
"""simple docstring"""




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


A__      :	Union[str, Any]          =    logging.get_logger(__name__)

# General docstring
A__      :	Tuple          =    """RegNetConfig"""

# Base docstring
A__      :	List[Any]          =    """facebook/regnet-y-040"""
A__      :	Union[str, Any]          =    [1, 1_088, 7, 7]

# Image classification docstring
A__      :	List[Any]          =    """facebook/regnet-y-040"""
A__      :	List[str]          =    """tabby, tabby cat"""

A__      :	List[Any]          =    [
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 3 ,			__UpperCamelCase = 1 ,			__UpperCamelCase = 1 ,			__UpperCamelCase = "relu" ,			)->					Any:
          super().__init__()
          UpperCAmelCase__							: Union[str, Any] 							=						nn.Convad(
              __UpperCamelCase ,			__UpperCamelCase ,			kernel_size=__UpperCamelCase ,			stride=__UpperCamelCase ,			padding=kernel_size // 2 ,			groups=__UpperCamelCase ,			bias=__UpperCamelCase ,			)
          UpperCAmelCase__							: Optional[Any] 							=						nn.BatchNormad(__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						ACTaFN[activation] if activation is not None else nn.Identity()



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: List[str] 							=						self.convolution(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[int] 							=						self.normalization(__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						self.activation(__UpperCamelCase  )
          return hidden_state







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase  )->					str:
          super().__init__()
          UpperCAmelCase__							: Dict 							=						RegNetConvLayer(
              config.num_channels ,			config.embedding_size ,			kernel_size=3 ,			stride=2 ,			activation=config.hidden_act  )
          UpperCAmelCase__							: Optional[int] 							=						config.num_channels



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: Dict 							=						pixel_values.shape[1]
          if 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."  )
          UpperCAmelCase__							: Optional[Any] 							=						self.embedder(__UpperCamelCase  )
          return hidden_state







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 2  )->					Union[str, Any]:
          super().__init__()
          UpperCAmelCase__							: Optional[Any] 							=						nn.Convad(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1 ,			stride=__UpperCamelCase ,			bias=__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						nn.BatchNormad(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tensor:
          UpperCAmelCase__							: List[str] 							=						self.convolution(__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						self.normalization(__UpperCamelCase  )
          return hidden_state







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
          super().__init__()

          UpperCAmelCase__							: Union[str, Any] 							=						nn.AdaptiveAvgPoolad((1, 1)  )
          UpperCAmelCase__							: Union[str, Any] 							=						nn.Sequential(
              nn.Convad(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1  ) ,			nn.ReLU() ,			nn.Convad(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1  ) ,			nn.Sigmoid() ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          # b c h w -> b c 1 1
          UpperCAmelCase__							: Union[str, Any] 							=						self.pooler(__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						self.attention(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						hidden_state * attention
          return hidden_state







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 1  )->					List[str]:
          super().__init__()
          UpperCAmelCase__							: Tuple 							=						in_channels != out_channels or stride != 1
          UpperCAmelCase__							: List[str] 							=						max(1 ,			out_channels // config.groups_width  )
          UpperCAmelCase__							: str 							=						(
              RegNetShortCut(__UpperCamelCase ,			__UpperCamelCase ,			stride=__UpperCamelCase  ) if should_apply_shortcut else nn.Identity()
          )
          UpperCAmelCase__							: List[Any] 							=						nn.Sequential(
              RegNetConvLayer(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1 ,			activation=config.hidden_act  ) ,			RegNetConvLayer(__UpperCamelCase ,			__UpperCamelCase ,			stride=__UpperCamelCase ,			groups=__UpperCamelCase ,			activation=config.hidden_act  ) ,			RegNetConvLayer(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1 ,			activation=__UpperCamelCase  ) ,			)
          UpperCAmelCase__							: str 							=						ACTaFN[config.hidden_act]



