict

											_lowerCAmelCase:Optional[int]									=  self.regnet(
											    a__     ,output_hidden_states=a__     ,return_dict=a__     ,training=a__)

											_lowerCAmelCase:Optional[Any]									=  outputs.pooler_output if return_dict else outputs[1]

											_lowerCAmelCase:Optional[Any]									=  self.classifier[0](a__)
											_lowerCAmelCase:Optional[int]									=  self.classifier[1](a__)

											_lowerCAmelCase:Tuple									=  None if labels is None else self.hf_compute_loss(labels=a__     ,logits=a__)

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

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












"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = None





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int=0.9_99      ,       snake_case			:					Union[str, Any]="cosine"      ,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case			:					Dict    ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(snake_case			:					Optional[Any]    ):
															return math.exp(t * -12.0    )

					else:
										raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

					_lowerCAmelCase:List[str]									=  []
					for i in range(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  i / num_diffusion_timesteps
										_lowerCAmelCase:Dict									=  (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case    ) / alpha_bar_fn(snake_case    )      ,       snake_case    )    )
					return torch.tensor(snake_case      ,       dtype=torch.floataa    )





class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Optional[Any]     ,a__      :	int = 1000     ,a__      :	float = 0.0001     ,a__      :	float = 0.02     ,a__      :	str = "linear"     ,a__      :	Optional[Union[np.ndarray, List[float]]] = None     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	int = 0     ,a__      :	str = "epsilon"     ,a__      :	float = 1.0     ,**a__      :	Any     ,)						->       str:




											"""simple docstring"""







											if kwargs.get('''set_alpha_to_one'''     ,a__) is not None:
																_lowerCAmelCase:Tuple									=  (
																    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																)
																deprecate('''set_alpha_to_one'''     ,'''1.0.0'''     ,a__     ,standard_warn=a__)
																_lowerCAmelCase:Optional[Any]									=  kwargs['''set_alpha_to_one''']
											if trained_betas is not None:
																_lowerCAmelCase:Any									=  torch.tensor(a__     ,dtype=torch.floataa)
											elif beta_schedule == "linear":
																_lowerCAmelCase:str									=  torch.linspace(a__     ,a__     ,a__     ,dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowerCAmelCase:int									=  (
																    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,a__     ,dtype=torch.floataa) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowerCAmelCase:Optional[int]									=  betas_for_alpha_bar(a__)
											else:
																raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											_lowerCAmelCase:Any									=  1.0 - self.betas
											_lowerCAmelCase:Optional[Any]									=  torch.cumprod(self.alphas     ,dim=0)

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											_lowerCAmelCase:Optional[Any]									=  torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowerCAmelCase:Any									=  1.0

											# setable values
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:List[Any]									=  torch.from_numpy(np.arange(0     ,a__).copy().astype(np.intaa))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	torch.FloatTensor     ,a__      :	Optional[int] = None)						->       torch.FloatTensor:




											"""simple docstring"""







											return sample





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	int     ,a__      :	Union[str, torch.device] = None)						->       List[Any]:




											"""simple docstring"""







											if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
																    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
																    F' maximal {self.config.num_train_timesteps} timesteps.')

											_lowerCAmelCase:Union[str, Any]									=  num_inference_steps
											_lowerCAmelCase:Union[str, Any]									=  self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											_lowerCAmelCase:str									=  (np.arange(0     ,a__) * step_ratio).round().copy().astype(np.intaa)
											_lowerCAmelCase:str									=  torch.from_numpy(a__).to(a__)
											self.timesteps += self.config.steps_offset





						def       __UpperCamelCase     (							self      :	int     ,a__      :	torch.FloatTensor     ,a__      :	int     ,a__      :	torch.FloatTensor     ,a__      :	float = 0.0     ,a__      :	bool = False     ,a__      :	Optional[torch.FloatTensor] = None     ,a__      :	bool = True     ,)						->       Union[DDIMSchedulerOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											_lowerCAmelCase:Optional[Any]									=  self.alphas_cumprod[timestep]
											_lowerCAmelCase:List[Any]									=  (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowerCAmelCase:str									=  1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																_lowerCAmelCase:int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_lowerCAmelCase:str									=  model_output
											elif self.config.prediction_type == "sample":
																_lowerCAmelCase:List[str]									=  model_output
																_lowerCAmelCase:Optional[int]									=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																_lowerCAmelCase:str									=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_lowerCAmelCase:Dict									=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
																    ''' `v_prediction`''')

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																_lowerCAmelCase:List[Any]									=  pred_original_sample.clamp(
																    -self.config.clip_sample_range     ,self.config.clip_sample_range)

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:str									=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:Optional[Any]									=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=a__     ,pred_original_sample=a__)







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




											"""simple docstring"""







											return self.config.num_train_timesteps






