


'''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, 1088, 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        _UpperCAmelCase						(		tf.keras.layers.Layer			):




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






       '''simple docstring'''






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





    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     self.convolution(self.padding(_UpperCamelCase			)			)
       lowercase_	:    List[Any]           =     self.normalization(_UpperCamelCase			)
       lowercase_	:    Optional[Any]           =     self.activation(_UpperCamelCase			)
       return hidden_state


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




    def __init__(			self  :       Optional[Any]  ,     a  :       RegNetConfig  ,     **a  :       Union[str, Any]			):






       '''simple docstring'''






       super().__init__(**_UpperCamelCase			)
       lowercase_	:    List[str]           =     config.num_channels
       lowercase_	:    Any           =     TFRegNetConvLayer(
           out_channels=config.embedding_size  ,     kernel_size=3  ,     stride=2  ,     activation=config.hidden_act  ,     name="embedder"  ,     )





    def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     shape_list(_UpperCamelCase			)[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_	:    Union[str, Any]           =     tf.transpose(_UpperCamelCase  ,     perm=(0, 2, 3, 1)			)
       lowercase_	:    Optional[int]           =     self.embedder(_UpperCamelCase			)
       return hidden_state


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




    def __init__(			self  :       Any  ,     a  :       int  ,     a  :       int = 2  ,     **a  :       List[Any]			):






       '''simple docstring'''






       super().__init__(**_UpperCamelCase			)
       lowercase_	:    Any           =     tf.keras.layers.ConvaD(
           filters=_UpperCamelCase  ,     kernel_size=1  ,     strides=_UpperCamelCase  ,     use_bias=_UpperCamelCase  ,     name="convolution"			)
       lowercase_	:    Any           =     tf.keras.layers.BatchNormalization(epsilon=1e-5  ,     momentum=0.9  ,     name="normalization"			)





    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       tf.Tensor  ,     a  :       bool = False			):






       '''simple docstring'''






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


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




    def __init__(			self  :       List[Any]  ,     a  :       int  ,     a  :       int  ,     **a  :       List[Any]			):






       '''simple docstring'''






       super().__init__(**_UpperCamelCase			)
       lowercase_	:    Optional[Any]           =     tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCamelCase  ,     name="pooler"			)
       lowercase_	:    Optional[Any]           =     [
           tf.keras.layers.ConvaD(filters=_UpperCamelCase  ,     kernel_size=1  ,     activation="relu"  ,     name="attention.0"			),
           tf.keras.layers.ConvaD(filters=_UpperCamelCase  ,     kernel_size=1  ,     activation="sigmoid"  ,     name="attention.2"			),
       ]





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    str           =     self.pooler(_UpperCamelCase			)
       for layer_module in self.attention:
          lowercase_	:    str           =     layer_module(_UpperCamelCase			)
       lowercase_	:    Dict           =     hidden_state * pooled
       return hidden_state


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




    def __init__(			self  :       int  ,     a  :       RegNetConfig  ,     a  :       int  ,     a  :       int  ,     a  :       int = 1  ,     **a  :       List[str]			):






       '''simple docstring'''






       super().__init__(**_UpperCamelCase			)
       lowercase_	:    List[Any]           =     in_channels != out_channels or stride != 1
       lowercase_	:    Optional[int]           =     max(1  ,     out_channels // config.groups_width			)
       lowercase_	:    Dict           =     (
           TFRegNetShortCut(_UpperCamelCase  ,     stride=_UpperCamelCase  ,     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_	:    Optional[Any]           =     [
           TFRegNetConvLayer(_UpperCamelCase  ,     kernel_size=1  ,     activation=config.hidden_act  ,     name="layer.0"			),
           TFRegNetConvLayer(
               _UpperCamelCase  ,     stride=_UpperCamelCase  ,     groups=_UpperCamelCase  ,     activation=config.hidden_act  ,     name="layer.1"			),
           TFRegNetConvLayer(_UpperCamelCase  ,     kernel_size=1  ,     activation=_UpperCamelCase  ,     name="layer.2"			),
       ]
       lowercase_	:    Dict           =     ACTaFN[config.hidden_act]