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Dict:
          UpperCAmelCase__							: Optional[Any] 							=						hidden_state
          UpperCAmelCase__							: Dict 							=						self.layer(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						self.shortcut(__UpperCamelCase  )
          hidden_state += residual
          UpperCAmelCase__							: Tuple 							=						self.activation(__UpperCamelCase  )
          return hidden_state







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 1  )->					str:
          super().__init__()
          UpperCAmelCase__							: Any 							=						in_channels != out_channels or stride != 1
          UpperCAmelCase__							: str 							=						max(1 ,			out_channels // config.groups_width  )
          UpperCAmelCase__							: Union[str, Any] 							=						(
              RegNetShortCut(__UpperCamelCase ,			__UpperCamelCase ,			stride=__UpperCamelCase  ) if should_apply_shortcut else nn.Identity()
          )
          UpperCAmelCase__							: List[str] 							=						nn.Sequential(
              RegNetConvLayer(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1 ,			activation=config.hidden_act  ) ,			RegNetConvLayer(__UpperCamelCase ,			__UpperCamelCase ,			stride=__UpperCamelCase ,			groups=__UpperCamelCase ,			activation=config.hidden_act  ) ,			RegNetSELayer(__UpperCamelCase ,			reduced_channels=int(round(in_channels / 4  )  )  ) ,			RegNetConvLayer(__UpperCamelCase ,			__UpperCamelCase ,			kernel_size=1 ,			activation=__UpperCamelCase  ) ,			)
          UpperCAmelCase__							: List[str] 							=						ACTaFN[config.hidden_act]



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          UpperCAmelCase__							: Any 							=						hidden_state
          UpperCAmelCase__							: Optional[Any] 							=						self.layer(__UpperCamelCase  )
          UpperCAmelCase__							: Union[str, Any] 							=						self.shortcut(__UpperCamelCase  )
          hidden_state += residual
          UpperCAmelCase__							: str 							=						self.activation(__UpperCamelCase  )
          return hidden_state







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = 2 ,			__UpperCamelCase = 2 ,			)->					str:
          super().__init__()

          UpperCAmelCase__							: Optional[int] 							=						RegNetXLayer if config.layer_type == "x" else RegNetYLayer

          UpperCAmelCase__							: Any 							=						nn.Sequential(
              # downsampling is done in the first layer with stride of 2
              layer(
                  __UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			stride=__UpperCamelCase ,			) ,			*[layer(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  ) for _ in range(depth - 1  )] ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: List[Any] 							=						self.layers(__UpperCamelCase  )
          return hidden_state







class   _lowercase						(       nn.Module				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase  )->					Tuple:
          super().__init__()
          UpperCAmelCase__							: int 							=						nn.ModuleList([]  )
          # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
          self.stages.append(
              RegNetStage(
                  __UpperCamelCase ,			config.embedding_size ,			config.hidden_sizes[0] ,			stride=2 if config.downsample_in_first_stage else 1 ,			depth=config.depths[0] ,			)  )
          UpperCAmelCase__							: int 							=						zip(config.hidden_sizes ,			config.hidden_sizes[1:]  )
          for (in_channels, out_channels), depth in zip(__UpperCamelCase ,			config.depths[1:]  ):
               self.stages.append(RegNetStage(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			depth=__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = False ,			__UpperCamelCase = True  )->					BaseModelOutputWithNoAttention:
          UpperCAmelCase__							: Dict 							=						() if output_hidden_states else None

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

               UpperCAmelCase__							: Optional[Any] 							=						stage_module(__UpperCamelCase  )

          if output_hidden_states:
               UpperCAmelCase__							: 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 BaseModelOutputWithNoAttention(last_hidden_state=__UpperCamelCase ,			hidden_states=__UpperCamelCase  )







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							RegNetConfig
     _A								=							'regnet'
     _A								=							'pixel_values'
     _A								=							True



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          if isinstance(__UpperCamelCase ,			nn.Convad  ):
               nn.init.kaiming_normal_(module.weight ,			mode="fan_out" ,			nonlinearity="relu"  )
          elif isinstance(__UpperCamelCase ,			(nn.BatchNormad, nn.GroupNorm)  ):
               nn.init.constant_(module.weight ,			1  )
               nn.init.constant_(module.bias ,			0  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False  )->					str:
          if isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
               UpperCAmelCase__							: str 							=						value





A__      :	Union[str, Any]          =    R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

A__      :	List[str]          =    R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""







@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'	,   lowerCAmelCase_	,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase  )->					Tuple:
          super().__init__(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						config
          UpperCAmelCase__							: Dict 							=						RegNetEmbeddings(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						RegNetEncoder(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						nn.AdaptiveAvgPoolad((1, 1)  )
          # Initialize weights and apply final processing
          self.post_init()



     @add_start_docstrings_to_model_forward(__UpperCamelCase  )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC ,			output_type=__UpperCamelCase ,			config_class=_CONFIG_FOR_DOC ,			modality="vision" ,			expected_output=_EXPECTED_OUTPUT_SHAPE ,			)
     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None  )->					BaseModelOutputWithPoolingAndNoAttention:
          UpperCAmelCase__							: Dict 							=						(
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          UpperCAmelCase__							: Any 							=						return_dict if return_dict is not None else self.config.use_return_dict

          UpperCAmelCase__							: Optional[Any] 							=						self.embedder(__UpperCamelCase  )

          UpperCAmelCase__							: Any 							=						self.encoder(
              __UpperCamelCase ,			output_hidden_states=__UpperCamelCase ,			return_dict=__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						encoder_outputs[0]

          UpperCAmelCase__							: Tuple 							=						self.pooler(__UpperCamelCase  )

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

          return BaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=__UpperCamelCase ,			pooler_output=__UpperCamelCase ,			hidden_states=encoder_outputs.hidden_states ,			)







@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '	,   lowerCAmelCase_	,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase  )->					List[Any]:
          super().__init__(__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						config.num_labels
          UpperCAmelCase__							: List[Any] 							=						RegNetModel(__UpperCamelCase  )
          # classification head
          UpperCAmelCase__							: Any 							=						nn.Sequential(
              nn.Flatten() ,			nn.Linear(config.hidden_sizes[-1] ,			config.num_labels  ) if config.num_labels > 0 else nn.Identity() ,			)
          # initialize weights and apply final processing
          self.post_init()



     @add_start_docstrings_to_model_forward(__UpperCamelCase  )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT ,			output_type=__UpperCamelCase ,			config_class=_CONFIG_FOR_DOC ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,			)
     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					ImageClassifierOutputWithNoAttention:
          UpperCAmelCase__							: Union[str, Any] 							=						return_dict if return_dict is not None else self.config.use_return_dict

          UpperCAmelCase__							: Any 							=						self.regnet(__UpperCamelCase ,			output_hidden_states=__UpperCamelCase ,			return_dict=__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						outputs.pooler_output if return_dict else outputs[1]

          UpperCAmelCase__							: Optional[Any] 							=						self.classifier(__UpperCamelCase  )

          UpperCAmelCase__							: Union[str, Any] 							=						None

          if labels is not None:
               if self.config.problem_type is None:
                    if self.num_labels == 1:
                         UpperCAmelCase__							: Optional[int] 							=						"regression"
                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                         UpperCAmelCase__							: str 							=						"single_label_classification"
                    else:
                         UpperCAmelCase__							: Tuple 							=						"multi_label_classification"
               if self.config.problem_type == "regression":
                    UpperCAmelCase__							: Tuple 							=						MSELoss()
                    if self.num_labels == 1:
                         UpperCAmelCase__							: Dict 							=						loss_fct(logits.squeeze() ,			labels.squeeze()  )
                    else:
                         UpperCAmelCase__							: int 							=						loss_fct(__UpperCamelCase ,			__UpperCamelCase  )
               elif self.config.problem_type == "single_label_classification":
                    UpperCAmelCase__							: Optional[int] 							=						CrossEntropyLoss()
                    UpperCAmelCase__							: List[str] 							=						loss_fct(logits.view(-1 ,			self.num_labels  ) ,			labels.view(-1  )  )
               elif self.config.problem_type == "multi_label_classification":
                    UpperCAmelCase__							: Optional[Any] 							=						BCEWithLogitsLoss()
                    UpperCAmelCase__							: Dict 							=						loss_fct(__UpperCamelCase ,			__UpperCamelCase  )