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     hidden_state
       for layer_module in self.layers:
          lowercase_	:    Optional[Any]           =     layer_module(_UpperCamelCase			)
       lowercase_	:    Optional[Any]           =     self.shortcut(_UpperCamelCase			)
       hidden_state += residual
       lowercase_	:    Optional[Any]           =     self.activation(_UpperCamelCase			)
       return hidden_state


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




    def __init__(			self  :       Dict  ,     a  :       RegNetConfig  ,     a  :       int  ,     a  :       int  ,     a  :       int = 1  ,     **a  :       str			):






       '''simple docstring'''






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





    def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     hidden_state
       for layer_module in self.layers:
          lowercase_	:    Tuple           =     layer_module(_UpperCamelCase			)
       lowercase_	:    Optional[int]           =     self.shortcut(_UpperCamelCase			)
       hidden_state += residual
       lowercase_	:    Any           =     self.activation(_UpperCamelCase			)
       return hidden_state


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




    def __init__(			self  :       Any  ,     a  :       RegNetConfig  ,     a  :       int  ,     a  :       int  ,     a  :       int = 2  ,     a  :       int = 2  ,     **a  :       int			):






       '''simple docstring'''






       super().__init__(**_UpperCamelCase			)

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





    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       str			):






       '''simple docstring'''






       for layer_module in self.layers:
          lowercase_	:    Union[str, Any]           =     layer_module(_UpperCamelCase			)
       return hidden_state


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




    def __init__(			self  :       int  ,     a  :       RegNetConfig  ,     **a  :       Any			):






       '''simple docstring'''






       super().__init__(**_UpperCamelCase			)
       lowercase_	:    Any           =     []
       # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
       self.stages.append(
           TFRegNetStage(
               _UpperCamelCase  ,     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_	:    Tuple           =     zip(config.hidden_sizes  ,     config.hidden_sizes[1:]			)
       for i, ((in_channels, out_channels), depth) in enumerate(zip(_UpperCamelCase  ,     config.depths[1:]			)			):
          self.stages.append(TFRegNetStage(_UpperCamelCase  ,     _UpperCamelCase  ,     _UpperCamelCase  ,     depth=_UpperCamelCase  ,     name=f"""stages.{i+1}"""			)			)





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






       '''simple docstring'''






       lowercase_	:    Any           =     () if output_hidden_states else None

       for stage_module in self.stages:
          if output_hidden_states:
             lowercase_	:    Union[str, Any]           =     hidden_states + (hidden_state,)

          lowercase_	:    Tuple           =     stage_module(_UpperCamelCase			)

       if output_hidden_states:
          lowercase_	:    List[Any]           =     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=_UpperCamelCase  ,     hidden_states=_UpperCamelCase			)


@keras_serializable
class        _UpperCAmelCase						(		tf.keras.layers.Layer			):
    __lowerCamelCase:   Union[str, Any]		       =    RegNetConfig




    def __init__(			self  :       Optional[Any]  ,     a  :       List[str]  ,     **a  :       Any			):






       '''simple docstring'''






       super().__init__(**_UpperCamelCase			)
       lowercase_	:    Optional[Any]           =     config
       lowercase_	:    Dict           =     TFRegNetEmbeddings(_UpperCamelCase  ,     name="embedder"			)
       lowercase_	:    Optional[int]           =     TFRegNetEncoder(_UpperCamelCase  ,     name="encoder"			)
       lowercase_	:    str           =     tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCamelCase  ,     name="pooler"			)





    @unpack_inputs
    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     a  :       tf.Tensor  ,     a  :       Optional[bool] = None  ,     a  :       Optional[bool] = None  ,     a  :       bool = False  ,     ):






       '''simple docstring'''






       lowercase_	:    Tuple           =     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase_	:    Optional[Any]           =     return_dict if return_dict is not None else self.config.use_return_dict

       lowercase_	:    Optional[int]           =     self.embedder(_UpperCamelCase  ,     training=_UpperCamelCase			)

       lowercase_	:    int           =     self.encoder(
           _UpperCamelCase  ,     output_hidden_states=_UpperCamelCase  ,     return_dict=_UpperCamelCase  ,     training=_UpperCamelCase			)

       lowercase_	:    Dict           =     encoder_outputs[0]
       lowercase_	:    Any           =     self.pooler(_UpperCamelCase			)