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

          return ImageClassifierOutputWithNoAttention(loss=__UpperCamelCase ,			logits=__UpperCamelCase ,			hidden_states=outputs.hidden_states  )





"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class   _lowercase						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

          # standard deviation of the initial noise distribution
          UpperCAmelCase__							: Tuple 							=						jnp.array(1.0 ,			dtype=self.dtype  )

          UpperCAmelCase__							: Tuple 							=						jnp.arange(0 ,			self.config.num_train_timesteps  ).round()[::-1]

          return DDPMSchedulerState.create(
              common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

          return state.replace(
              num_inference_steps=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: int 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small":
               UpperCAmelCase__							: Dict 							=						jnp.clip(__UpperCamelCase ,			a_min=1E-20  )
          # for rl-diffuser https://arxiv.org/abs/2205.09991
          elif variance_type == "fixed_small_log":
               UpperCAmelCase__							: Tuple 							=						jnp.log(jnp.clip(__UpperCamelCase ,			a_min=1E-20  )  )
          elif variance_type == "fixed_large":
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
          elif variance_type == "fixed_large_log":
               # Glide max_log
               UpperCAmelCase__							: Optional[int] 							=						jnp.log(state.common.betas[t]  )
          elif variance_type == "learned":
               return predicted_variance
          elif variance_type == "learned_range":
               UpperCAmelCase__							: List[str] 							=						variance
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
               UpperCAmelCase__							: Optional[int] 							=						(predicted_variance + 1) / 2
               UpperCAmelCase__							: Any 							=						frac * max_log + (1 - frac) * min_log

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

          if key is None:
               UpperCAmelCase__							: int 							=						jax.random.PRNGKey(0  )

          if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
               UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						jnp.split(__UpperCamelCase ,			sample.shape[1] ,			axis=1  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						None

          # 1. compute alphas, betas
          UpperCAmelCase__							: Union[str, Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: Tuple 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )
          UpperCAmelCase__							: Union[str, Any] 							=						1 - alpha_prod_t
          UpperCAmelCase__							: Tuple 							=						1 - alpha_prod_t_prev

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          if self.config.prediction_type == "epsilon":
               UpperCAmelCase__							: List[Any] 							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
               UpperCAmelCase__							: Any 							=						model_output
          elif self.config.prediction_type == "v_prediction":
               UpperCAmelCase__							: Union[str, Any] 							=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
                   " for the FlaxDDPMScheduler."  )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
               UpperCAmelCase__							: List[Any] 							=						jnp.clip(__UpperCamelCase ,			-1 ,			1  )

          # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          UpperCAmelCase__							: List[str] 							=						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
          UpperCAmelCase__							: List[Any] 							=						state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

          # 5. Compute predicted previous sample µ_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          UpperCAmelCase__							: Tuple 							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          def random_variance():
               UpperCAmelCase__							: Any 							=						jax.random.split(__UpperCamelCase ,			num=1  )
               UpperCAmelCase__							: int 							=						jax.random.normal(__UpperCamelCase ,			shape=model_output.shape ,			dtype=self.dtype  )
               return (self._get_variance(__UpperCamelCase ,			__UpperCamelCase ,			predicted_variance=__UpperCamelCase  ) ** 0.5) * noise

          UpperCAmelCase__							: Dict 							=						jnp.where(t > 0 ,			random_variance() ,			jnp.zeros(model_output.shape ,			dtype=self.dtype  )  )

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def __len__(      self  )->					Tuple:
          return self.config.num_train_timesteps