       # Change to NCHW output format have uniformity in the modules
       lowercase_	:    Optional[int]           =     tf.transpose(_UpperCamelCase  ,     perm=(0, 3, 1, 2)			)
       lowercase_	:    Any           =     tf.transpose(_UpperCamelCase  ,     perm=(0, 3, 1, 2)			)

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


class        _UpperCAmelCase						(		__lowerCAmelCase			):
    __lowerCamelCase:   Tuple		       =    RegNetConfig
    __lowerCamelCase:   int		       =    'regnet'
    __lowerCamelCase:   str		       =    'pixel_values'




    @property
    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)  ,     dtype=tf.floataa			)}


UpperCamelCase__ =			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"

UpperCamelCase__ =			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.'				,					__lowerCAmelCase				,					)
class        _UpperCAmelCase						(		__lowerCAmelCase			):




    def __init__(			self  :       List[Any]  ,     a  :       RegNetConfig  ,     *a  :       int  ,     **a  :       List[Any]			):






       '''simple docstring'''






       super().__init__(_UpperCamelCase  ,     *_UpperCamelCase  ,     **_UpperCamelCase			)
       lowercase_	:    int           =     TFRegNetMainLayer(_UpperCamelCase  ,     name="regnet"			)





    @unpack_inputs
    @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  :       List[Any]  ,     a  :       tf.Tensor  ,     a  :       Optional[bool] = None  ,     a  :       Optional[bool] = None  ,     a  :       Tuple=False  ,     ):






       '''simple docstring'''






       lowercase_	:    Any           =     (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase_	:    str           =     return_dict if return_dict is not None else self.config.use_return_dict

       lowercase_	:    List[str]           =     self.regnet(
           pixel_values=_UpperCamelCase  ,     output_hidden_states=_UpperCamelCase  ,     return_dict=_UpperCamelCase  ,     training=_UpperCamelCase  ,     )
       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(
    '\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				,					)
class        _UpperCAmelCase						(		__lowerCAmelCase				,					__lowerCAmelCase			):




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






       '''simple docstring'''






       super().__init__(_UpperCamelCase  ,     *_UpperCamelCase  ,     **_UpperCamelCase			)
       lowercase_	:    Any           =     config.num_labels
       lowercase_	:    Union[str, Any]           =     TFRegNetMainLayer(_UpperCamelCase  ,     name="regnet"			)
       # classification head
       lowercase_	:    Union[str, Any]           =     [
           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(_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  :       Optional[int]  ,     a  :       tf.Tensor = None  ,     a  :       tf.Tensor = None  ,     a  :       bool = None  ,     a  :       bool = None  ,     a  :       List[Any]=False  ,     ):






       '''simple docstring'''






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

       lowercase_	:    List[str]           =     self.regnet(
           _UpperCamelCase  ,     output_hidden_states=_UpperCamelCase  ,     return_dict=_UpperCamelCase  ,     training=_UpperCamelCase			)

       lowercase_	:    Dict           =     outputs.pooler_output if return_dict else outputs[1]

       lowercase_	:    Any           =     self.classifier[0](_UpperCamelCase			)
       lowercase_	:    List[Any]           =     self.classifier[1](_UpperCamelCase			)

       lowercase_	:    List[Any]           =     None if labels is None else self.hf_compute_loss(labels=_UpperCamelCase  ,     logits=_UpperCamelCase			)

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

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












'''simple docstring'''
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


UpperCamelCase__ =			'scheduler_config.json'


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   int		       =    1
    __lowerCamelCase:   List[Any]		       =    2
    __lowerCamelCase:   Optional[Any]		       =    3
    __lowerCamelCase:   int		       =    4
    __lowerCamelCase:   Optional[int]		       =    5


@dataclass
class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   jnp.ndarray


class        _UpperCAmelCase						:
    __lowerCamelCase:   List[str]		       =    SCHEDULER_CONFIG_NAME
    __lowerCamelCase:   Optional[int]		       =    ['dtype']
    __lowerCamelCase:   int		       =    []
    __lowerCamelCase:   Dict		       =    True




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Dict[str, Any] = None  ,     a  :       Optional[str] = None  ,     a  :       Union[str, Any]=False  ,     **a  :       Union[str, Any]  ,     ):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     cls.load_config(
           pretrained_model_name_or_path=a  ,     subfolder=a  ,     return_unused_kwargs=a  ,     **a  ,     )
       lowercase_    ,		lowercase_	:    Union[str, Any]           =     cls.from_config(a  ,     return_unused_kwargs=a  ,     **a			)

       if hasattr(a  ,     "create_state"			) and getattr(a  ,     "has_state"  ,     a			):
          lowercase_	:    Tuple           =     scheduler.create_state()

       if return_unused_kwargs:
          return scheduler, state, unused_kwargs

       return scheduler, state




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Union[str, os.PathLike]  ,     a  :       bool = False  ,     **a  :       int			):






       '''simple docstring'''






       self.save_config(save_directory=a  ,     push_to_hub=a  ,     **a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       return self._get_compatibles()





    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     list(set([cls.__name__] + cls._compatibles			)			)
       lowercase_	:    str           =     importlib.import_module(__name__.split("."			)[0]			)
       lowercase_	:    Optional[Any]           =     [
           getattr(a  ,     a			) for c in compatible_classes_str if hasattr(a  ,     a			)
       ]
       return compatible_classes




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   assert len(_UpperCamelCase							) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCamelCase							) - x.ndim)							)						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=0.999						,		_UpperCamelCase=jnp.floataa							):


   """simple docstring"""




   def alpha_bar(_UpperCamelCase							):
      return math.cos((time_step + 0.008) / 1.008 * math.pi / 2							) ** 2

   lowercase_	:    int           =     []
   for i in range(_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     i / num_diffusion_timesteps
      lowercase_	:    Dict           =     (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(_UpperCamelCase							) / alpha_bar(_UpperCamelCase							)						,		_UpperCamelCase							)							)
   return jnp.array(_UpperCamelCase						,		dtype=_UpperCamelCase							)


@flax.struct.dataclass
class        _UpperCAmelCase						:
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray
    __lowerCamelCase:   jnp.ndarray




    @classmethod
    def 				lowerCAmelCase__				(			cls  :       Tuple  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    Any           =     scheduler.config

       if config.trained_betas is not None:
          lowercase_	:    Union[str, Any]           =     jnp.asarray(config.trained_betas  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "linear":
          lowercase_	:    List[Any]           =     jnp.linspace(config.beta_start  ,     config.beta_end  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       elif config.beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase_	:    Tuple           =     (
              jnp.linspace(
                  config.beta_start**0.5  ,     config.beta_end**0.5  ,     config.num_train_timesteps  ,     dtype=scheduler.dtype			)
              ** 2
          )
       elif config.beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase_	:    Union[str, Any]           =     betas_for_alpha_bar(config.num_train_timesteps  ,     dtype=scheduler.dtype			)
       else:
          raise NotImplementedError(
              f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

       lowercase_	:    str           =     1.0 - betas

       lowercase_	:    Dict           =     jnp.cumprod(a  ,     axis=0			)

       return cls(
           alphas=a  ,     betas=a  ,     alphas_cumprod=a  ,     )




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     state.alphas_cumprod

   lowercase_	:    Optional[Any]           =     alphas_cumprod[timesteps] ** 0.5
   lowercase_	:    int           =     sqrt_alpha_prod.flatten()
   lowercase_	:    Union[str, Any]           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   lowercase_	:    Optional[int]           =     (1 - alphas_cumprod[timesteps]) ** 0.5
   lowercase_	:    Union[str, Any]           =     sqrt_one_minus_alpha_prod.flatten()
   lowercase_	:    Any           =     broadcast_to_shape_from_left(_UpperCamelCase						,		original_samples.shape							)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    str           =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_	:    int           =     get_sqrt_alpha_prod(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_	:    Any           =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity




